As the popularity of ClickHouse increased over the years, more and more people know about the core features of ClickHouse like its incredible performance, its high compression ratio or the huge capabilities of reading and writing nearly all dataformats there are.

But ClickHouse also has a lot of hidden gems, which can help in your day to day work, a lot of the people don’t know about. Even so, most of them are well documented in the official Documentation, if you don’t know you are looking for them, you will not find them.

In this blog post, I’ll highlight some of my favorite small features that more people should know about.

Select modifiers

In column based DBMS like ClickHouse, queries like SELECT * FROM table should generally be avoided. At least that’s true when it comes to regular queries issued by your application. The typical day to day work of a DBA or database developer however often includes these types of queries, as otherwise it would take a huge amount of time to type down all X columns of a table manually.

But what if you want to run a lot of operations on some columns? Imagine you have the following table:

Unset

CREATE TABLE customer (
customerId UInt64,
custom_num_1 UInt64,
custom_num_2 UInt64,
...
custom_num_50 UInt64,
custom_string_1 String,
custom_string_2 String,
...
custom_string_50 String
);

When optimizing your table, you might be interested in the average length of the String columns in your table, as well as the maximum value of your number columns. If you want to handwrite a query to collect this information, you would have to write the correct function for 100 columns this way.

In some databases (like MySQL but also ClickHouse) you can utilize the INFORMATION_SCHEMA.COLUMNS table to build your query. This can be convenient for a lot of DBAs as they might already be used to this, but ClickHouse provides an even faster way to achieve your goal: Select Modifiers

Utilizing a combination of modifiers, our task comes down to a simple query:

Unset

SELECT

       COLUMNS('.*num.*') APPLY max,

       COLUMNS('.*string.*') APPLY length APPLY max

FROM customer

We are utilizing the COLUMNS modifier to apply a regex to the column names to only get columns with names num or string in it, and on all those columns we apply the function max if it has been in the set of number columns, or we first apply the function length and afterwards the function max. This gives us the wanted results, and takes you way less time than building a query via the information schema, or writing down 100 columns manually.

Manual test data in various formats

Often when I want to help customers or users of ClickHouse in the open slack channel, there are cases where you need to provide examples in a test table. Sometimes writing the table definition for such a test table and filling it with test data can even be more code than what you want to show in the first place.

As you could see in the previous section, the table I used for explaining select modifiers is longer than the actual code I want to present (and it has been abbreviated already).

But there is a possibility in ClickHouse to directly work on Data, as if it would be in a table, which is the format table-function

With this function you can specify which format you want to use (like JSONEachRow) and then directly enter the data, instead of inserting it into a table first:

Unset

SELECT *
FROM FORMAT(JSONEachRow, '{"customerId":1,"type":1,"custom_num1":4711}\n{"customerId":2, "type":2,"custom_ips":["127.0.0.1","127.0.0.2"]}')

+-customerId-+-type-+-custom_num1-+-custom_ips----------------+
| 1 | 1 | 4711 | [] |
| 2 | 2 | NULL | ['127.0.0.1','127.0.0.2'] |
+------------+------+-------------+---------------------------+

You can see it generates a result set with two rows, and even complex types like Arrays are doable. And you can use a large variety of data formats to use.

Generate series with numbers table function

Some DBMS have possibilities to generate number sequences by using table functions. ClickHouse is no exception in this regard, providing the numbers() table function for these regards. But did you know that you can easily generate date sequences or time sequences with this table function as well?

ClickHouse allows simple arithmetic functions on Date and DateTime data types, allowing you to easily generate sequences of dates or timestamps:

Unset

SELECT
number,
now() - number AS previousTimes,
toDate(now()) + number AS nextDays
FROM numbers(10)
+-number-+-------previousTimes-+---nextDays-+
|  0 | 2024-04-25 13:04:30 | 2024-04-25 |
|  1 | 2024-04-25 13:04:29 | 2024-04-26 |
|  2 | 2024-04-25 13:04:28 | 2024-04-27 |
|  3 | 2024-04-25 13:04:27 | 2024-04-28 |
|  4 | 2024-04-25 13:04:26 | 2024-04-29 |
|  5 | 2024-04-25 13:04:25 | 2024-04-30 |
|  6 | 2024-04-25 13:04:24 | 2024-05-01 |
|  7 | 2024-04-25 13:04:23 | 2024-05-02 |
|  8 | 2024-04-25 13:04:22 | 2024-05-03 |
|  9 | 2024-04-25 13:04:21 | 2024-05-04 |
+--------+---------------------+------------+

By applying multiplication you can also introduce steps etc.

As the final Datatype of the columns will still be Date or DateTime you can be sure, that only valid dates are generated

Data formatting in custom columns

Sometimes it is needed to partially format your data on output. For example if you want to insert data into a streaming service like Kafka. Typically you have some columns you would need as direct columns, but also combine the data of other columns into a payload column, in a given format (Typically JSON).

Of course, you can do a lot of these things in other DBMS as well, by using string concatenation, and build your JSON manually, or use specific JSON functions like toJSONString or manually create JSON Objects etc.

But ClickHouse also has you covered here, by giving you the function formatRowNoNewline(). This function allows you to format an arbitrary amount of columns into all possible output formats ClickHouse has to offer.

And of course you can also use Select Modifiers to specify which columns to format:

Unset

SELECT
customerId,
formatRowNoNewline('JSONEachRow', COLUMNS('.*num.*')) AS payload
FROM customer
LIMIT 10

+-customerId-+-payload------------------------------------------------------------------------------+
|      20 | {"custom_num_1":"4503644724578621668","custom_num_2":"156","custom_num_50":"32624"}  |
|    111 | {"custom_num_1":"9395348731023764754","custom_num_2":"4","custom_num_50":"8919"} |
|    187 | {"custom_num_1":"4410745110154105282","custom_num_2":"67","custom_num_50":"19015"}   |
|    231 | {"custom_num_1":"8206799308850873985","custom_num_2":"151","custom_num_50":"43260"}  |
|    262 | {"custom_num_1":"14904510309594397590","custom_num_2":"83","custom_num_50":"2802"}   |
|    375 | {"custom_num_1":"14468162691130275987","custom_num_2":"13","custom_num_50":"6910"}   |
|    388 | {"custom_num_1":"15191470301382236130","custom_num_2":"110","custom_num_50":"39490"} |
|    434 | {"custom_num_1":"11648353314185268442","custom_num_2":"211","custom_num_50":"52725"} |
|    439 | {"custom_num_1":"8430391284904487000","custom_num_2":"31","custom_num_50":"43376"}   |
|    468 | {"custom_num_1":"11265143749859112447","custom_num_2":"41","custom_num_50":"58748"}  |
+------------+--------------------------------------------------------------------------------------+

Querying the whole cluster

Sometimes querying a single node is not enough. Imagine the case, that you are looking for queries which run longer than a specific threshold. You can get the information within ClickHouse’s system.query_log table, but you would have to check all hosts separately.

But again ClickHouse has you covered. There is a table function clusterAllReplicas which allows you to execute a query on all nodes of a cluster and giving you the combined result, as it would have been a local table:

Unset

SELECT
user,
substring(query, 1, 15) AS query_part,
query_duration_ms
FROM clusterAllReplicas('mytestcluster', system, query_log)
WHERE query_duration_ms > 50
LIMIT 3

+-user----+-query_part------+-query_duration_ms-+
| default | INSERT INTO col |                52 |
| default | INSERT INTO col |                55 |
| default | INSERT INTO col |                51 |
+---------+-----------------+-------------------+

Bonus: Working with AggregateStates

Working with Materialized Views and possibilities of AggregateFunctions could fill multiple blog posts on its own (for example this one about performance impact of Materialized Views). Therefore I only want to briefly mention some functionality not everyone knows about, but could come in handy.

Let’s assume we have the following 2 tables, which just count unique customers per hour or day:

Unset

CREATE TABLE customers_hourly (
eventDate Date,
eventHour UInt8,
uniqueCustomers AggregateFunction(uniq, UInt64)
) ENGINE=AggregatingMergeTree
ORDER BY (eventDate, eventHour);

CREATE TABLE customers_daily (
eventDate Date,
uniqueCustomers AggregateFunction(uniq, UInt64)
) ENGINE=AggregatingMergeTree
ORDER BY (eventDate);

Initialize aggregation

Filling those tables is quite easy with Materialized Views. But what if you manually want to insert a row. For example for testing purposes you want to insert the test customerId 4711 on 3 different hours of the same day.

As uniqueCustomers is an AggregationFunction you cannot directly insert the customerId into that column so something like this doesn’t work:

Unset

INSERT INTO customers_hourly
SELECT eventDate,eventHour,customerId
FROM FORMAT(JSONEachRow,
$${"customerId":4711,"eventDate":"2024-04-26","eventHour":1}
{"customerId":4711,"eventDate":"2024-04-26","eventHour":2}
{"customerId":4711,"eventDate":"2024-04-26","eventHour":3}$$)

Of course it’s possible to generate an aggregation state by using window functions:

Unset

INSERT INTO customers_hourly
SELECT eventDate,eventHour,
uniqState(toUInt64(assumeNotNull(customerId))) OVER ()
FROM FORMAT(JSONEachRow,
$${"customerId":4711,"eventDate":"2024-04-26","eventHour":1}
{"customerId":4711,"eventDate":"2024-04-26","eventHour":2}
{"customerId":4711,"eventDate":"2024-04-26","eventHour":3}$$)

But if you have multiple arguments to the function that could be hard to read, so you can also just use the initializeAggregation function to directly insert into the table:

Unset

INSERT INTO customers_hourly
SELECT eventDate,eventHour,
initializeAggregation('uniqState',toUInt64(assumeNotNull(customerId)))
FROM FORMAT(JSONEachRow,
$${"customerId":4711,"eventDate":"2024-04-26","eventHour":1}
{"customerId":4711,"eventDate":"2024-04-26","eventHour":2}
{"customerId":4711,"eventDate":"2024-04-26","eventHour":3}$$)

Finalize aggregation

Now that we know how to directly write into an aggregation function, how can we read from it? Of course we can use uniqMerge to get the final result, but this is an Aggregation function, therefore we need to do GROUP BY etc to get the final result. And if we want to see the intermediate results we added into the table, we would also need to use a window again, to prevent the collapsing of the rows. Or we just use finalizeAggregation to make it easier:

Unset

SELECT
       eventDate,
       eventHour,
       finalizeAggregation(uniqueCustomers)
FROM customers_hourly
┌──eventDate─┬─eventHour─┬─finalizeAggregation(uniqueCustomers)─┐
1. │ 2024-04-26 │         1 │                                    1 │
2. │ 2024-04-26 │         2 │                                    1 │
3. │ 2024-04-26 │         3 │                                    1 │
└────────────┴───────────┴──────────────────────────────────────┘
┌──eventDate─┬─eventHour─┬─finalizeAggregation(uniqueCustomers)─┐
4. │ 2024-04-26 │         1 │                                    1 │
5. │ 2024-04-26 │         2 │                                    1 │
6. │ 2024-04-26 │         3 │                                    1 │
└────────────┴───────────┴──────────────────────────────────────┘

MergeState

How to go from the hourly aggregation to the daily aggregation? In cases where you could just sum up the results from the hourly table, that’s of course quite easy to achieve. Unfortunately with the uniq function, you cannot just sum up the intermediate results, as the same user could have been active during multiple hours, therefore the daily result is not the sum of all hourly results. I’ve seen customers resulting to just recalculate the daily table from the basic table, instead of just continuing with the aggregation by using uniqMergeState.:

Unset

INSERT INTO customers_daily
SELECT eventDate, uniqMergeState(uniqueCustomers)
FROM customers_hourly
GROUP BY eventDate

The logic is as easy as it sounds. It will merge the intermediate results, but instead of giving the merged result, it will give the new internal state, therefore being stored in the daily Aggregation Table.

Those have been 5 (+3 bonus) small features I think are good to know. If you have any more neat features or topics you want to read about, feel free to contact me via slack or linked.in

Open Source lives from its community!

Featured image credit: Kevin Ku/Unsplash

The use of large-scale data is revolutionizing the way companies work across industries. This is particularly true for web experience optimization. Companies no longer have to guess at their sales trends, user engagement, customer preferences, or other indicators. With the use of data, companies can examine all manner of factors that go into their company decision making and come out with much more accurate results.

In the sections below, we will look at some specific examples of how data analytics can be used to improve the web experience, and how you can make it work for your company.

The power of data insights

Data analytics is used for every aspect of web development and monitoring. This includes everything from app performance to analyzing user behavior, as well as trends in the market as a whole.

With data analytics, you can gain insight into such things as the amount of time that people put items in their shopping carts before they make a purchase, or how many times they save items and later delete them. These kinds of things provide insight into the nuances of customer behavior and provide important details about trends and personal tendencies.

In cursing custom data analytics solutions, you can pinpoint the patterns of howh customers make decisions and determine likely reasons that they did so. Companies can use the data to refine their sites in different ways, including streamlining the buying process and altering layouts so as to make them more appealing. They can also refine their marketing campaigns thanks to insight gained from buyer preferences.

Customer personalization and improved recommendations

With data analytics, companies can keep much better track of the details of user interactions. In analyzing these interactions, analysts can hone their marketing tools and create more personalized experiences for their customers. Whereas companies might have targeted certain demographic groups based upon larger statistics for those groups, they can now be refined by assessing which types of products individuals are drawn towards and which ones they tend to ignore.

Similarly, companies can better segment their marketing and make their campaigns more targeted to what people really want. This helps to increase customer engagement and reduce churn.

Improved security

Companies can use data analytics to improve their security significantly. Hackers often keep pace with technological developments, and therefore it is critical that cybersecurity outpace them and continue strengthening itself to ward off potential threats.

Certain industries are using data analytics by standard to ward off threats. In finance, for example, analytical tools scan interactions to detect anomalies that might be an indication of fraud.

Split testing

Another way that companies are using data analytics is in split testing. Split testing, or A/B testing, involves creating different versions of a site to assess which version functions better. The way this works is that data analytics monitor the respective engagement levels and methods of users with both versions of a site. This includes ease of use, conversion rates, problems encountered, etc.

The results of these analyses help companies optimize their site for the best possible functioning and ease of use. Tests are performed repeatedly so as to keep sites up-to-date with other changes online.

User behavior monitoring

User behavior is another critical aspect of proper web experience adjustment. Based upon the results of user behavior analysis, analysts can adjust layouts, page features, internal links, etc. For example, if you have a digital publishing solution, you might experience issues with content distribution or reader engagement. In using data analytics, you can pinpoint exactly where the issues lie and resolve them easily.

Performance optimization

Ultimately, the use of data analytics helps companies optimize their web performance by collecting, analyzing, and interpreting data to identify performance bottlenecks and optimize code. For example, you might have issues with the speed of an app, and this could consequently cause a high bounce rate and also turn users away. In analyzing the granular details of your site’s performance, you can eliminate the issues that are causing it to slow down. Any number of performance aspects can be analyzed and optimized with data analytics.

The future of data-driven app development

The use of data analytics is becoming standardized for company websites. Companies that don’t use data to optimize their sites are soon going to find that they’ve been left behind. And this applies to companies of all sizes and in all parts of the world.

If you want your site to keep up with those of the competition, look into employing data analytics as soon as possible. You’ll quickly find that it makes a big difference in customer satisfaction, conversion rates, and ultimately your company growth overall.

Featured image credit: Campaign Creators/Unsplash

Whether you’re in the process of developing web applications, mobile apps, or any other software solution, it’s crucial to recognize that performance and scalability are intertwined. A system that struggles to scale efficiently is likely to be overtaken and supplanted by a competing system that handles growth adeptly. Thus, to ensure the durability of your software system, the initial step is to pinpoint where performance bottlenecks occur within your software architecture.

This blog will provide an overview of performance testing fundamentals, identify prevalent performance bottlenecks, and offer strategies for proficiently executing these tests.

What is performance testing?

Performance testing refers to a type of software testing that focuses on assessing the speed, responsiveness, stability, and scalability of an application. Its primary goal is to identify performance bottlenecks, potential issues, and areas for improvement before the software is deployed to production. Performance testing can help answer questions such as:

• How fast does the application respond under different levels of user traffic?
• Does the application perform consistently over time?
• Can the system handle the expected load without crashing or slowing down?
• What are the resource utilization patterns (CPU, memory, network) under load?

Understanding performance bottlenecks

Performance bottlenecks are specific points in your software architecture where the system’s performance is limited or constrained. Identifying these bottlenecks is crucial for optimizing your software and ensuring its scalability. Here are some common performance bottlenecks:

• CPU Utilization: High CPU usage can slow down your application. Bottlenecks related to CPU often involve inefficient algorithms, excessive calculations, or resource-intensive operations.
• Memory Leaks: These occur when a program fails to release memory properly, causing it to consume more and more memory over time. Memory leaks can often lead to performance degradation and system crashes.
• Database Performance: Slow database queries, inefficient indexing, or insufficient database server resources can impact application performance. Optimizing database access is essential.
• Network Latency: Delays in data transmission between different parts of your application, especially in distributed systems, can slow down response times.
• Concurrency Issues: Poorly managed concurrency can lead to contention for shared resources, leading to performance bottlenecks. Deadlocks, race conditions, and inefficient synchronization can be culprits.
• I/O Operations: Excessive I/O operations, such as reading and writing to disk or external services, can be a bottleneck, especially if they are not asynchronous or optimized.

How can you perform performance testing for your mobile applications?

Executing mobile app performance testing is essential to ensure they handle the expected load and test user experience for positive brand value. Following are the steps to perform performance testing:

1. Define performance objectives

To start performance testing, it’s crucial to clearly define your performance objectives. This involves determining the specific performance metrics you want to measure, such as throughput, response time, and resource utilization. Additionally, set performance goals and establish acceptable thresholds for these metrics to serve as benchmarks during testing.

2. Identify performance testing environment

Creating an appropriate testing environment is essential. This environment should closely behave like the production environment, including hardware, software, and network configurations. Isolation of the test environment from other activities is necessary to prevent interference.

3. Select performance testing tools

Choose the right performance testing tools that align with your objectives. Popular tools like JMeter, LoadRunner, Gatling, and Apache Benchmark can be used. Ensure that you install and configure these tools correctly in your testing environment.

4. Design test scenarios

In this step, design test scenarios that replicate real-world usage patterns and user interactions with the application. Identify critical user journeys and transactions to be tested. Define user profiles, load levels, and any necessary test data requirements.

5. Capacity planning

After conducting load and soak tests, analyze the results to identify performance bottlenecks and areas for improvement. Utilize performance monitoring tools to profile and troubleshoot issues, such as code inefficiencies, database query problems, and system configuration limitations.

6. Tuning and optimization

Make necessary optimizations based on the identified bottlenecks and performance issues. Test the application again to ensure that the changes have a positive impact on performance.

7. Regression testing:

Perform regression testing to ensure that performance improvements do not introduce new issues or regressions in the application. It’s a critical step to maintain and enhance performance over time.

8. Reporting and documentation

Document all aspects of the performance testing process, including test scenarios, test data, test results, and observations. Create comprehensive performance test reports that summarize findings and highlight areas that require attention.

9. Iterate and retest

Continuously iterate the performance testing process as the application evolves. Regularly retest the application to ensure that it consistently meets performance objectives, especially as new features are added or changes are made.

10. Final validation

Once performance meets the defined objectives, validate the application’s performance with stakeholders and obtain their approval before proceeding to production.

11. Continuous performance testing

It is crucial to integrate performance testing into your continuous delivery pipeline, ensuring that performance is continually evaluated as code changes are deployed. This practice helps catch and address performance issues early in the development cycle.

What are the tools used for performance testing?

Tools commonly used for performance testing include:

• Apache JMeter: An open-source tool for load testing, performance testing, and functional testing.
• LoadRunner: A performance testing tool by Micro Focus that supports various protocols and technologies.
• Apache Benchmark (ab): A simple command-line tool for benchmarking HTTP server performance.
• HeadSpin: HeadSpin is a mobile performance testing platform that specializes in testing the performance of mobile apps and websites on real devices and networks. It provides a wide array of AI-driven testing and monitoring capabilities for mobile applications.

Bottom line

Performance testing is crucial as it ensures that software applications can handle expected loads, providing a reliable and responsive user experience while uncovering potential issues, bottlenecks, and vulnerabilities, ultimately contributing to improved software quality and customer satisfaction.

Featured image credit: Towfiqu barbhuiya/Unsplash

In today’s rapidly evolving investment landscape, staying ahead of the curve is crucial for venture capital firms, private equity firms, investment companies, and family offices. That’s where Rundit – a portfolio management and monitoring solutions provider – wants to make a difference and bring the investment industry up-to-date. Ironically, the investors that are funding the future still wrestle with outdated tools like Excel spreadsheets, LP portals, and static PDF reports.

Rundit has long been committed to reshaping the way investment reporting is done. Founded in 2017 in Helsinki, Finland, by a team of entrepreneurs with expertise in fintech, funding, and finance, it has $3 million in funding and customers in over 30 countries.

Today, Rundit has launched its LP Report Builder, designed to give investors seamless reports and enriched insights. Instead of cumbersome spreadsheets or static documents, it offers an automated and visually appealing web-based presentation. But it’s about more than providing easier access or better-looking dashboards.

Investment regulations are continually evolving to enhance transparency and protect investors. The U.S. Securities and Exchange Commission (SEC) recently introduced new rules under the Investment Advisers Act of 1940. These rules require registered private fund advisers to provide investors with detailed quarterly statements covering private fund performance, fees, and expenses.

While these regulations are relatively new in the United States, they have long been in place in Europe. The company claims that its LP Report Builder makes it easier for firms to comply with these regulations, bridging the gap between regulatory environments.

Crucially, it adheres to EIF-Invest Europe-ILPA compliance standards, fostering trust and credibility between fund managers and their limited partners (LPs).

EIF-Invest Europe-ILPA compliance standards refer to guidelines and best practices that private equity and venture capital firms adhere to when reporting to their investors, particularly limited partners (LPs). These standards are designed to enhance transparency, accuracy, and consistency in reporting, ensuring that LPs receive comprehensive and reliable information about the performance of their investments.

By simplifying the reporting process, Rundit’s solution allows professionals to focus on what truly matters: analyzing data, identifying trends, and making strategic moves.

“Our goal is to empower investors with real-time insights that drive better decision-making while freeing them from the burden of consolidating information from multiple data sources,” Jori Karstikko, CEO and co-founder at Rundit, said.

It might not be as sexy as augmented reality or Web3, but by providing an innovative, automated, and visually appealing solution, Rundit allows investors to stay ahead of the curve. In an industry where time is money, this tool streamlines the reporting process, fosters trust between fund managers and LPs, and ultimately enhances decision-making in the fast-paced investment landscape.

This news was originally published at ArcticStartup and is reproduced with permission

In the modern era of data-driven decision-making, business intelligence projects have become the cornerstone for organizations aiming to harness their data for strategic insights. The BI landscape continues to evolve, with innovative projects taking center stage.

The year 2023 brings forth a multitude of trends that will shape the BI. From augmented analytics and AI-driven insights to the convergence of BI and machine learning, these trends are poised to redefine how organizations derive value from their data.

As technology evolves, so do the business models. There are perhaps thousands of different approaches to the analysis of data, each with the potential to create new business intelligence projects. But this diversity often leads to sound pollution. So which business intelligence projects can you trust in your next adventure? Is the right idea always the right investment? Let’s take a look together.

Good business intelligence projects have a lot in common

One of the cornerstones of a successful business intelligence (BI) implementation lies in the availability and utilization of cutting-edge BI tools such as Microsoft’s Fabric. These tools not only streamline the process of data analysis but also empower teams to efficiently dissect complex datasets, uncover intricate patterns, and make informed decisions that drive business growth and innovation.

The advanced capabilities of these tools transcend traditional data processing, enabling organizations to extract actionable insights, identify market trends, and optimize various facets of their operations. From interactive visualizations to real-time collaboration features, these BI tools are a testament to the fusion of technology and business acumen.

The indispensability of BI for companies

Business Intelligence (BI) has transcended its status as a mere technological option; it has now become an indispensable strategic asset for companies across diverse industries. In an age where data reigns supreme, organizations are leveraging BI to not only gain a competitive edge but to fundamentally transform the way they operate.

By utilizing the power of BI, companies can dive deep into market insights, understand customer behaviors, and optimize their operations based on data-driven insights. The integration of BI into decision-making processes enhances agility, enabling companies to pivot swiftly in response to changing market dynamics. This transformation from raw data to actionable intelligence is the catalyst that propels companies toward sustainable success.

Integration of IoT

Internet of Things (IoT) synergizes with Business Intelligence projects, giving rise to a landscape where data-driven insights are no longer confined to static datasets. The seamless integration of IoT-generated data with BI platforms yields real-time insights that unveil dynamic trends, enabling proactive decision-making.

From manufacturing floors to retail spaces, the co-working of IoT and BI empowers organizations to monitor operations in real time, predict maintenance needs, and optimize processes based on live data streams. This convergence is more than a technological advancement; it’s a paradigm shift that empowers organizations to be agile, responsive, and proactive in an ever-evolving business landscape.

Ethical data utilization

Our age is marked by heightened awareness of data privacy and ethics and the spotlight on ethical data utilization has never been more intense.

Business Intelligence projects that give enough importance to data security, compliance, and transparent data practices are gaining traction as organizations recognize the profound importance of responsible data management.

These projects adhere to stringent data protection regulations, ensuring that data is collected, stored, and analyzed in a manner that respects user privacy and maintains data integrity. By establishing trust through ethical data practices, BI projects foster stronger relationships with customers, build brand reputation, and mitigate the potential risks associated with data breaches.

Personalized experiences

The seismic shift toward user-centric design is reshaping how insights are accessed and utilized. Business intelligence projects that offer personalized BI experiences are gaining prominence, recognizing that every stakeholder within an organization has distinct needs and priorities.

These projects craft intuitive dashboards that cater to individual preferences, ensuring that decision-makers can effortlessly access the insights most relevant to their roles. Tailored recommendations, interactive visualizations, and customizable interfaces empower users to interact with data in a more meaningful way, fostering an environment where data-driven decisions are not just a necessity but a seamless and intuitive process.

Business intelligence projects to watch out for

BI, AI, and ML technologies now offer sophisticated and effective solutions to many modern problems. The widespread adoption of these technologies is still very new and one should not miss this train. Investing in the many areas where there are potential effective uses for these technologies may be a step you want to take in your financial adventure.

Every successful investor should follow these projects closely.

Customer churn analysis

Customer churn analysis stands as a vital undertaking in the realm of business intelligence, particularly due to its practicality and popularity. This business intelligence project entails dissecting customer data to discern patterns of attrition, revealing insights that can steer strategic decision-making.

By employing advanced BI tools, teams can analyze regional product sales and profits, identify churn trends over time, and allocate resources effectively. The utilization of interactive visualizations, like combo charts and bar graphs, enhances the interpretability of this analysis, making it an essential venture for businesses aiming to retain their customer base.

Product sales data analysis

In the pursuit of data-driven excellence, businesses are turning to product sales data analysis as a cornerstone of their operations. This project delves into sales records, unearthing critical insights into product performance, profitability, and market trends.

By utilizing the power of BI tools, companies can transform raw sales data into actionable intelligence. Through the adept use of visualization techniques such as pie charts and funnel charts, organizations gain a comprehensive view of their sales landscape, empowering informed decisions.

Marketing campaign insights analysis

The efficacy of marketing campaigns finds a powerful ally in BI projects designed to unravel insights from marketing analytics datasets. This undertaking aids marketing managers in evaluating campaign success rates, product performance, and platform effectiveness.

By using BI tools and diverse visualization methods like bar charts and smart narratives, businesses can align their marketing strategies with actionable insights, optimizing their approach and fostering a competitive edge.

From zero to BI hero: Launching your business intelligence career

Financial performance analysis

The financial realm undergoes a transformative evolution through business intelligence projects centered on financial performance analysis. By harnessing the prowess of BI tools, financial institutions can streamline data analysis, moving from traditional spreadsheets to dynamic BI dashboards.

This project serves to provide timely financial reports, enhance data accuracy, and empower clients to gauge their financial health effectively. As organizations seek robust financial insights, this project paves the way for innovative data-driven solutions.

AutoML cashflow optimization

Automated machine learning (AutoML) projects redefine cash flow optimization. By automating machine learning processes, organizations enhance model quality and rapidly generate insights.

This business intelligence project, optimizes cash flow projections, bolstering decision-making accuracy. Utilizing BI tools, Python scripts, and visualization techniques such as bar charts and tables, multiple sectors find a robust solution for financial analysis.

Healthcare sales analysis

BI projects find resonance in the healthcare sector, offering insights that optimize decision-making. The healthcare sales analysis project, specifically tailored for animal healthcare, enables the tracking of product sales dedicated to treating minor animal species.

Employing BI tools and visualization techniques like column charts and treemaps, businesses can scrutinize sales trends, therapeutic group-wise performance, and city-specific comparisons. This undertaking empowers the sector to deliver enhanced healthcare solutions.

Loan application analysis

The loan application analysis project introduces latent Dirichlet allocation (LDA) to glean insights from loan data. Employing LDA, businesses uncover abstract topics within applications, enhancing decision-making on loan types’ impact on default rates. This business intelligence project transforms raw data into actionable insights, amplifying data-driven lending practices.

Global health expenditure analysis

The global health expenditure analysis project harnesses clustering analysis through Power BI and PyCaret. This venture allows health-related data to be clustered into meaningful categories, shedding light on expenditure patterns.

With visualization techniques like filled maps and scatter charts, this project enables stakeholders to identify trends and disparities, fostering data-driven health initiatives.

Movie sales visualization

The movie sales visualization project infuses cinematic flair into BI endeavors. By transforming movie sales data into interactive visual experiences, this business intelligence project provides stakeholders with comprehensive insights.

Utilizing IMDb datasets and diverse visualizations, such as radial bar charts and histograms, this project encapsulates the synergy of data and storytelling, redefining how movie sales are understood.

Anomaly detection in credit card transactions

Business intelligence projects can also tackle anomaly detection in credit card transactions. By fusing machine learning with BI tools, organizations combat fraud and safeguard financial systems.

This business intelligence project, executed through supervised, semi-supervised, or unsupervised approaches, plays a pivotal role in identifying suspicious activities. Through careful dataset selection, model training, and visualization using line charts and bubble charts, the financial landscape gains a shield against anomalies.

Far from risk-free

Investing in Business Intelligence (BI) and Artificial Intelligence (AI) projects holds the promise of significant benefits, but it’s imperative to recognize the accompanying risks and challenges that these endeavors entail. These technologies have the potential to revolutionize business operations, decision-making, and overall efficiency, yet prudent consideration of the potential pitfalls is crucial for informed decision-making.

BI and AI initiatives frequently necessitate seamless integration with existing systems, a process that can be intricate and time-intensive. Negotiating integration challenges can lead to delays in deployment or unforeseen expenses. The intricate web of connections required for these technologies to function optimally demands careful planning and execution. Mishandling integration can hinder the project’s success and impose unanticipated costs.

The implementation of both BI and AI solutions demands substantial financial investment and the allocation of skilled human resources. Without effective management and oversight, the costs associated with these projects can escalate beyond expectations, potentially undermining the return on investment. Proper budgeting, resource allocation, and diligent oversight are paramount to prevent financial strains and diminished returns.

The foundation of BI and AI lies in data. Dependable insights and decisions hinge on the quality and accuracy of the data being processed. Inaccurate or subpar data can introduce bias and inaccuracies into the outcomes, leading to flawed strategic conclusions. Moreover, as data privacy regulations become more stringent, ensuring the protection of sensitive information and compliance with legal standards is vital to prevent legal and reputational repercussions. Organizations must take measures to guarantee the integrity of data and safeguard individual privacy.

One of the risks associated with the hype surrounding BI and AI is the creation of unrealistic expectations. Organizations might envision immediate, transformative results, only to be disappointed if these technologies take longer to generate substantial value. Clear communication of the project’s timeline and potential outcomes is essential to align expectations with reality.

Not all AI and business intelligence projects yield the anticipated value. Mismatches between project goals and business objectives can lead to underwhelming outcomes. Vague project objectives and insufficient management can contribute to a misalignment between the project’s scope and the organization’s needs.

Implementing business intelligence projects and AI solutions requires specialized skills and expertise. Organizations lacking in-house professionals skilled in these domains might struggle to execute projects effectively. Addressing this gap might involve recruiting new talent, upskilling existing employees, or partnering with external experts.

The rapid pace of evolution in AI and BI technologies means that what is cutting-edge today might be outdated tomorrow. Investing in solutions built on outdated technologies can lead to projects quickly becoming obsolete, resulting in lost investments. Staying abreast of technological advancements and selecting future-proof solutions is vital to safeguard long-term investments.

The allure of AI and business intelligence projects is undeniable, but due diligence and prudent management are essential to navigate the potential pitfalls. By acknowledging the intricacies of integration, budget considerations, data quality, and the dynamic nature of technology, organizations can strategically harness these technologies for sustainable growth and innovation.

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If you’re ready to take your business to the next level and start the digital transformation journey, figuring out how to go about doing so and the first steps to take may seem overwhelming. This is where the creation of a comprehensive digital strategy comes in. A plan will help you figure out exactly the type of software you need and the benefits it will deliver to your customers, employees, and, ultimately, your business’s bottom line.

Providing better customer experiences

When choosing digital software, always keep a focus on what will add value to your clients or customers – think about tools that will enhance the customer journey, make transactions smoother, or allow your clients to get in contact with your support services easier. You could even consider bringing on board some specific DX (digital experience) software that fuses a content management system with a customer relationship management platform to improve the usability and functionality of your eCommerce site – which is likely to translate into an increased conversion rate.

Analytical tools that can interrogate big data to provide you with the best overview possible of who your target audience is, along with buying behavior and the performance of specific marketing drives and campaigns, are also vital.

Boosting productivity

You also need to think carefully about the type of digital software that can be deployed across the entire business to boost productivity and help your teams and departments to communicate better and collaborate. To this end, creating a software evaluation checklist is a good idea to figure out what type of digital tool would best serve your specific business and help you hone in on the best options for your company’s needs.

There are plenty of options available, and it’d likely serve you best to think about solutions designed to support end-to-end digital transformation. Software to enhance video and audio comms, innovative email platforms with built-in collaboration tools, AI automation and agents, digital contact centers, and digital whiteboarding are just a few examples of what’s on the market right now.

Assuring cybersecurity

A key element of any digital transformation strategy must include the deployment of cybersecurity software. The most common types of cybersecurity solutions include antivirus software, firewalls, encryption, and intrusion protection and prevention. As well as general protection for data and against viruses and other malware, look for software that protects identities, networks, and your business’s physical digital assets.

Begin by identifying potential vulnerabilities in your digital systems, processes, and network, thinking about the cyber threats your business is most at risk from.

Is it scaleable?

When choosing any form of software for your business, it’s vital to ensure that it’s scalable and can grow and develop with your company. Ideally, solutions should be able to go the distance and be fully upgradeable so you don’t end up with the inconvenience of having to totally switch systems when your business is ready to take its next step.

Consider digital solutions that have, for example, multiple tiers, so you’ll just pay for what you need right now but are able to hop up a level in time to access additional features, capacity, etc. This will not only save your business money in the long term but will allow productivity to continue unhindered.

Integration is vital

Your business digital transformation strategy should have at its heart a comprehensive overall picture of how the new digital tools you’ll introduce will work together. Solutions should be compatible with your basic means of operations and each other – for example, your analytical software should be able to interrogate data from a range of systems, and your CRM software needs to be compatible with your other digital marketing platforms.

A plan for data management and system integration should underpin your plan to ensure the best customer and employee experiences.

Making sense of the digital transformation journey

Having a clear strategy for your business’s digital transformation is crucial, and this should be fully developed before any software is purchased or deployed. Keeping in mind the reasons behind the switch and the ways in which your customer experience can be upgraded and the productivity of your team can be enhanced is the best way to create a path to your business’s digital future.

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Making the right decisions in an aggressive market is crucial for your business growth and that’s where decision intelligence (DI) comes to play. As each choice can steer the trajectory of an organization, propelling it towards remarkable growth or leaving it struggling to keep pace. In this era of information overload, utilizing the power of data and technology has become paramount to drive effective decision-making.

Decision intelligence is an innovative approach that blends the realms of data analysis, artificial intelligence, and human judgment to empower businesses with actionable insights. Decision intelligence is not just about crunching numbers or relying on algorithms; it is about unlocking the true potential of data to make smarter choices and fuel business success.

Imagine a world where every decision is infused with the wisdom of data, where complex problems are unraveled and transformed into opportunities, and where the path to growth is paved with confidence and foresight. Decision intelligence opens the doors to such a world, providing organizations with a holistic framework to optimize their decision-making processes.

At its core, decision intelligence harnesses the power of advanced technologies to collect, integrate, and analyze vast amounts of data. This data becomes the lifeblood of the decision-making process, unveiling hidden patterns, trends, and correlations that shape business landscapes. But decision intelligence goes beyond the realm of data analysis; it embraces the insights gleaned from behavioral science, acknowledging the critical role human judgment plays in the decision-making journey.

Think of decision intelligence as a synergy between the human mind and cutting-edge algorithms. It combines the cognitive capabilities of humans with the precision and efficiency of artificial intelligence, resulting in a harmonious collaboration that brings forth actionable recommendations and strategic insights.

From optimizing resource allocation to mitigating risks, from uncovering untapped market opportunities to delivering personalized customer experiences, decision intelligence is a guiding compass that empowers businesses to navigate the complexities of today’s competitive world. It enables organizations to make informed choices, capitalize on emerging trends, and seize growth opportunities with confidence.

What is decision intelligence?

Decision intelligence is an advanced approach that combines data analysis, artificial intelligence algorithms, and human judgment to enhance decision-making processes. It leverages the power of technology to provide actionable insights and recommendations that support effective decision-making in complex business scenarios.

At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.

In addition to data analysis, decision intelligence integrates principles from behavioral science to understand how human behavior influences decision-making. By incorporating insights from psychology, cognitive science, and economics, decision models can better account for biases, preferences, and heuristics that impact decision outcomes.

AI algorithms play a crucial role in decision intelligence. These algorithms are carefully selected based on the specific decision problem and are trained using the prepared data. Machine learning algorithms, such as neural networks or decision trees, learn from the data to make predictions or generate recommendations.

The development of decision models is an essential step in decision intelligence. These models capture the relationships between input variables, decision options, and desired outcomes. Rule-based systems, optimization techniques, or probabilistic frameworks are employed to guide decision-making based on the insights gained from data analysis and AI algorithms.

Human judgment is integrated into the decision-making process to provide context, validate recommendations, and ensure ethical considerations. Decision intelligence systems provide interfaces or interactive tools that enable human decision-makers to interact with the models, incorporate their expertise, and assess the impact of different decision options.

Continuous learning and improvement are fundamental to decision intelligence. The system adapts and improves over time as new data becomes available or new insights are gained. Decision models can be updated and refined to reflect changing circumstances and improve decision accuracy.

At the end of the day, decision intelligence empowers businesses to make informed decisions by leveraging data, AI algorithms, and human judgment. It optimizes decision-making processes, drives growth, and enables organizations to navigate complex business environments with confidence.

How does decision intelligence work?

Decision intelligence operates by combining advanced data analysis techniques, artificial intelligence algorithms, and human judgment to drive effective decision-making processes.

Let’s delve into the technical aspects of how decision intelligence works.

Data collection and integration

The process begins with collecting and integrating relevant data from various sources. This includes structured data from databases, unstructured data from text documents or images, and external data from APIs or web scraping. The collected data is then organized and prepared for analysis.

Data analysis and modeling

Decision intelligence relies on data analysis techniques to uncover patterns, trends, and relationships within the data. Statistical methods, machine learning algorithms, and data mining techniques are employed to extract meaningful insights from the collected data.

This analysis may involve feature engineering, dimensionality reduction, clustering, classification, regression, or other statistical modeling approaches.

Behavioral science integration

Decision intelligence incorporates principles from behavioral science to understand and model human decision-making processes. Insights from psychology, cognitive science, and economics are utilized to capture the nuances of human behavior and incorporate them into decision models.

This integration helps to address biases, preferences, and heuristics that influence decision-making.

AI algorithm selection and training

Depending on the nature of the decision problem, appropriate artificial intelligence algorithms are selected. These may include machine learning algorithms like neural networks, decision trees, support vector machines, or reinforcement learning.

The chosen algorithms are then trained using the prepared data to learn patterns, make predictions, or generate recommendations.

Decision model development

Based on the insights gained from data analysis and AI algorithms, decision models are developed. These models capture the relationships between input variables, decision options, and desired outcomes.

The models may employ rule-based systems, optimization techniques, or probabilistic frameworks to guide decision-making.

Human judgment integration

Decision intelligence recognizes the importance of human judgment in the decision-making process. It provides interfaces or interactive tools that enable human decision-makers to interact with the models, incorporate their expertise, and assess the impact of different decision options. Human judgment is integrated to provide context, validate recommendations, and ensure ethical considerations are accounted for.

Continuous learning and improvement

Decision intelligence systems often incorporate mechanisms for continuous learning and improvement. As new data becomes available or new insights are gained, the models can be updated and refined.

This allows decision intelligence systems to adapt to changing circumstances and improve decision accuracy over time.

Decision execution and monitoring

Once decisions are made based on the recommendations provided by the decision intelligence system, they are executed in the operational environment. The outcomes of these decisions are monitored and feedback is collected to assess the effectiveness of the decisions and refine the decision models if necessary.

How is decision intelligence different from artificial intelligence?

AI, standing for artificial intelligence, encompasses the theory and development of algorithms that aim to replicate human cognitive capabilities. These algorithms are designed to perform tasks that were traditionally exclusive to humans, such as decision-making, language processing, and visual perception. AI has witnessed remarkable advancements in recent years, enabling machines to analyze vast amounts of data, recognize patterns, and make predictions with increasing accuracy.

On the other hand, Decision intelligence takes AI a step further by applying it in the practical realm of commercial decision-making. It leverages the capabilities of AI algorithms to provide recommended actions that specifically address business needs or solve complex business problems. The focus of Decision intelligence is always on achieving commercial objectives and driving effective decision-making processes within organizations across various industries.

To illustrate this distinction, let’s consider an example. Suppose there is an AI algorithm that has been trained to predict future demand for a specific set of products based on historical data and market trends. This AI algorithm alone is capable of generating accurate demand forecasts. However, Decision intelligence comes into play when this initial AI-powered prediction is translated into tangible business decisions.

In the context of our example, Decision intelligence would involve providing a user-friendly interface or platform that allows a merchandising team to access and interpret the AI-generated demand forecasts. The team can then utilize these insights to make informed buying and stock management decisions. This integration of AI algorithms and user-friendly interfaces transforms the raw power of AI into practical Decision intelligence, empowering businesses to make strategic decisions based on data-driven insights.

By utilizing Decision intelligence, organizations can unlock new possibilities for growth and efficiency. The ability to leverage AI algorithms in the decision-making process enables businesses to optimize their operations, minimize risks, and capitalize on emerging opportunities. Moreover, Decision intelligence facilitates decision-making at scale, allowing businesses to handle complex and dynamic business environments more effectively.

Below we have prepared a table summarizing the difference between decision intelligence and artificial intelligence:

How can decision intelligence help with your business growth?

Decision intelligence is a powerful tool that can drive business growth. By leveraging data-driven insights and incorporating artificial intelligence techniques, decision intelligence empowers businesses to make informed decisions and optimize their operations.

Strategic decision-making is enhanced through the use of decision intelligence. By analyzing market trends, customer behavior, and competitor activities, businesses can make well-informed choices that align with their growth goals and capitalize on market opportunities.

From zero to BI hero: Launching your business intelligence career

Optimal resource allocation is another key aspect of decision intelligence. By analyzing data and using optimization techniques, businesses can identify the most efficient use of resources, improving operational efficiency and cost-effectiveness. This optimized resource allocation enables businesses to allocate their finances, personnel, and time effectively, contributing to business growth.

Risk management is critical for sustained growth, and decision intelligence plays a role in mitigating risks. Through data analysis and risk assessment, decision intelligence helps businesses identify potential risks and develop strategies to minimize their impact. This proactive approach to risk management safeguards business growth and ensures continuity.

Market insights are invaluable for driving business growth, and decision intelligence help businesses uncover those insights. By analyzing data, customer behavior, and competitor activities, businesses can gain a deep understanding of their target market, identify emerging trends, and seize growth opportunities. These market insights inform strategic decisions and provide a competitive edge.

Personalized customer experiences are increasingly important for driving growth, and decision intelligence enable businesses to deliver tailored experiences. By analyzing customer data and preferences, businesses can personalize their products, services, and marketing efforts, enhancing customer satisfaction and fostering loyalty, which in turn drives business growth.

Agility is crucial in a rapidly changing business landscape, and decision intelligence supports businesses in adapting quickly. By continuously monitoring data, performance indicators, and market trends, businesses can make timely adjustments to their strategies and operations. This agility enables businesses to seize growth opportunities, address challenges, and stay ahead in competitive markets.

There are great companies that offer decision intelligence solutions your business need

There are several companies that offer decision intelligence solutions. These companies specialize in developing platforms, software, and services that enable businesses to leverage data, analytics, and AI algorithms for improved decision-making.

Below, we present you with the best decision intelligence companies out there.

• Qlik
• ThoughtSpot
• DataRobot
• IBM Watson
• Microsoft Power BI
• Salesforce Einstein Analytics

Qlik

Qlik offers a range of decision intelligence solutions that enable businesses to explore, analyze, and visualize data to uncover insights and make informed decisions. Their platform combines data integration, AI-powered analytics, and collaborative features to drive data-driven decision-making.

ThoughtSpot

ThoughtSpot provides an AI-driven analytics platform that enables users to search and analyze data intuitively, without the need for complex queries or programming. Their solution empowers decision-makers to explore data, derive insights, and make informed decisions with speed and simplicity.

DataRobot

DataRobot offers an automated machine learning platform that helps organizations build, deploy, and manage AI models for decision-making. Their solution enables businesses to leverage the power of AI algorithms to automate and optimize decision processes across various domains.

IBM Watson

IBM Watson provides a suite of decision intelligence solutions that leverage AI, natural language processing, and machine learning to enhance decision-making capabilities. Their portfolio includes tools for data exploration, predictive analytics, and decision optimization to support a wide range of business applications.

Microsoft Power BI

Microsoft Power BI is a business intelligence and analytics platform that enables businesses to visualize data, create interactive dashboards, and derive insights for decision-making. It integrates with other Microsoft products and offers AI-powered features for advanced analytics.

While you can access Power BI for a fixed fee, with the giant company’s latest announcement, Microsoft Fabric, you can access all the support your business needs with this service in a pay-as-you-go pricing form.

Salesforce Einstein Analytics

Salesforce Einstein Analytics is an AI-powered analytics platform that helps businesses uncover insights from their customer data. It provides predictive analytics, AI-driven recommendations, and interactive visualizations to support data-driven decision-making in sales, marketing, and customer service.

These are just a few examples of companies offering decision intelligence solutions. The decision intelligence market is continuously evolving, with new players entering the field and existing companies expanding their offerings.

Organizations can explore these solutions to find the one that best aligns with their specific needs and objectives to achieve business growth waiting for them on the horizon.

Certified Business Analysis Professionals, equipped with the necessary skills and expertise, play a pivotal role in the ever-changing world of business. In order to remain relevant and seize opportunities, organizations must make well-timed, informed decisions. This is precisely where the proficiency of business examiners, commonly known as business analysts, becomes invaluable. Certified Business Analysis Professionals specialize in evaluating multiple factors within a company, thereby fostering its growth.

To thrive in the role of a business analyst, it is imperative to stay updated with the latest industry developments. And what better way to achieve this than by obtaining the prestigious CBAP certification? Offered by the International Institute of Business Analysis, headquartered in Canada, this certification carries immense value.

It signifies a level 3 certification, serving as a testament to the individual’s experience and prowess in the field. Armed with this distinguished certificate, professionals can anticipate securing positions at the intermediate or senior level, aligning with their exceptional abilities.

Who is a Certified Business Analysis Professional?

A Certified Business Analysis Professional (CBAP) refers to an individual who has successfully acquired the CBAP certification, a prestigious credential bestowed by the International Institute of Business Analysis (IIBA). These professionals specialize in the field of business analysis and showcase a remarkable level of knowledge, expertise, and experience in this particular domain.

The CBAP certification serves as a testament to the extensive expertise and comprehensive understanding of business analysis possessed by individuals who have earned this prestigious designation. It is specifically designed for seasoned business analysts who have demonstrated proficiency across various facets of the discipline. These include requirements planning and management, enterprise analysis, elicitation and collaboration, requirements analysis, solution assessment, and validation, as well as business analysis planning and monitoring.

By attaining the CBAP certification, professionals validate their proficiency and commitment to excellence in the field of business analysis, thereby distinguishing themselves as highly skilled practitioners. This prestigious credential enhances their credibility, opens up new career opportunities, and sets them apart as recognized leaders in the realm of business analysis.

Working areas of Certified Business Analysis Professional

Certified Business Analysis Professionals (CBAPs) are highly versatile and can be found working in various areas within the field of business analysis. Their expertise and knowledge equip them to handle diverse roles and responsibilities. Here are some common working areas where CBAPs make a significant impact:

Elicitation and analysis: Certified Business Analysis Professionals excel in gathering and comprehending business requirements from stakeholders. They employ techniques such as interviews, workshops, and surveys to extract requirements and analyze them to ensure alignment with organizational objectives.

Planning and management: CBAPs possess the skills to develop strategies and plans for effectively managing requirements throughout the project lifecycle. They establish processes for change management, prioritize requirements, and create requirements traceability matrices.

Business process analysis: CBAPs evaluate existing business processes to identify areas for improvement. They collaborate with stakeholders to streamline workflows, enhance operational efficiency, and boost productivity.

Solution assessment and validation: CBAPs play a crucial role in evaluating and validating proposed solutions to ensure they meet desired business objectives. They conduct impact analyses, assess risks, and perform user acceptance testing to verify solution effectiveness.

Business analysis planning and monitoring: CBAPs contribute to defining the scope and objectives of business analysis initiatives. They develop comprehensive plans, set realistic timelines, allocate resources, and monitor progress to ensure successful project delivery.

Stakeholder engagement and communication: CBAPs possess excellent communication and interpersonal skills, enabling them to engage with stakeholders effectively. They facilitate workshops, conduct presentations, and foster clear communication between business units and project teams.

Enterprise analysis: CBAPs possess a holistic understanding of the organization and conduct enterprise analysis. They assess strategic goals, perform feasibility studies, and identify opportunities for business improvement and innovation.

Data analysis and modeling: Certified Business Analysis Professionals have a solid grasp of data analysis techniques and can create data models to support business analysis activities. They identify data requirements, develop data dictionaries, and collaborate with data management teams.

Business case development: CBAPs contribute to the development of business cases by evaluating costs, benefits, and risks associated with proposed projects. They provide recommendations for investment decisions and assist in justifying initiatives.

Continuous improvement: Certified Business Analysis Professionals actively contribute to the continuous improvement of business analysis practices within organizations. They identify areas for process enhancement, propose new methodologies, and mentor other business analysts.

These examples illustrate the wide range of working areas where Certified Business Analysis Professionals thrive, leveraging their versatile skill set to drive effective analysis and strategic decision-making. Their contributions are instrumental in helping organizations achieve their business goals.

Is CBAP certification recognized?

The CBAP certification is widely recognized and highly regarded in the business analysis industry. It holds international recognition and carries significant value among employers, industry professionals, and organizations.

Employers often prioritize candidates with CBAP certification when hiring for business analysis positions. This certification serves as tangible evidence of a candidate’s proficiency and commitment to the field. It validates their expertise in business analysis principles, techniques, and methodologies.

How to get certified as a business analyst

The CBAP certification is acknowledged as a significant milestone in professional development within the business analysis domain. It can substantially broaden career prospects, open doors to new opportunities, and distinguish individuals in a competitive job market.

The International Institute of Business Analysis (IIBA), the governing body responsible for granting the CBAP certification, is globally acknowledged as a leading authority in the field of business analysis. The IIBA upholds rigorous standards for the certification process, ensuring that Certified Business Analysis Professionals meet the necessary requirements and possess the skills and knowledge essential for success in their roles.

Benefits of becoming a Certified Business Analysis Professional

CBAP certification offers several compelling benefits that can significantly impact your career trajectory. Let’s explore some of the key advantages that can propel your professional growth to new heights.

Undergoing CBAP certification training can greatly enhance your chances of passing the certification exam on your first attempt. Additionally, you can find more detailed information about CBAP benefits here.

Credibility: The CBAP certification holds wide acceptance, which translates to increased credibility in the eyes of employers. It serves as tangible proof of your expertise and competence as a skilled business analyst, making you a desirable candidate for job opportunities.

Job satisfaction: Attaining CBAP certification grants you access to a wealth of valuable tools and resources that streamline your job responsibilities. This means you can work on critical and impactful projects, instilling a sense of importance and confidence in your role. In reputable organizations, knowledge is highly valued, enabling you to apply your expertise and derive job satisfaction from making a meaningful impact.

Skill development: Becoming a Certified Business Analysis Professional equips you with a diverse range of techniques that further develop your skills and enhance your problem-solving abilities. The comprehensive curriculum provides valuable insights and practical knowledge to excel in your business analysis endeavors.

Salary advancement: CBAP certification opens doors to potential salary increases and career advancement opportunities. With this prestigious certification, you demonstrate your proficiency in handling complex programs and projects, positioning yourself for higher-paying roles within the industry.

Industry recognition: Certified Business Analysis Professionals are held in high regard within the business analysis domain. Their commitment to continuous learning and professional development makes them highly sought after by top industries and organizations.

Networking opportunities: Effective networking plays a pivotal role in the business analysis field. CBAP certification provides you with the opportunity to tap into the untapped potential of the industry and connect with like-minded peers, expanding your professional network and fostering valuable collaborations.

By utilizing the benefits of CBAP certification, you can elevate your career prospects, gain industry recognition, and unlock new opportunities for growth and success.

How much does a Certified Business Analysis Professional earn?

In recent years, companies have increasingly sought out professionals with CBAP certifications due to their specialized skill sets and expertise in business analysis. As a result, individuals holding the CBAP designation enjoy several advantages, including better job opportunities, higher income potential, and global recognition.

One of the key benefits of CBAP certification is the improved job prospects it offers. Companies value the comprehensive knowledge and advanced competencies that CBAP recipients possess, making Certified Business Analysis Professionals highly desirable candidates for business analysis roles. With a CBAP certification, you have a competitive edge in the job market, opening doors to a wider range of career opportunities.

The global recognition of CBAP certification further enhances its value. Certified Business Analysis Professionals are acknowledged internationally for their proficiency in business analysis and their adherence to globally recognized standards. This recognition not only adds prestige to your professional profile but also facilitates career advancement on a global scale.

According to data from Indeed, a Certified Business Analysis Professional earn an average salary of $83,000 per year. This figure showcases the financial benefits that come with attaining the CBAP certification, solidifying its reputation as a valuable credential in the field of business analysis.

Alternative certifications to CBAP certification

The CBAP is not the only certification available for professionals who want to demonstrate their skills as business analysts. The Professional in Business Analysis (PBA) certification offered by the Project Management Institute (PMI) is also popular among industry professionals.

Here is a quick overview of the key differences between the two certifications to help you determine which one aligns better with your goals:

CBAP certification

• Requirements: Complete a minimum of 7,500 hours of business analysis work experience within the past ten years, with at least 3,600 hours dedicated to combined areas outlined in the BABOK (Business Analysis Body of Knowledge); 35 hours of professional development within the last four years
• Exam: Consists of 120 multiple-choice questions to be answered within a time frame of 3.5 hours

PBA certification

• Requirements: With a secondary degree, complete 7,500 hours of work experience as a business analysis practitioner, earned within the last eight years, with at least 2,000 hours focused on working on project teams. With a Bachelor’s degree or higher, complete 4,500 hours of work experience as a business analysis practitioner, with at least 2,000 hours dedicated to working on project teams. Both secondary degree and Bachelor’s degree holders require 35 hours of training in business analysis
• Exam: Consists of 200 multiple-choice questions to be answered within a time frame of over 4 hours

The choice of certification will depend on personal preferences. PMI has been established for a longer time than IIBA, but CBAP has been around longer than PBA. Consequently, some employers may be more familiar with one organization or certification than the other. Nevertheless, both certifications are highly regarded. In 2020, for instance, CIO, a notable tech publication, listed CBAP and PBA among the top ten business analyst certifications.

The role of Certified Business Analysis Professionals (CBAPs) in the field of business analysis cannot be overstated. These highly skilled individuals have demonstrated their expertise, knowledge, and commitment to the profession through the rigorous CBAP certification process. The CBAP designation not only signifies credibility and recognition on a global scale but also opens doors to better job opportunities and higher income potential.

In a world of evolving business landscapes and increasing demand for effective decision-making, CBAPs play a crucial role in driving organizational success. Their expertise, coupled with their commitment to excellence, makes them instrumental in delivering impactful business solutions and fostering innovation. With their invaluable contributions to the field of business analysis, CBAPs shape the future of organizations and drive success in an ever-changing business world.

Microsoft has made good on its promise to deliver a simplified and more efficient Microsoft Fabric price model for its end-to-end platform designed for analytics and data workloads. Based on the total compute and storage utilized by customers, the company’s new pricing structure eliminates the need for separate payment for compute and storage buckets associated with each of Microsoft’s multiple services.

This strategic move lights up the competition with major rivals like Google and Amazon, who offer similar analytics and data products but charge customers multiple times for various discrete tools employed on their respective cloud platforms.

Microsoft Fabric price is about to be announced

Although we do not have official Microsoft Fabric price data, which will be shared tomorrow, VentureBeat shared the average prices that Microsoft will charge for this service, and it is as follows:

As you can see in the table, the Microsoft Fabric price is shaped to deliver the service your company needs with minimum expenditure by choosing a way that you will pay as much as you use according to the quantity of SKU and CU you will use and not on a fixed price of the service you receive.

Especially for small businesses, we think that this kind of payment plan is much more accurate and a good step to ensure equality in the market because similar services in the market are not very accessible, especially on a low budget.

Microsoft’s unified pricing model for the Fabric suite marks a significant advancement in the analytics and data market. With this model, customers will be billed based on the total computing and storage they utilize.

This eliminates the complexities and costs associated with separate billing for individual services. By streamlining the pricing process, Microsoft is positioning itself as a formidable competitor to industry leaders such as Google and Amazon, who have repeatedly charged customers for different tools employed within their cloud ecosystems.

It is a fact that the Microsoft Fabric price will differentiate it from other tools in the industry because, normally, when you buy such services, you are billed for several services that you do not really use. The pricing Microsoft offers your business is a bit unusual.

All you need in one place

So is the Microsoft Fabric price the tech giant’s only plan to stay ahead of the data game? Of course not!

Microsoft Fabric suite integration brings together six different tools into a unified experience and data architecture, including:

• Azure Data Factory
• Azure Synapse Analytics
  • Data engineering
  • Data warehouse
  • Data science
  • Real-time analytics
• Power BI

This consolidation within the Microsoft Fabric price you will pay allows engineers and developers to seamlessly extract insights from data and present them to business decision-makers.

Microsoft’s focus on integration and unification sets Fabric apart from other vendors in the market, such as Snowflake, Qlik, TIBCO, and SAS, which only offer specific components of the analytics and data stack.

This integrated approach provides customers with a comprehensive solution encompassing the entire data journey, from storage and processing to visualization and analysis.

The contribution of Power BI

The integration of Microsoft Power BI and Microsoft Fabric offers a powerful combination for organizations seeking comprehensive data analytics and insights. Together, these two solutions work in harmony, providing numerous benefits:

• Streamlined analytics workflow: Power BI’s intuitive interface and deep integration with Microsoft products seamlessly fit within the Microsoft Fabric ecosystem, enabling a cohesive analytics workflow.
• Unified data storage: Fabric’s centralized data lake, Microsoft OneLake, eliminates data silos and provides a unified storage system, simplifying data access and retrieval.
• Cost efficiency: Power BI can directly leverage data stored in OneLake, eliminating the need for separate SQL queries and reducing costs associated with data processing.
• Enhanced insights through AI: Fabric’s generative AI capabilities, such as Copilot, enhance Power BI by enabling users to use conversational language to create data flows, build machine learning models, and derive deeper insights.
• Multi-cloud support: Fabric’s support for multi-cloud environments, including shortcuts that virtualize data lake storage across different cloud providers, allows seamless incorporation of diverse data sources into Power BI for comprehensive analysis.
• Flexible data visualization: Power BI’s customizable and visually appealing charts and reports, combined with Fabric’s efficient data storage, provide a flexible and engaging data visualization experience.
• Scalability and performance: Fabric’s robust infrastructure ensures scalability and performance, supporting Power BI’s data processing requirements as organizations grow and handle larger datasets.
• Simplified data management: With Fabric’s unified architecture, organizations can provision compute and storage resources more efficiently, simplifying data management processes.
• Data accessibility: The integration allows Power BI users to easily access and retrieve data from various sources within the organization, promoting data accessibility and empowering users to derive insights.

This combination enables organizations to unlock the full potential of their data and make data-driven decisions with greater efficiency and accuracy.

Centralized data lake for all your data troubles

At the core of Microsoft Fabric lies the centralized data lake, known as Microsoft OneLake. OneLake is designed to store a single copy of data in a unified location, leveraging the open-source Apache Parquet format.

This open format allows for seamless storage and retrieval of data across different databases. By automating the integration of all Fabric workloads into OneLake, Microsoft eliminates the need for developers, analysts, and business users to create their own data silos.

This approach not only improves performance by eliminating the need for separate data warehouses but also results in substantial cost savings for customers.

Flexible compute capacity

One of the key advantages of Microsoft Fabric is its ability to optimize compute capacity across different workloads. Unused compute capacity from one workload can be utilized by another, ensuring efficient resource allocation and cost optimization. Microsoft’s commitment to innovation is evident in the addition of Copilot, Microsoft’s chatbot powered by generative AI, to the Fabric suite.

Copilot enables developers and engineers to interact in conversational language, simplifying data-related tasks such as querying, data flow creation, pipeline management, code generation, and even machine learning model development.

Moreover, Fabric supports multi-cloud capabilities through “Shortcuts,” allowing virtualization of data lake storage in Amazon S3 and Google Cloud Storage, providing customers with flexibility in choosing their preferred cloud provider.

Why should your business use Microsoft Fabric?

Microsoft Fabric offers numerous advantages for businesses that are looking to enhance their data and analytics capabilities.

Here are compelling reasons why your business should consider using Microsoft Fabric:

• Unified data platform: Microsoft Fabric provides a comprehensive end-to-end platform for data and analytics workloads. It integrates multiple tools and services, such as Azure Data Factory, Azure Synapse Analytics, and Power BI, into a unified experience and data architecture. This streamlined approach eliminates the need for separate solutions and simplifies data management.
• Simplified pricing: The Microsoft Fabric price is based on total compute and storage usage. Unlike some competitors who charge separately for each service or tool, Microsoft Fabric offers a more straightforward pricing model. This transparency helps businesses control costs and make informed decisions about resource allocation.
• Cost efficiency: With Microsoft Fabric, businesses can leverage a shared pool of compute capacity and a single storage location for all their data. This eliminates the need for creating and managing separate storage accounts for different tools, reducing costs associated with provisioning and maintenance. This is one of the most important features that make the Microsoft Fabric price even more accessible.
• Improved performance: Fabric’s centralized data lake, Microsoft OneLake, provides a unified and open architecture for data storage and retrieval. This allows for faster data access and eliminates the need for redundant SQL queries, resulting in improved performance and reduced processing time.
• Advanced analytics capabilities: Microsoft Fabric offers advanced analytics features, including generative AI capabilities like Copilot, which enable users to leverage artificial intelligence for data analysis, machine learning model creation, and data flow creation. These capabilities empower businesses to derive deeper insights and make data-driven decisions.
• Multi-cloud support: Fabric’s multi-cloud support allows businesses to seamlessly integrate data from various cloud providers, including Amazon S3 and Google storage. This flexibility enables organizations to leverage diverse data sources and work with multiple cloud platforms as per their requirements.
• Scalability and flexibility: Microsoft Fabric is designed to scale with the needs of businesses, providing flexibility to handle growing data volumes and increasing analytics workloads. The platform’s infrastructure ensures high performance and reliability, allowing businesses to process and analyze large datasets effectively.
• Streamlined workflows: Fabric’s integration with other Microsoft products, such as Power BI, creates a seamless analytics workflow. Users can easily access and analyze data stored in the centralized data lake, enabling efficient data exploration, visualization, and reporting.
• Simplified data management: Microsoft Fabric’s unified architecture and centralized data lake simplify data management processes. Businesses can eliminate data silos, provision resources more efficiently, and enable easier data sharing and collaboration across teams.
• Microsoft ecosystem integration: As part of the broader Microsoft ecosystem, Fabric integrates seamlessly with other Microsoft services and tools. This integration provides businesses with a cohesive and comprehensive solution stack, leveraging the strengths of various Microsoft offerings.

When we take the Microsoft Fabric price into account, bringing all these features together under a pay-as-you-go model is definitely a great opportunity for users.

How to try Microsoft Fabric for free

Did you like what you saw? You can try this platform that can handle all your data-related tasks without even paying the Microsoft Fabric price.

To gain access to the Fabric app, simply log in to app.fabric.microsoft.com using your Power BI account credentials. Once logged in, you can take advantage of the opportunity to sign up for a free trial directly within the app, and the best part is that no credit card information is needed.

In the event that the account manager tool within the app does not display an option to initiate the trial, it is possible that your organization’s tenant administration has disabled access to Fabric or trials. However, don’t worry, as there is still a way for you to acquire Fabric. You can proceed to purchase Fabric via the Azure portal by following the link conveniently provided within the account manager tool.

Microsoft Fabric price and its impact on competitors

The move on the Microsoft Fabric price, which offers a unified approach, poses a significant challenge to major cloud competitors like Amazon and Google, who have traditionally charged customers separately for various services.

By providing a comprehensive and integrated package of capabilities, Fabric also puts pressure on vendors that offer only specific components of the analytics and data stack. For instance, Snowflake’s reliance on proprietary data formats and limited interoperability raises questions about its ability to compete with Microsoft’s holistic solution.

Let’s see if Microsoft can once again prove why it is a leading technology company and usher in a new era of data management.

Open source business intelligence software is a game-changer in the world of data analysis and decision-making. It has revolutionized the way businesses approach data analytics by providing cost-effective and customizable solutions that are tailored to specific business needs. With open source BI software, businesses no longer need to rely on expensive proprietary software solutions that can be inflexible and difficult to integrate with existing systems.

Instead, open source BI software offers a range of powerful tools and features that can be customized and integrated seamlessly into existing workflows, making it easier than ever for businesses to unlock valuable insights and drive informed decision-making.

What is open source business intelligence?

Open-source business intelligence (OSBI) is commonly defined as useful business data that is not traded using traditional software licensing agreements. This is one alternative for businesses that want to aggregate more data from data-mining processes without buying fee-based products.

What are the features of an open source business intelligence software?

Open source business intelligence software provides a cost-effective and flexible way for businesses to access and analyze their data. Here are some of the key features of open source BI software:

• Data integration: Open source BI software can pull data from various sources, such as databases, spreadsheets, and cloud services, and integrate it into a single location for analysis.
• Data visualization: Open source BI software offers a range of visualization options, including charts, graphs, and dashboards, to help businesses understand their data and make informed decisions.
• Report generation: Open source BI software enables businesses to create customized reports that can be shared with team members and stakeholders to communicate insights and findings.
• Predictive analytics: Open source BI software can use algorithms and machine learning to analyze historical data and identify patterns that can be used to predict future trends and outcomes.
• Collaboration: Open source BI software allows team members to work together on data analysis and share insights with each other, improving collaboration and decision-making across the organization.

How to select the right business intelligence software?

Selecting the right open source business intelligence software can be a challenging task, as there are many options available in the market. Here are some factors to consider when selecting the right BI software for your business:

• It’s important to identify the specific business needs that the BI software should address. Consider the types of data you want to analyze, the frequency of reporting, and the number of users who will need access to the software.
• Look for BI software that can integrate data from different sources, such as databases, spreadsheets, and cloud services. This ensures that all data is available for analysis in one central location.
• BI software should be easy to use and have a user-friendly interface. This allows users to quickly analyze data and generate reports without needing extensive training.
• BI software should allow for customization of reports and dashboards. This allows users to tailor the software to their specific needs and preferences.
• Ensure that the BI software has robust security features to protect sensitive data. Look for software that supports role-based access control, data encryption, and secure user authentication.
• Consider the future growth of your business and ensure that the BI software can scale to meet your future needs.
• Consider the cost of the software and any associated licensing fees or maintenance costs. Open source BI software can be a cost-effective option as it is typically free to use and has a large community of developers who provide support.

The right business intelligence strategy leads to lucrative results

Why not opt for a paid version instead?

While open source business intelligence software is a great option for many businesses, there are also some benefits to using a paid version. Here are some reasons why businesses may want to consider a paid BI software:

• Paid BI software often comes with more advanced features, such as predictive analytics and machine learning, that can provide deeper insights into data.
• Paid BI software often comes with dedicated technical support, which can help businesses troubleshoot any issues and ensure that the software is running smoothly.
• Paid BI software often provides more robust security features, such as data encryption and secure user authentication, to protect sensitive data.
• Paid BI software often integrates with other tools, such as customer relationship management (CRM) or enterprise resource planning (ERP) software, which can provide a more comprehensive view of business operations.
• Paid BI software often allows for greater customization, allowing businesses to tailor the software to their specific needs and preferences.
• Paid BI software often offers more scalability options, allowing businesses to easily scale up or down as needed to meet changing business needs.

15 open source business intelligence software (free)

It’s important to note that the following list of 15 open source business intelligence software tools is not ranked in any particular order. Each of these software solutions has its own unique features and capabilities that are tailored to different business needs. Therefore, businesses should carefully evaluate their specific requirements before choosing a tool that best fits their needs.

ClicData

ClicData provides a range of dashboard software solutions, including ClicData Personal, which is available free of cost and provides users with 1 GB of data storage capacity along with unlimited dashboards for a single user. Alternatively, the premium version of ClicData offers more extensive features, including a greater number of data connectors, the ability to automate data refreshes, and advanced sharing capabilities for multi-user access.

JasperReports Server

JasperReports Server is a versatile reporting and analytics software that can be seamlessly integrated into web and mobile applications, and used as a reliable data repository that can deliver real-time or scheduled data analysis. The software is open source, and also has the capability to manage the Jaspersoft paid BI reporting and analytics platform.

Preset

Preset is a comprehensive business intelligence software designed to work with Apache Superset, an open-source software application for data visualization and exploration that can manage data at the scale of petabytes. Preset provides a fully hosted solution for Apache Superset, which was originally developed as a hackathon project at Airbnb in the summer of 2015.

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Helical Insight

Helical Insight is an open-source business intelligence software that offers a wide range of features, including e-mail scheduling, visualization, exporting, multi-tenancy, and user role management. The framework is API-driven, allowing users to seamlessly incorporate any additional functionality they may require. The Instant BI feature of Helical Insight facilitates a user-friendly experience, with a Google-like interface that enables users to ask questions and receive relevant reports and charts in real-time.

Lightdash

Lightdash is a recently developed open-source business intelligence software solution that can connect with a user’s dbt project, and enable the addition of metrics directly in the data transformation layer. This allows users to create and share insights with the entire team, promoting collaboration and informed decision-making.

KNIME

KNIME is a powerful open-source platform for data analysis that features over 1,000 modules, an extensive library of algorithms, and hundreds of pre-built examples of analyses. The software also offers a suite of integrated tools, making it an all-in-one solution for data scientists and BI executives. With its broad range of features and capabilities, KNIME has become a popular choice for data analysis across a variety of industries.

Abixen

Abixen is a software platform that is based on microservices architecture, and is primarily designed to facilitate the creation of enterprise-level applications. The platform empowers users to implement new functionalities by creating new, separate microservices. Abixen’s organizational structure is divided into pages and modules, with one of the modules dedicated to Business Intelligence services. This module enables businesses to leverage sophisticated data analysis tools and techniques to gain meaningful insights into their operations and drive informed decision-making.

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Microsoft Power BI

Microsoft Power BI offers a free version of their platform, which comes with a 1 GB per user data capacity limit and a once-per-day data-refresh schedule. The platform’s dashboards allow users to present insights from a range of third-party platforms, including Salesforce and Google Analytics, on both desktop and mobile devices. Additionally, Power BI provides users with the ability to query the software using natural language, which enables users to enter plain English queries and receive meaningful results.

ReportServer

ReportServer is a versatile open source business intelligence software solution that integrates various reporting engines into a single user interface, enabling users to access the right analytics tool for the right purpose at the right time. The software is available in both a free community tier and an enterprise tier, and offers a range of features and capabilities, including the ability to generate ad-hoc list-like reports through its Dynamic List feature. This functionality empowers users to quickly generate customized reports based on their specific needs, promoting informed decision-making across the organization.

SpagoBI / Knowage

SpagoBI is a comprehensive open-source business intelligence suite that comprises various tools for reporting, charting, and data-mining. The software is developed by the Open Source Competency Center of Engineering Group, which is a prominent Italian software and services company that provides a range of professional services, including user support, maintenance, consultancy, and training. The SpagoBI team has now rebranded the software under the Knowage brand, which continues to offer the same suite of powerful BI tools and features.

Helical Insight

Helical Insights is an innovative open-source BI tool that adopts a unique approach to self-service analytics. The software provides a BI platform that enables end-users to seamlessly incorporate any additional functionality that they may require by leveraging the platform’s API. This enables businesses to customize the BI tool to their specific needs, and to promote informed decision-making based on meaningful insights.

A comprehensive look at data integration and business intelligence

Jaspersoft

Jaspersoft is a versatile and highly customizable Business Intelligence platform that is developer-friendly, and allows developers to create analytics solutions that are tailored to the specific needs of their business. The platform is highly regarded by many users for its extensive customization options, and is particularly favored by Java developers. However, some users have noted certain weaknesses of the platform, such as a lack of support in the community for specific problems, as well as an unintuitive design interface. Nonetheless, Jaspersoft remains a popular choice for businesses that require a flexible and developer-friendly BI platform.

Tableau Public

Tableau Public is a free, powerful BI software that empowers users to create interactive charts and live dashboards, and publish them on the internet, embed them on a website, or share them on social media. The software provides a range of customization options that enable users to optimize the display of their content across various platforms, including desktop, tablet, and mobile devices. Additionally, Tableau Public can connect to Google Sheets, and data can be auto-refreshed once per day, ensuring that users always have access to the most up-to-date information. Overall, Tableau Public is an excellent choice for anyone who wants to create and share compelling data visualizations.

BIRT

BIRT (Business Intelligence Reporting Tool) is an open source business intelligence software project that has achieved top-level status within the Eclipse Foundation. The software is designed to pull data from various data sources, enabling users to generate powerful reports and visualizations that support informed decision-making. With its flexible architecture and extensive set of features, BIRT is a popular choice for businesses and organizations that require a reliable and versatile BI tool.

Zoho Reports

Zoho Reports is a powerful BI platform that enables users to connect to almost any data source and generate visual reports and dashboards for analysis. The software is equipped with a robust analytics engine that can process hundreds of millions of records and return relevant insights in a matter of seconds. With its extensive range of features, Zoho Reports is a popular choice for businesses that require a reliable and versatile BI tool. The software also offers a free version that allows for up to two users, making it a cost-effective option for smaller organizations or teams.

Final words

Open source business intelligence software has become an essential tool for businesses looking to make data-driven decisions. The benefits of open source BI software are clear: cost-effectiveness, customization, flexibility, and scalability. With a wide range of tools and features available, businesses can easily adapt open source BI software to their specific needs, and leverage powerful analytics tools to gain meaningful insights into their operations. By embracing open source BI software, businesses can stay ahead of the competition, make informed decisions, and drive growth and success.

From zero to BI hero: Launching your business intelligence career

FAQ

What are the benefits of using open source business intelligence software?

The benefits of using open source business intelligence software include cost savings, customization capabilities, and community support. Open source business intelligence software can provide organizations with the tools they need to analyze data, create reports, and make informed business decisions.

How do I choose the right open source business intelligence software for my organization?

When choosing the right open source business intelligence software for your organization, consider factors such as features, data sources, user interface, customization options, and community support.

How do I integrate open source business intelligence software with other systems?

Integrating open source business intelligence software with other systems can be done using APIs or connectors. Choose compatible systems and test the integration to ensure that it is working correctly.

How can I ensure the security of my open source business intelligence software?

Implement access controls, encryption, and keep the software up-to-date with the latest security patches and updates. Use strong passwords and two-factor authentication to provide an extra layer of security.

Obtaining a certification as a Certified Business Analysis Professional (CBAP) can prove to be a valuable asset for career advancement. The International Institute of Business Analysis (IIBA®) recognizes CBAPs as authoritative figures in identifying an organization’s business needs and formulating effective business solutions.

As primary facilitators, CBAPs act as intermediaries between clients, stakeholders, and solution teams, thus playing a crucial role in the success of projects. Given the increasing recognition of their role as indispensable contributors to projects, CBAPs assume responsibility for requirements development and management.

Obtaining the CBAP certification involves showcasing the experience, knowledge, and competencies required to qualify as a proficient practitioner of business analysis, as per the criteria laid out by the IIBA. This certification program caters to intermediate and senior level business analysts, and the rigorous certification process assesses the candidate’s ability to perform business analysis tasks across various domains, such as strategy analysis, requirements analysis, and solution evaluation.

It is noteworthy that becoming an IIBA member is not a prerequisite for appearing in the CBAP exam. Thus, this certification program provides an excellent opportunity for non-members to leverage their skills and elevate their careers in business analysis.

Benefits of obtaining the CBAP certification

Acquiring a CBAP certification can have a significant positive impact on a professional’s job prospects, wage expectations, and career trajectory. Some of the most prevalent benefits of obtaining this certification include:

• Distinguish oneself to prospective employers: In today’s competitive job market, obtaining the CBAP certification can set one apart from other candidates and improve the chances of securing a job. Research conducted by the U.S. Bureau of Labor Statistics suggests that professionals with certifications or licenses are less likely to face unemployment compared to those without such credentials.
• Demonstrate expertise and experience: To qualify for the CBAP certification, applicants must have a minimum of five years (7,200 hours) of relevant work experience and pass a comprehensive exam covering various aspects of business analysis, including planning and monitoring, requirements elicitation and management, solution evaluation, and others. This certification, therefore, serves as an indicator of one’s skill set, knowledge, and experience in business analysis.
• Potentially increase remuneration: According to the IIBA’s Annual Business Analysis Survey, professionals who hold the CBAP certification earn, on average, 13% more than their uncertified peers. Hence, obtaining the CBAP certification may lead to higher compensation and financial benefits.

How to become a certified business analysis professional (CBAP)?

Becoming an IIBA CBAP requires a dedicated effort towards the study and application of business analysis principles. If you’re considering pursuing this certification, here are the key steps you’ll need to take:

Conclude the assessment requirements

Becoming an IIBA CBAP requires a dedicated effort towards the study and application of business analysis principles. If you’re considering pursuing this certification, here are the key actions you’ll need to take:

• Meet the eligibility requirements: To qualify for the CBAP certification, you must have a minimum of five years (7,200 hours) of relevant work experience in business analysis, as well as 35 hours of Professional Development (PD) in the past four years.
• Prepare for the certification exam: The CBAP exam is a comprehensive assessment of your knowledge and skills in various domains of business analysis. The IIBA provides study materials such as the BABOK® Guide (Business Analysis Body of Knowledge) to help you prepare for the exam.
• Schedule and pass the exam: Once you feel confident in your preparation, you can schedule the CBAP exam at an IIBA-approved testing center. Passing the exam demonstrates your expertise and competence in business analysis, qualifying you as a Certified Business Analysis Professional.
• Maintain your certification: To maintain your CBAP certification, you must complete a minimum of 60 Continuing Development Units (CDUs) every three years. These activities demonstrate your commitment to professional development and help you stay current with the latest trends and practices in business analysis.

From zero to BI hero: Launching your business intelligence career

Register for the exam

Once you have fulfilled the eligibility requirements, you can proceed to enroll for the CBAP exam. To register for the exam, you must provide two professional references who can vouch for your credentials and experience in business analysis. Additionally, you must agree to abide by the IIBA’s Code of Conduct and Terms and Conditions, and pay a $145 application fee.

Train for the test

To ensure success on the day of the CBAP exam, it is essential to allocate sufficient time for exam preparation. The CBAP exam comprises 120 multiple-choice questions that cover a wide range of topics related to business analysis.

• Business analysis and Planning: 14%
• Elicitation and Collaboration: 12%
• Requirements life cycle management: 15%
• Strategy analysis: 15%
• Requirements analysis and design definition: 30%
• Solution evaluation: 14%

To increase the likelihood of success on the CBAP exam, it is recommended to allocate time for dedicated study and practice rather than relying solely on work experience. While many of the topics covered in the exam may be familiar to business analysts from their regular work, the testing environment is markedly different from the workplace.

Take the CPAB exam

The CBAP exam can be taken through either in-person testing at a PSI test center or online remote proctoring. When registering for the exam, candidates should select the testing environment that suits their needs to perform optimally on the test. The exam comprises 120 multiple-choice questions and must be completed within 3.5 hours, covering various domains of business analysis. The purpose of the exam is to assess the candidate’s knowledge and skills in business analysis, and passing it leads to the award of the CBAP certification.

Congratulations

After passing the CBAP exam, candidates are awarded the CBAP certification. They can add this credential to their professional documents, such as their resume and LinkedIn profile, to showcase their business analysis expertise. The certification demonstrates their commitment to professional development and can enhance their career prospects.

Average certified business analysis professional salary

Glassdoor estimates that the median annual pay for a Certified Business Analyst in the United States area is $69,390, with an estimated total pay of $74,607 per year. The estimated additional pay for a Certified Business Analyst is $5,217 per year, which may include cash bonuses, commissions, tips, and profit sharing. These estimates are based on data collected from Glassdoor’s proprietary Total Pay Estimate model and reflect the midpoint of the salary ranges. The “Most Likely Range” represents the values that fall within the 25th and 75th percentile of all pay data available for this role.

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Bottom line

The complexity of modern business demands a deep understanding of organizational needs, market trends, and the latest technological advancements. As the role of business analysts continues to grow in importance, obtaining a Certified Business Analysis Professional (CBAP) certification has become an indispensable step for those seeking to excel in the field. This prestigious certification attests to a professional’s mastery of the key principles and practices of business analysis, enabling them to navigate complex challenges and drive strategic growth for their organizations.

In a world of rapid technological change and increasing market complexity, the CBAP certification has emerged as a vital credential for professionals seeking to stay competitive in the field of business analysis. With its focus on advanced skills and knowledge, the CBAP certification represents a hallmark of excellence and a commitment to delivering tangible results in the fast-paced world of business.

In today’s fast-paced business landscape, companies need to stay ahead of the curve to remain competitive. Business intelligence (BI) has emerged as a key solution to help companies gain insights into their operations and market trends. BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions.

A career path in BI can be a lucrative and rewarding choice for those with interest in data analysis and problem-solving. In this article, we will explore the importance of BI in today’s business landscape, the skills and qualifications needed for a career in BI, and the opportunities available in this growing field.

What is business intelligence?

Business intelligence is the process of analyzing data to provide businesses with insights into their operations and market trends. BI involves using data mining, reporting, and querying techniques to identify key business metrics and KPIs that can help companies make informed decisions. BI solutions can include tools such as dashboards, scorecards, and visualizations that allow businesses to track and analyze their performance in real-time.

Importance of business intelligence in today’s business landscape

In today’s fast-paced business landscape, companies need to have a solid understanding of their operations and the market to remain competitive. Business intelligence provides this understanding by giving companies insights into their performance and industry trends.

Some of the key benefits of BI include the following:

• Improved decision-making: BI enables businesses to make data-driven decisions by providing real-time insights into their operations, customer behavior, and market trends.
• Increased efficiency: BI can help streamline operations by automating data collection and analysis, reducing manual effort, and improving accuracy.
• Better customer experience: BI solutions can help businesses gain a better understanding of their customers’ needs and preferences, allowing them to tailor their products and services to meet these needs.
• Competitive advantage: BI can give businesses a competitive edge by enabling them to identify emerging trends, stay ahead of the competition, and capitalize on new opportunities.

Business intelligence career opportunities

A career path in business intelligence offers a wide range of opportunities for individuals with the right skills and qualifications. Some of the common career opportunities in BI include:

Entry-level roles

• Data analyst: A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.
• BI developer: A BI developer is responsible for designing and implementing BI solutions, including data warehouses, ETL processes, and reports. They may also be involved in data integration and data quality assurance.
• Junior consultant: A junior consultant works with clients to identify their business requirements and develop solutions that meet their needs. They may also be involved in project management and training.

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Mid-level roles

• Senior analyst: A senior analyst is responsible for leading data analysis projects and presenting insights to senior management. They may also be involved in mentoring junior team members and contributing to the development of BI strategy.
• BI manager: A BI manager is responsible for overseeing the BI team, setting strategy and goals, and ensuring that BI solutions meet business requirements. They may also be responsible for budgeting, hiring, and training.
• Consultant: A consultant works with clients to develop and implement BI solutions. They may also be involved in business process analysis, project management, and training.

Advanced roles

• Director of BI: A director of BI is responsible for leading the development and implementation of BI strategy across an organization. They may also be involved in budgeting, hiring, and training.
• Chief data officer: A chief data officer is responsible for the management and governance of an organization’s data assets. They may also be involved in developing data policies and standards and ensuring compliance with regulations.
• Executive consultant: An executive consultant works with senior executives to develop and implement BI solutions that support business strategy. They may also be involved in thought leadership and business development.

To pursue a career path in BI, a strong background in data analysis and programming is essential. A degree in computer science, mathematics, statistics, or a related field is often preferred.

Relevant certifications, such as those offered by Microsoft, IBM, or Tableau, can also help demonstrate expertise in BI tools and techniques. Additionally, gaining experience through internships or entry-level positions can help individuals build a strong foundation in BI and advance their careers.

Skills and qualifications for a business intelligence career path

Business intelligence is a technical field that requires a combination of technical and soft skills. To succeed in a BI career path, individuals need to possess the following skills and qualifications:

Technical skills

• Data analysis: A BI professional should have a strong background in data analysis, including the ability to collect, manipulate, and interpret data.
• Database management: A BI professional should be able to design and manage databases, including data modeling, ETL processes, and data integration.
• Programming: A BI professional should have knowledge of programming languages such as SQL, Python, or R, and be able to use them to extract data and perform advanced analytics.
• Data visualization: A BI professional should be able to create reports, dashboards, and visualizations that communicate complex data insights to stakeholders.

Soft skills

• Communication: A BI professional should have excellent communication skills, both verbal and written, to effectively communicate insights to stakeholders and collaborate with other team members.
• Problem-solving: A BI professional should have the ability to identify problems, analyze data, and develop solutions that meet business needs.
• Critical thinking: A BI professional should have strong critical thinking skills to evaluate data and develop insights that drive business decisions.
• Teamwork: A BI professional should be able to work collaboratively with cross-functional teams, including IT, marketing, finance, and operations.

Educational background

• Degrees: Many BI professionals have degrees in computer science, information systems, mathematics, or statistics. However, individuals with degrees in other fields, such as business or economics, can also pursue a career in BI with the right technical skills and experience.
• Certifications: In addition to degrees, certifications in BI tools and technologies, such as Microsoft Power BI, Tableau, or Oracle BI, can also help demonstrate expertise and advance a BI career.

Industry and salary outlook for business intelligence

Business intelligence is a rapidly growing field, and the demand for BI professionals is on the rise. In this section, we will provide an overview of the current demand for BI professionals, the industries that rely on BI, and the average salaries for BI roles.

Current demand for BI Professionals

The demand for BI professionals is expected to grow in the coming years as organizations increasingly rely on data-driven insights to inform their decisions. According to the U.S. Bureau of Labor Statistics, employment of management analysts, a category that includes BI professionals, is projected to grow 11 percent from 2029 to 2029, which is much faster than the average for all occupations.

Industries that rely on BI

Many industries rely on business intelligence to make informed decisions and stay competitive. Some of the industries that heavily rely on BI include:

• Healthcare: BI is used in healthcare to improve patient outcomes, manage costs, and optimize operations.
• Finance: BI is used in finance to manage risk, improve financial performance, and make data-driven investment decisions.
• Retail: BI is used in retail to analyze customer behavior, optimize inventory management, and personalize marketing.
• E-commerce: BI is used in e-commerce to track website performance, analyze customer behavior, and optimize product offerings.

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Average salaries for BI Roles

The average salary for a BI professional can vary depending on the level of experience and the specific job title. Here are some average salaries for common BI roles:

• Entry-level: The average salary for an entry-level BI analyst is around $62,000 per year, while an entry-level BI developer can earn around $76,000 per year.
• Mid-level: The average salary for a senior BI analyst is around $92,000 per year, while a BI manager can earn around $110,000 per year.
• Advanced-level: The average salary for a director of BI is around $160,000 per year, while a chief data officer can earn around $230,000 per year.

It’s important to note that salaries can vary depending on factors such as location, company size, and industry. However, BI careers offer a competitive salary and a wide range of opportunities for growth and advancement.

How to start a career path in business intelligence?

Starting a career path in business intelligence can be challenging, but with the right skills and experience, it can be a rewarding and lucrative choice. In this section, we will provide some tips for gaining relevant skills and experience, networking and job searching strategies, and resources for further learning and development.

Tips for gaining relevant skills and experience

• Internships: Internships can provide valuable hands-on experience in BI and help individuals develop relevant skills and build a network of contacts.
• Online courses: There are many online courses available that cover topics such as data analysis, programming, and BI tools and technologies. These courses can be a great way to gain new skills and demonstrate expertise to potential employers.
• Certifications: Certifications in BI tools and technologies, such as Microsoft Power BI, Tableau, or Oracle BI, can help demonstrate expertise and advance a BI career.
• Personal projects: Developing personal projects that showcase skills in BI can be a great way to demonstrate expertise and build a portfolio of work.

Networking strategies

• Attend industry events and conferences: Attending industry events and conferences can be a great way to meet professionals in the field, learn about new developments, and build a network of contacts.
• Join professional organizations: Joining professional organizations, such as the Data Warehousing Institute (TDWI) or the Association for Computing Machinery (ACM), can provide opportunities for networking and professional development.
• Job boards and LinkedIn: Job boards and LinkedIn can be great resources for finding job openings in BI. Additionally, building a strong LinkedIn profile can help individuals connect with potential employers and showcase their skills and experience.

Resources for further learning and development

• Online courses: In addition to gaining new skills, online courses can also be a great way to stay up-to-date with new developments in BI and learn about emerging trends.
• Professional organizations: Professional organizations often provide resources such as webinars, whitepapers, and research reports that can help individuals stay up-to-date with the latest trends and best practices in BI.
• Conferences and workshops: Attending conferences and workshops can provide opportunities for networking and learning from industry experts.

Final words

Business intelligence has become an essential part of modern business operations. With the right skills and qualifications, individuals can pursue a rewarding career in BI and make a significant impact on the success of their organizations. As the demand for BI professionals continues to grow, there are many opportunities available for those interested in this field.

By gaining relevant skills and experience, networking with professionals, and staying up-to-date with emerging trends and best practices, individuals can build a successful career in BI. So, whether you’re just starting out or looking to advance your career, the field of business intelligence offers a world of possibilities.

Data integration and business intelligence are two critical components of a modern data-driven organization. While both are essential to managing data and driving insights, they serve different purposes and have unique characteristics. In this article, we will examine the differences and similarities between data integration and business intelligence, explore the tools and techniques used in each field, and discuss how they can be used together to maximize their effectiveness.

Data integration vs business intelligence

The era of automation and big data has transformed the way organizations operate, and data integration and business intelligence have become critical components of this transformation. With the explosion of data, businesses need to consolidate and process vast amounts of data from various sources, including internal systems, cloud-based solutions, and third-party data sources. To achieve this, businesses need data integration tools that can help them bring data from various sources into a central repository for analysis.

The rise of automation has also increased the need for accurate and timely data. Businesses need data integration to provide data to various automated systems to run their operations effectively. For instance, manufacturing companies rely on integrated data from sensors, robots, and other equipment to optimize their production processes.

Business intelligence is also essential in that regard. With businesses having access to more data than ever before, business intelligence tools are needed to make sense of the data and turn it into actionable insights. By analyzing data from various sources, organizations can identify patterns, trends, and opportunities that can help them improve their operations, drive innovation, and gain a competitive edge.

Definition of data integration

Data integration is the process of combining data from multiple sources to provide a unified view of the data. It involves transforming and loading data into a central repository or data warehouse, where it can be easily accessed and analyzed. Data integration is a critical component of any data-driven organization, as it enables businesses to make informed decisions based on accurate and timely data.

Here are some key points to consider when discussing data integration:

• Data integration is a complex process that involves combining data from different sources.
• Data integration is often performed using ETL (Extract, Transform, Load) tools that enable businesses to extract data from disparate sources, transform it into a consistent format, and load it into a centralized data warehouse.
• Data integration can help businesses improve data quality, reduce redundancy, and streamline their data management processes.
• Popular data integration tools include Informatica PowerCenter, Talend Open Studio, and Microsoft SQL Server Integration Services (SSIS).

Definition of business intelligence

Business intelligence is a process of analyzing and interpreting data to provide valuable insights that can inform business decisions. It involves using various tools and techniques to collect, analyze, and visualize data, enabling businesses to gain a deeper understanding of their operations and make data-driven decisions.

Here are some key points to consider when discussing business intelligence:

• Business intelligence involves using data to gain insights into business operations, processes, and performance.
• Business intelligence is a multi-step process that involves data warehousing, data mining, reporting and analysis, and dashboarding and visualization.
• Business intelligence can help businesses improve their decision-making processes, identify opportunities for growth, and optimize their operations.
• Popular business intelligence tools include Tableau, Power BI, and QlikView.

Understanding data integration

In the next section, we will explore data integration in detail. We will discuss the benefits of data integration, as well as the different types of data integration and popular tools used by various organizations.

Benefits of data integration

Data integration provides numerous benefits to organizations, including:

• Improved data quality: Data integration helps ensure that data is accurate, consistent, and up-to-date, which is essential for making informed decisions.
• Reduced data redundancy: By integrating data from multiple sources, businesses can eliminate redundant data, which can save time and reduce costs.
• Increased efficiency: Data integration can help streamline data management processes, making it easier and faster to access and analyze data.
• Better analytics: With a unified view of data, businesses can perform more comprehensive and accurate analytics, enabling them to gain deeper insights into their operations.

Types of data integration

There are several types of data integration, including:

• ETL (Extract, Transform, Load): ETL is the most common type of data integration. It involves extracting data from source systems, transforming it to a common format, and loading it into a target system, such as a data warehouse.
• ELT (Extract, Load, Transform): ELT is similar to ETL, but it involves loading data into a target system before transforming it. This approach is often used when dealing with large volumes of data.
• ETLT (Extract, Transform, Load, Transform): ETLT is a hybrid approach that combines elements of ETL and ELT. It involves extracting data from source systems, transforming it, loading it into a target system, and then transforming it again.

Break down management or governance difficulties by data integration

Popular data integration tools

Here are some popular data integration tools used by businesses today:

• Informatica PowerCenter: Informatica PowerCenter is a comprehensive data integration tool that supports ETL, ELT, and ETLT. It offers a range of features, including data quality, data profiling, and metadata management.
• Talend Open Studio: Talend Open Studio is an open-source data integration tool that supports ETL and ELT. It offers over 1,000 connectors and supports real-time data integration.
• Microsoft SQL Server Integration Services (SSIS): SSIS is a data integration tool that is part of the Microsoft SQL Server suite. It supports ETL and ELT and offers a range of features, including data profiling, data cleansing, and workflow management.

Understanding business intelligence

The upcoming section will provide a comprehensive examination of business intelligence. We will delve into the explanation and advantages of business intelligence, as well as the several elements and commonly utilized tools by most firms.

Benefits of business intelligence

BI provides numerous benefits to organizations, including:

• Improved decision-making: BI provides businesses with the data and insights they need to make informed decisions, leading to improved performance and increased profits.
• Increased efficiency: BI tools automate many of the data management and analysis tasks, saving time and reducing costs.
• Enhanced customer satisfaction: BI can help businesses understand their customers better, allowing them to offer more personalized products and services.
• Competitive advantage: By leveraging BI, businesses can gain a competitive advantage by identifying new opportunities, optimizing processes, and making data-driven decisions.

Components of business intelligence

BI is composed of several key components, including:

• Data warehousing: Data warehousing is the process of collecting and storing data from various sources in a central location. This provides businesses with a single, unified view of their data, which is essential for analysis and decision-making.
• Data mining: Data mining involves extracting insights and patterns from large data sets using statistical and machine learning techniques. This process helps identify trends and relationships in the data, which can be used to make more informed decisions.
• Reporting and analysis: Reporting and analysis involve generating reports and visualizations from the data. This provides businesses with a clear and concise view of their performance and helps them identify areas for improvement.
• Dashboarding and visualization: Dashboards and visualizations provide businesses with an at-a-glance view of their performance. They are used to monitor key performance indicators (KPIs) and provide insights into business operations.

Mastering the art of efficiency through business process transformation

Popular business intelligence tools

Here are some popular business intelligence tools used by businesses today:

• Tableau: Tableau is a data visualization tool that allows businesses to create interactive dashboards and reports. It supports a wide range of data sources and offers powerful analytics capabilities.
• Power BI: Power BI is a business analytics tool that allows businesses to create reports, visualizations, and dashboards. It offers advanced data modeling and integration capabilities and is highly customizable.
• QlikView: QlikView is a business intelligence tool that allows businesses to create interactive reports and visualizations. It offers powerful data exploration and analysis capabilities and can handle large data sets.

Differences between data integration and business intelligence

Let’s delve into the differences between these terms in the upcoming section.

Purpose

The main purpose of data integration is to combine data from various sources and transform it into a usable format. Data integration is often used to support other processes such as business intelligence, data warehousing, and data migration.

Business intelligence, on the other hand, is used to analyze and make sense of data. The purpose of business intelligence is to provide insights and actionable information to decision-makers, helping them to improve organizational performance.

Scope

The scope of data integration is limited to the data integration process itself. Data integration involves the processes and tools used to combine and transform data from various sources, but it does not include the analysis of that data.

The scope of business intelligence is broader and includes the analysis of data. Business intelligence involves the processes and tools used to analyze and make sense of data, and it often includes the use of data visualization and reporting tools.

Transforming data into insightful information with BI reporting

Types of data

Data integration deals with structured and unstructured data from various sources, including databases, file systems, and cloud-based applications. Business intelligence typically deals with structured data, although it may also include unstructured data from sources such as social media and web analytics.

Role of users

Data integration is typically performed by IT professionals and data engineers who are responsible for ensuring that data is integrated and transformed correctly. Business intelligence is used by a wider range of users, including business analysts, managers, and executives, who need to analyze data and make informed decisions.

Importance in decision-making

While data integration is important for ensuring that data is integrated and transformed correctly, it does not directly impact decision-making. Business intelligence, on the other hand, is critical for decision-making. By providing insights and actionable information, business intelligence helps decision-makers to make informed decisions that can improve organizational performance.

Conclusion

In this article, we discussed the concepts of data integration and business intelligence. We explained what each of these terms means, their benefits, and popular tools and provided examples of their uses.

We also compared data integration and business intelligence and highlighted their similarities and differences. Both processes deal with data, but they have different purposes, scopes, and users. While data integration is focused on combining and transforming data, business intelligence is focused on analyzing and interpreting data to provide insights for decision-making.

Final thoughts and recommendations

Choosing the right tool for your organization’s needs is important. Consider your organization’s size, data sources, and analysis needs when selecting a tool. Data integration and business intelligence tools are often used together to support decision-making, but it is essential to understand the differences between them to select the right tool for your needs.

In conclusion, data integration and business intelligence are crucial components of any organization’s data management strategy. By selecting the right tools and leveraging the insights generated from these processes, organizations can make informed decisions that can drive business success.

Analytics as a Service allows organizations to outsource their analytics needs to specialized providers, giving them access to advanced analytics tools and expertise without the need for expensive infrastructure or dedicated staff. The era of “as a service” business models has brought about a significant shift in the way organizations approach their operations and decision-making. One of the key components of this shift is the adoption of Analytics as a Service (AaaS). With the ability to gain valuable insights, improve decision-making, and drive business growth, AaaS is becoming an essential component of modern business operations.

What is Analytics as a Service (AaaS)?

Analytics as a Service (AaaS) refers to the delivery of analytics capabilities as a service, typically over the internet, rather than as a product installed on a local computer. This can include data visualization, data mining, predictive modeling, and other analytics functions that can be accessed remotely by users through a web browser or API. The service is typically offered on a subscription basis, with customers paying for the amount of data and the level of service they need. This allows organizations to access advanced analytics capabilities without having to invest in expensive software or hardware.

Understanding Predictive Analytics as a Service

Predictive analytics is a powerful tool that can help organizations make better decisions by using data and statistical algorithms to identify patterns and predict future outcomes. Predictive analytics can be used in a wide range of industries, including healthcare, finance, and marketing. However, not all organizations have the resources or expertise to implement predictive analytics on their own. This is where predictive Analytics as a Service (PAaaS) comes in.

PAaaS is a type of Analytics as a Service that provides organizations with access to predictive analytics capabilities through the cloud. This allows organizations to leverage the expertise and resources of a third-party provider, without having to invest in expensive software or hardware. With PAaaS, organizations can gain access to advanced predictive analytics capabilities and expertise, without having to hire a dedicated data scientist or build a data science team.

PAaaS providers typically offer a variety of services, including data visualization, data mining, and machine learning. These services can be accessed remotely by users through a web browser or API, allowing organizations to easily integrate predictive analytics into their existing systems and processes.

PAaaS can be especially useful for small and medium-sized businesses that do not have the resources to invest in a dedicated data science team. However, even large organizations can benefit from using PAaaS as it allows them to scale their analytics capabilities as needed, without having to make a large upfront investment.

Analytics as a Service “as a” business model

Analytics as a Service represents a paradigm shift in the way organizations access and utilize analytical capabilities. Traditionally, organizations have had to invest significant resources in terms of time, money, and human capital to build and maintain analytical systems, which can be both costly and time-consuming. AaaS, on the other hand, enables organizations to access analytical capabilities via the cloud, through a subscription-based or pay-per-use model.

AaaS providers offer a wide range of analytical services, including data visualization, data mining, predictive modeling, and machine learning. These services are delivered through a web-based interface or Application Programming Interface (API), making it easy for organizations to integrate analytical capabilities into their existing systems and processes. This allows organizations to gain insights from their data, make informed decisions and improve their performance, without the need for significant upfront investments.

Streamlining operations with IPaaS

One of the key advantages of AaaS is that it allows organizations to be more agile and responsive to changes in the market. Since the analytical infrastructure is handled by the AaaS provider, organizations can quickly scale their analytical capabilities as needed, without having to make a large upfront investment. This is particularly beneficial for small and medium-sized businesses, who may not have the resources to invest in a dedicated data science team or analytical infrastructure.

Additionally, AaaS allows organizations to reduce their IT costs, and improve their return on investment (ROI). The AaaS provider takes care of the maintenance, upgrades, and scaling of the analytical infrastructure, which eliminates the need for organizations to invest in expensive software or hardware. Furthermore, organizations do not have to hire a dedicated data science team, which can be both costly and difficult to find.

Insights as a Service (IaaS) vs Analytics as a Service (AaaS)

Insights as a Service (IaaS) and Analytics as a Service (AaaS) are similar in that they both provide organizations with access to analytical capabilities through the cloud. However, there are some key differences between the two.

AaaS typically refers to the delivery of a wide range of analytical capabilities, including data visualization, data mining, predictive modeling, and machine learning. These capabilities are provided through a web-based interface or API, and can be accessed remotely by users. The main focus of AaaS is to provide organizations with the tools and resources they need to analyze their data and make informed decisions.

IaaS, on the other hand, is more focused on providing organizations with actionable insights from their data. IaaS providers typically use advanced analytical techniques, such as machine learning and natural language processing, to extract insights from large and complex data sets. The main focus of IaaS is to help organizations understand their data and turn it into actionable information.

How can an organization benefit from Analytics as a Service?

Analytics as a Service is revolutionizing the way organizations approach their data and decision-making. By outsourcing their analytics needs to specialized providers, organizations can access advanced analytics tools and expertise without the need for expensive infrastructure or dedicated staff. Let’s get to know its benefits:

• Cost-effective: Analytics as a Service eliminates the need for expensive infrastructure and software, as well as the cost of hiring and training dedicated analytics staff.
• Scalability: With AaaS, organizations can scale their analytics capabilities up or down as needed, to match the changing needs and priorities of the business.
• Access to expertise: AaaS providers have teams of experienced data scientists and analysts who can help organizations make sense of their data and extract valuable insights.
• Flexibility: AaaS solutions can be customized to meet the specific needs of an organization, providing more flexibility than off-the-shelf software.
• Speed: AaaS solutions can be implemented quickly, allowing organizations to start gaining insights and making data-driven decisions in a short period of time.
• Security: AaaS providers are often responsible for ensuring the security of data and infrastructure, allowing organizations to focus on their core business.
• Improved decision-making: Analytics as a Service enables organizations to make data-driven decisions, improving the accuracy of predictions and allowing for more effective decision-making.
• Increased efficiency: Automated analytics solutions can process large amounts of data quickly and accurately, increasing the efficiency of business operations.

Exploring the strong growth of BaaS in the fintech sector

What are the challenges of implementing Analytics as a Service to an organization?

Unlocking the full potential of your data with Analytics as a Service does not come without challenges. From data integration and security to skills and expertise, organizations must navigate a complex landscape to ensure a successful implementation. Our team of experts can help you overcome these challenges and unlock the value of your data.”

• Data integration: Integrating data from different sources can be a complex and time-consuming task.
• Security: Ensuring the security and privacy of sensitive data is a major concern for organizations.
• Lack of skills and expertise: Organizations may not have the necessary skills and expertise to implement and maintain analytics solutions.
• Organizational culture: Changing the organizational culture to one that is data-driven can be difficult.
• Technical complexity: the complexity of technical systems and architecture may pose a challenge for organizations.
• Data governance: Ensuring data quality and consistency can be difficult, especially when dealing with large data sets.
• Cost: The cost of implementing and maintaining an analytics solution can be high.

What’s the market size of Analytics as a Service?

The market size for Analytics as a Service has been growing rapidly in recent years, and is expected to continue to do so in the future. According to a research from MarketsandMarkets, the global AaaS market size is expected to grow from USD 8.5 billion in 2019 to USD 20.5 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 18.9% during the forecast period. The growth of this market is driven by the increasing adoption of cloud-based analytics solutions, the growing need for advanced analytics in various industries, and the increasing awareness about the benefits of AaaS.

Best Analytics as a Service examples

In this list, we will highlight some of the best AaaS providers currently available, providing an overview of their capabilities. These providers are capable of handling a wide range of data analysis needs, from basic reporting to advanced machine learning and predictive analytics. Whether you’re looking for a basic tool to help you understand your data or a more advanced solution to drive your business forward, there’s an AaaS provider on this list that can help.

Amazon Web Services (AWS)

Amazon Web Services (AWS) offers a variety of analytics services, including Amazon QuickSight for data visualization, Amazon Redshift for data warehousing, and Amazon Machine Learning for predictive analytics.

IBM Watson Studio

IBM’s Watson Studio offers a cloud-based platform for data scientists and developers to build, train, and deploy machine learning models.

Google Analytics 360

Google Analytics 360 is a web analytics service that allows businesses to track and analyze data from their websites, mobile apps, and other digital properties.

Microsoft Azure

Microsoft Azure offers a range of analytics services, including Power BI for data visualization, Azure Machine Learning for predictive analytics, and Azure Stream Analytics for real-time data processing.

Tableau Online

Tableau Online is a cloud-based data visualization and reporting service that allows users to create interactive dashboards and reports.

SAP Analytics Cloud

SAP Analytics Cloud is a cloud-based analytics platform that enables businesses to access and analyze data from multiple sources, create visualizations, and perform predictive analytics.

Looker

Looker is a cloud-based data platform that allows users to explore and visualize data, create customized dashboards, and build data applications.

Alteryx

Alteryx is a cloud-based data analytics platform that enables users to blend, analyze, and share data using a drag-and-drop interface.

Boomi

Dell Technologies provides analytics services through its Boomi platform which allows customers to blend, cleanse, and normalize data from various sources.

Salesforce Einstein Analytics

Salesforce Einstein Analytics is a cloud-based analytics platform that allows businesses to gain insights from their Salesforce data and other data sources.

Conclusion

In the era of “as a service” business models, Analytics as a Service is playing an increasingly important role in helping organizations gain a competitive advantage. AaaS allows organizations to outsource their analytics needs to specialized providers, giving them access to advanced analytics tools and expertise without the need for expensive infrastructure or dedicated staff.

By providing organizations with the ability to gain valuable insights, improve decision-making, and drive business growth, AaaS is becoming an essential component of modern business operations. As data continues to drive business decisions, organizations that fail to adopt AaaS risk falling behind their competitors. The ability to access and analyze data quickly, accurately and cost-effectively is the key to unlocking the full potential of business intelligence and making data-driven decisions.

Business intelligence reporting is a critical component of any organization’s operations. It entails collecting, analyzing, and presenting data to support informed decision-making. In today’s fast-paced business environment, it is imperative for organizations to have access to accurate and relevant data to make strategic decisions that drive growth and success. Business intelligence reporting tools enable organizations to gather and analyze data from various sources and present it in a meaningful and actionable manner.

What is business intelligence reporting?

Business intelligence reporting refers to the process of collecting, analyzing, and presenting data to support informed decision-making in an organization. This often includes creating and delivering reports, dashboards, and other visualizations that help managers and other stakeholders understand key performance indicators, trends, and other important business data. Business intelligence reporting can be used to support a wide range of business activities, from financial analysis and budgeting to marketing and customer relationship management.

Standard reports in business intelligence

Standard reports in business intelligence are predefined, regularly-used reports that provide key performance indicators (KPIs) and other important data to help managers and other stakeholders make informed decisions. These reports are typically created using business intelligence software and can be run on a regular schedule, such as daily, weekly, or monthly.

What are the different types of business intelligence reports?

Business intelligence reporting encompasses a diverse set of applications in BI, including everything from traditional reports to interactive dashboards and embedded analytics. When planning your BI strategy, it’s important to take into account the bigger picture and anticipate any future needs. For instance, you may initially only require static reports, but later on, you may need alerts for certain key performance indicators, which would necessitate real-time dashboards. Additionally, dashboards may lead to the requirement for self-serve BI, enabling any user to easily access and explore data to quickly answer their own questions.

Some examples of business intelligence reporting are mentioned below:

• Sales reports: These reports provide data on sales performance, such as revenue, unit sales, and gross margin, by product, region, or other dimensions.
• Financial reports: These reports provide data on financial performance, such as income statements, balance sheets, and cash flow statements.
• Inventory reports: These reports provide data on inventory levels, such as stock-on-hand, stock turnover, and reorder points.
• Customer reports: These reports provide data on customer behavior, such as demographics, purchase history, and lifetime value.
• Production reports: These reports provide data on production performance, such as output, efficiency, and downtime.
• Employee reports: These reports provide data on employee performance, such as headcount, turnover, and productivity.
• Marketing reports: These reports provide data on the effectiveness of marketing campaigns, such as lead generation, conversion rates, and return on investment.
• Supply chain reports: These reports provide data on supply chain performance, such as supplier performance, delivery times, and inventory levels.

Standard reports are typically created with the goal of providing easy and quick access to the relevant data in order to support the decision-making process.

What are the benefits of business intelligence reporting?

Business intelligence reporting can provide a variety of benefits to organizations.

Improved decision making

By providing easy access to accurate, up-to-date data, business intelligence reporting can help managers and other stakeholders make more informed decisions. This can lead to better business performance and improved competitiveness.

Greater efficiency

Business intelligence reporting can automate many routine data-gathering and analysis tasks, allowing employees to focus on more strategic activities. This can lead to increased productivity and cost savings.

Enhanced visibility

Business intelligence reporting can provide organizations with a comprehensive view of their performance across different departments and business units. This can help identify areas of strength and weakness and support targeted improvement efforts.

Transforming industries and enhancing the CX with visual AI

Better customer service

Business intelligence reporting can be used to analyze customer data and behavior, helping organizations identify key trends, preferences, and pain points. This can support the development of more effective customer service and support strategies.

Better forecasting

Business intelligence reporting can provide data that can be used for forecasting, which can help organizations make better decisions on budgeting and resource allocation.

Improved compliance

Business intelligence reporting can help organizations stay compliant with regulations and reporting requirements by providing accurate and timely data.

Strategic planning

Business intelligence reporting can help organizations identify long-term trends and patterns in their data, which can inform strategic planning and decision-making.

How to create a quality business intelligence report?

Creating a quality business intelligence report typically involves several steps, including:

Step 1: Define the report’s purpose and audience

Before beginning to create a report, it’s important to understand its purpose and who will be reading it. This will help to ensure that the report is tailored to the specific needs of its audience and that it addresses the right questions.

Step 2: Gather and clean the data

Collecting and preparing the data that will be used in the report is an important step. This may involve pulling data from various sources, such as databases, spreadsheets, and external systems, and then cleaning and organizing it to ensure that it is accurate and complete.

Step 3: Analyze the data

Once the data has been prepared, it can be analyzed to identify key trends, patterns, and insights. This may involve using various statistical techniques, such as descriptive statistics, correlation analysis, and regression analysis, to understand the data.

Step 4: Create the report

Once the data has been analyzed, it can be used to create the report. This may involve creating charts, tables, and other visualizations to help convey the information clearly and effectively. It’s important to choose the right visualization format that fits the data and message to be conveyed.

Step 4: Review and distribute the report

Before distributing the report, it’s important to review it to ensure that it is accurate and that the information is presented clearly. This can be done internally or with a third-party review. Once the report has been reviewed, it can be distributed to the intended audience, either in print or electronic format.

Step 5: Monitor and update the report

Business intelligence is not a one-time effort, it is a continuous process. Therefore, it’s important to set up a schedule to monitor the report, and make updates as needed.

In addition to these steps, it’s important to consider the design, formatting, and overall layout of the report, as well as the use of clear and concise language. This can help to ensure that the report is easy to understand and that the information is presented in a way that is meaningful to its intended audience.

BIDW: What makes business intelligence and data warehouses inseparable?

BI reporting tools list

Below, you can find some of the top business intelligence reporting tools available in the market; each of them offers unique features and serves specific purposes to help organizations make informed decisions:

Power BI reporting

Power BI, a product from Microsoft, is a business intelligence tool that allows you to visualize your data and share insights. It can take data from various sources and transform it into interactive dashboards, and BI reports.

The key features of Power BI reporting:

• Advanced analytics and data management: Power BI provides advanced analytics capabilities that help users gain valuable insights and transform data into powerful components that can provide new ideas and solutions to business problems.
• Quick Insights: With the help of powerful algorithms, users can quickly identify insights from various subsets of data. Quick Insights makes it easy for users to access analytics results with just a single click.
• Ask a question: Power BI allows users to ask questions and get instant visual responses in the form of charts, tables, or graphs.
• Relevant reports: Power BI reports are well-organized collections of dashboards that provide relevant visualizations and formats for specific business issues and allow users to share them with others.
• Integration with Azure machine learning: Power BI includes the integration of Azure Machine Learning, which enables users to visualize the outcomes of Machine Learning algorithms by easily dragging, dropping, and joining data modules.

Sisense BI reporting

Sisense is considered one of the leading solutions in the business intelligence market, having won the Best Business Intelligence Software Award from FinancesOnline. It offers an all-inclusive BI reporting tool, which is a self-service analytics and reporting platform that enables anyone to quickly create interactive dashboards and powerful reports. Even those without technical expertise can easily generate robust visual reports without needing to spend extra time on preparation.

The key features of Sisense BI reporting:

• Reports that are fully interactive and self-updating
• Reports and dashboards that are fed by real-time data.
• Effortless building and sharing of dashboards
• Ability to create reports from multiple data sources such as Excel, text/CSV files, any database (SQL Server, Oracle, MySQL), and cloud sources.
• Reports with visually appealing and informative visualizations
• Compatibility with any mobile or desktop device.

Oracle BI reporting

Oracle’s business intelligence solution offers a comprehensive set of features for intelligence, analytics, and reporting. It is designed to handle heavy workloads and complex deployments, making it suitable for organizations of all types. With Oracle BI, users can explore and organize their business data from a wide range of sources, and the system will create visualizations to display the data in a user-friendly format. This allows users to easily gain insights from charts, graphs, and other graphics, which in turn helps them make data-driven decisions for their organization.

The key features of Oracle BI reporting:

• Oracle BI offers a wide range of interactive data visualization tools such as dashboards, charts, graphs, and other tools. Users can filter, drill down, or pivot data directly from the dashboard, and the system provides prompts and suggestions to guide the exploration process and uncover additional insights.
• Oracle Exalytics allows users to analyze large datasets efficiently without the need for technical professionals such as data analysts.
• The system is designed to be self-service, allowing non-technical users to explore, organize, and interpret data through intuitive visualizations that are easy to understand and share with employees at any level of data literacy.
• Oracle BI provides actionable intelligence by analyzing data and identifying trends, which helps users make informed decisions about business practices, quotas, forecasting, and more.
• Users can set up predefined alerts that send real-time updates when triggered or scheduled, which can be sent via preferred channels, including email, internal file storage, and text message based on the severity of the alert.
• The solution is designed for mobile access with a consistent interface, including multitouch and gestural interactions with map graphics and other advanced features.

SAP Crystal Reports

SAP Crystal Reports is a widely-used business intelligence reporting software solution that is known for its ability to accelerate decision-making quickly. It is designed to make presenting data through reports simple and easy. The software offers flexibility and customization options for various reports and presentations that can be generated on a daily basis and can be accessed by multiple users. Additionally, it can produce reports based on information from almost any data source.

The key features of SAP Crystal Reports:

• SAP Crystal Reports enables users to create highly formatted reports quickly and easily.
• It enables efficient content delivery throughout large organizations.
• It allows users to create multilingual reports.
• It provides an interactive report exploration feature.
• It offers the ability to access parameter values without the need to refresh data.
• It allows users to create direct connectivity to data sources without data modeling.

Final words

Business intelligence reporting is an essential aspect of any organization’s operations. Access to accurate and timely data is crucial for making strategic decisions that drive growth and success. Business intelligence reporting tools provide organizations with the capability to gather and analyze data from various sources and present it in a meaningful and actionable manner. These tools are invaluable for organizations looking to stay ahead of the curve and make data-driven decisions.

What is the impact of artificial intelligence in insurance? Well, there are a lot of use cases for artificial intelligence in everyday life, but what about AI in insurance? The effects of artificial intelligence in business heavily include insurance.

Are you scared of AI jargon? We have already created a detailed AI glossary for the most commonly used artificial intelligence terms and explained the basics of artificial intelligence as well as the risks and benefits of artificial intelligence for organizations and others. So, it’s time to explore the role of artificial intelligence in insurance sector.

Impact of artificial intelligence in insurance industry

One of the most revolutionary advances has been the use of AI in insurance, which has been hailed as having significant economic and societal advantages that eventually boost risk pooling and improve risk reduction, mitigation, and prevention.

Automation enables insurance businesses to quickly respond to requests and guarantee that the customers they pledge to serve will receive high-quality service.

“Machines have instructions; we have a purpose. We will need intelligent machines to help us turn our grandest dreams into reality.”

Garry Kasparov

Is Kasparov right? Absolutely. However, the insurance industry has not adapted to AI technologies despite their many benefits. The traditional insurance industry has generally been hesitant to adapt to new technologies. According to a Deloitte survey, while practically every business has found success with AI or has begun investing in it, the insurance sector appears to be far behind, with only 1.33% of insurance companies investing in AI compared to 32% in software and internet technologies.

The good news is that whoever adopts AI early in the insurance sector will be a pioneer and receive the largest piece of the pie.

The environment is currently evolving quickly with the emergence of InsureTech startups and technological incumbents. In addition to requiring less money and resources, they can provide on-demand plans, more transparent pricing, and quicker claim payments.

What is the InsurTech industry?

The term “InsurTech ” describes technical advancements developed and used to increase the effectiveness of the insurance sector. The invention, distribution, and management of the insurance industry are all supported by InsurTech.

The shifting dynamics create global prospects for the AI-enabled insurance sector. So, let’s see the benefits of artificial intelligence in insurance and explore the taste of “the pie.”

Benefits of artificial intelligence in insurance

These are some of the best benefits of artificial intelligence in insurance:

• Expanded data and insight access
• The right information at the right moment to the right people
• Consistent performance from employees
• Better, quicker decisions are driven by data

Let’s take a closer look at the advantages of artificial intelligence in insurance and find out how Artificial intelligence is helping the underwriting process in insurance.

Expanded data and insight access

Building a better, more precise data foundation is a prerequisite for integrating AI into a workflow, and doing so benefits people even before AI is used.

Consider a worker attempting to ascertain whether some clients are spending too much time in the service center, particularly if they have a low estimated lifetime value. The underwriter receives a forecasted lifetime value score and can use it to inform a better price decision thanks to access to customer journey information and insights.

After AI is implemented, any previous activities can be sent to the machine-learning model and the customer’s information. By targeting the most profitable customers and avoiding those who are most likely to be unprofitable, the sales and marketing teams can improve future results.

The right information at the right moment to the right people

A submission forwarded to underwriting is first evaluated in real-time using predictive models for criteria including “broker sincerity” and “projected loss ratio for this class.” To help with issues like “Which risk should I work on next that will be most advantageous for our company?” AI can then develop a scoring system for those inputs.

Given the insights provided, the underwriter can choose the optimal course of action by digitizing the underwriting process with AI. In this instance, AI aids in bridging the gap between the employee’s action based on the recommendation made by the AI engine and the information gained.

Consistent performance from employees

Decisions become more accurate, correct, and consistent thanks to AI’s elimination of a large portion of the guesswork involved in decision-making.

While training is still essential, applying AI enables less experienced employees to pick up new skills much faster because they receive recommendations based on decisions that have already been proven to be correct. This reduces a lot of the risk that comes with hiring a new employee.

Check out how is artificial intelligence changing the recruiting process

An insurance claims adjuster with less expertise might overcompensate a client for a claim. In contrast, an adjuster empowered by AI can be directed through suggested next actions based on prior experiences, all within the same analytics system.

Better, quicker decisions are driven by data

Think about an insurance provider attempting to prevent fraud. Unlike humans, AI can read and depend on vast amounts of historical data based on false claims.

As a result, future fraud is caught considerably more quickly and precisely. This also helps the AI swiftly enhance its grasp of typical fraud behaviors. Much more than a human counterpart could ever calculate or act upon.

Because of these benefits, there are a lot of use cases of artificial intelligence in insurance.

Check out how big data is changing the insurance industry

AI in insurance use cases

AI is increasingly important in the insurance industry, from claims processing to compliance to risk reduction and damage analysis. These are some of the best AI in insurance use cases:

• Claims processing
• Claims fraud detection
• Claims adjudication
• Automated underwriting
• Submission intake
• Pricing and risk management
• Policy servicing
• Insurance distribution
• Product recommendation
• Property damage analysis
• Automated inspections
• Customer lifetime value prediction
• Speech analytics
• Customer segmentation
• Workstream balancing for agents
• Self-servicing for policy management
• Claim volume forecasting

How is technology changing the insurance industry? How does AI & ml enable insurers to tackle current challenges? Let’s explore artificial intelligence in insurance use cases and find out!

Claims processing

In order to comply with policy and regulatory requirements, insurers must make sure that claims are valid throughout the whole process cycle.

Handling thousands of claims and client inquiries is a laborious task that takes time. The entire procedure is effective and efficient, thanks to machine learning. Moving claims through the first report, analysis, and contacting the consumers significantly increases the value chain of claims processes.

Check out the 15 real-life examples of machine learning

Employees could concentrate on more complicated claims and one-on-one customer interactions because of the time savings.

Claims fraud detection

According to research by the Federal Bureau of Investigation on US insurance firms, the total cost of insurance fraud (non-health insurance) is nearly $40 billion annually.

In terms of higher premiums, insurance fraud costs the typical US household $400 to $700 annually. These shocking figures highlight the critical need for precise automated theft detection solutions to enable insurance companies to improve their due diligence procedure.

Claims adjudication

According to the Council for Affordable Quality (CAQH) Index research, automating eligibility and claim verification can save the healthcare insurance industry alone $ 5.2 billion annually. With a chatbot that communicates with consumers and gathers the necessary data, the claim initiation automation process helps insurers save time.

A first-level validation can be done throughout the claim start process using chatbots to capture information in a structured way. According to a World Economic Forum (WEF) report, computers will be used to carry out 62% of an organization’s data processing and storage tasks by 2022. Due to the expanding automation industry, investing in auto-adjudication systems will help firms stay relevant shortly.

Automated underwriting

Do you know a better love story than AI in insurance underwriting? In the past, insurance underwriting relied mainly on employees to examine historical data and come to wise conclusions. Working with chaotic systems, procedures, and workflows was another challenge as they attempted to reduce risks and provide customer value. Intelligent process automation simplifies the underwriting process by offering Machine Learning algorithms that gather and make sense of enormous volumes of data. It is one of the most used artificial intelligence in insurance use cases.

Additionally, it enhances the performance of rules, controls straight-through acceptance (STA) rates, and guards against application mistakes. Underwriters can concentrate only on complex instances that may need manual attention as most of the procedure has been automated.

Submission intake

When combined with AI and NLP, automation can extract data from structured and unstructured sources, including brokers’ emails, spreadsheets, loss runs, and ACORD forms, facilitating effective teamwork and accelerating and improving risk assessment.

Additionally, automation makes managing various submission queues for new businesses, renewals, and endorsements easier. Machine learning models quickly sift through hundreds of submissions and rank the best entries following the underwriting triage criteria and risk appetite.

Pricing and risk management

Price optimization uses data analytic techniques to determine an organization’s ideal rates while considering its objectives. It is one of the best artificial intelligence in insurance use cases.

It analyzes how customers respond to various pricing strategies for goods and services. GLMs (Generalized Linear Models) are mostly used by insurance companies to optimize prices in industries like auto and life insurance. With this method, insurance businesses may better understand their clients, balance supply and demand, and increase conversion rates.

Automation of risk assessment also improves operational efficiency. Risk assessment automation increases efficiency by fusing RPA with machine learning and cognitive technologies to build intelligent operations. Insurance companies can provide a better client experience and lower turnover because the automated procedure takes much less time.

Check out cyber risk assessments examples

Policy servicing

The policy administration system can be integrated to get information about each policy thanks to the automated intake of policy data. This lessens the manual search and location effort needed to discover the pertinent fields for policy endorsements.

Additionally, it enables parallel processing to handle complex circumstances where many requests are made by different clients, which reduces the turnaround time for processing and servicing insurance policies. RPA in the insurance industry helps to efficiently complete various tasks without requiring extensive system navigation. It automates administrative and transactional tasks like accounting, settlements, risk capture, credit control, tax preparation, and regulatory compliance.

Insurance distribution

In the pre-digital era, insurance customers might visit a local carrier or contact a financial adviser to learn about coverage possibilities. In a specialized market, there would often be a leading carrier for a certain product. The carrier would carry out underwriting tasks and share a quote based on the customer’s submitted data. Digitalized insurance distribution methods flipped this scenario.

Today, almost all carriers have an online site where customers may browse their selection of products and services before making a choice. This change in consumer behavior brought on a significant disruption in the insurance industry. Beyond underwriting and claims clearance, AI has the ability to revolutionize the sales and distribution stage of the insurance value chain by utilizing cutting-edge AI algorithms that are now on the market.

Insurance companies can benefit from a customer’s digital behavior by using digital technologies like optical character recognition (OCR), machine learning (ML), and natural language processing (NLP).

Product recommendation

Each day, the insurance industry produces a large amount of transaction data. Automation can help businesses in this situation accurately and effectively propose insurance products to customers, increasing the insurance company’s ability to compete.

Price optimization uses data analytic techniques to determine an organization’s ideal rates while considering its objectives. It is one of the most common artificial intelligence in insurance use cases.

Property damage analysis

The first step in any damage insurance claim process, whether it involves a mobile phone, a car, or a piece of property, is inspection.

With physical intervention, estimating the damages to determine repair costs is difficult for insurance companies. Data analysis and AI-powered object detection compare the level of damage before and after the occurrence. Machine learning algorithms can identify broken auto parts and provide repair cost estimates.

Automated inspections

Motor insurance claim assessment has historically been handled manually by surveyors and claim adjusters. Manual inspection is expensive because it necessitates the adjuster or surveyor to contact the policyholder. Each examination costs between $50 to $200. The processing of claims would also take longer because report generation and estimation typically take one to seven days.

Insurance firms can examine car damage with AI-based image processing. The system then produces a thorough assessment report explaining the car parts that can be repaired and replaced and their approximate costs. Insurance companies can lower claim estimation expenses and improve the procedure’s effectiveness. Additionally, it populates reliable data to determine the final settlement sum.

Customer lifetime value prediction

One of the most important technologies that allow businesses to forecast client lifetime value using machine learning is the customer lifetime value (LTV).

According to research by Bain & Co., an improvement in retention of 5% can result in a profit increase of 25% to 95% for a business. A customer’s purchasing history is compared to a huge product inventory by machine learning algorithms to uncover hidden patterns and group products that are similar. It is one of the most important artificial intelligence in insurance use cases.

Customers are then given access to these products, eventually promoting product purchases. Insurance companies can strike the ideal balance between customer acquisition and retention by knowing the lifetime worth of each customer.

Speech analytics

Speech recognition is a potent tool for lead call analysis based on customer speech to enhance the personalization. It can detect fraud based on voice analysis of customer calls to increase security measures and identify customer pain points with products using speech analytics of comments to improve future products.

Do you know artificial intelligence customer services are on the rise?

Customer segmentation

The first step in developing customization is customer segmentation. It improves consumer happiness, product design, marketing, and budgeting. It is one of the most common artificial intelligence in insurance use cases.

Machine learning algorithms examine customer data to uncover trends and insights. Tools with AI assistance accurately identify client categories that are difficult to complete manually or use traditional analytical techniques.

Workstream balancing for agents

Utilizing AI-assisted models that give them access to consumers and enable them to grow their businesses is becoming increasingly popular among insurance agents.

AI will undoubtedly be the cornerstone for increasing consumer happiness and, in turn, expanding the reach of insurance brokers because simplicity is its defining characteristic.

Self-servicing for policy management

Self-service business intelligence (BI) is a data analytics platform that enables users to access, examine, and analyze data sets without prior knowledge of BI, data mining, or statistical analysis.

Self-service BI technologies allow users to filter, organize, analyze, and visualize data without the help of BI and IT teams in a company. These tools make it simpler for staff members to gain insightful business knowledge from the data gathered in BI systems. Ultimately, this strategy promotes more informed decision-making, which raises revenues, boosts productivity, and improves client happiness.

Check out the role of artificial intelligence in information systems

Claim volume forecasting

Setting the premium at the start of the insurance contract is fundamental to insurance practice. A precise and reliable assessment of the number of claims occurrences and the total claim amounts is crucial to arriving at an insurance company’s precise premium for the upcoming year. It is one of the most critical artificial intelligence in insurance use cases.

The forecasting for individual claims is faster and more accurate, thanks to machine learning. This enhances the effectiveness of an insurer’s pricing.

Check out how is artificial intelligence used in the military

Insurance companies using artificial intelligence (Top 5)

What insurance companies are using AI? Insurance companies are utilizing artificial intelligence to create customized plans, automate the underwriting process, and give customers worldwide more precise estimates. These are some of the best insurance companies using artificial intelligence:

• Liberty Mutual Insurance
• CCC Intelligent Solutions
• Insurify
• Clearcover
• Bold Penguin

Check out these Insurance companies using artificial intelligence to learn more about how AI affects the insurance sector.

Liberty Mutual Insurance

Through the Solaria Labs program, Liberty Mutual investigates AI in fields including computer vision and natural language processing. One outcome of their efforts is the Auto Damage Estimator. This AI solution uses comparative studies of anonymous claims images to swiftly evaluate vehicle damage and offer repair estimates after an accident. It is one of the firms that used artificial intelligence in insurance.

CCC Intelligent Solutions

Artificial intelligence is used by CCC Intelligent Solutions to digitize and automate the whole claims process. Photos taken at accident scenes are analyzed using AI and guidelines agreed by the insurance. Based on this information, CCC’s AI can determine the extent of the damage and promptly offer estimates that insurers can accept and forward to their clients for confirmation.

Insurify

Utilizing artificial intelligence, Insurify instantly connects clients with auto and home insurance providers that meet their individual requirements. The business uses RateRank algorithms to identify the insurance that would suit each client, taking into account details like location and desired discount level.

Clearcover

Clearcover uses artificial intelligence to process claims and insure users quickly. Users of Clearcover can receive AI-generated quotations and select the one that best suits their needs after completing a brief questionnaire. Users only need to take a few images and complete a brief form if they are ever in an accident before ClearAI jumpstarts the claims procedure.

Bold Penguin

With two AI-powered tools, SubmissionLink and ClauseLink, Bold Penguin enables insurance businesses to produce policies that stand out in the sector swiftly. SubmissionLink examines documents that carriers receive from authorities and identifies crucial information for underwriters. While this is going on, ClauseLink examines insurance provisions to assist providers in comparing their plans to those of rivals.

AI in insurance market size

With a predicted CAGR of 32.56% from 2022 to 2031, the global AI in the insurance market, valued at $2.74 billion in 2021, is expected to increase to $45.74 billion by 2031, according to AlliedMarketResearch.

The global AI in the insurance market is expanding due to an increase in investment by insurance companies in AI and machine learning, as well as a rise in demand for personalized insurance services.

Check out how data science helps insurance companies

Conclusion

AI will drive the future of insurance. Utilizing various AI techniques will quickly automate insurance processing, from claim submission to payment, without human involvement. Saving this money and effort will enable the insurance sector to develop better product categories and customized premium rates based on information gathered from multiple sources.

A wave of homogeneity across various market sectors, industrial verticals, and service providers is brought forth by AI. As a result, procedures for getting insurance and handling claims can be more consistently standardized.

Greater operational excellence, lower costs, and improved client experiences are other advantages that we can anticipate. It is clear that AI-driven insurance has a bright future, and the use of AI in the insurance sector will significantly increase in the years to come.

Is artificial intelligence better than human intelligence? Explore the cons of artificial intelligence before you decide whether artificial intelligence in insurance is good or bad.

What is the difference between business intelligence and data analytics? Business intelligence (BI) and data analytics are frequently used interchangeably in data-driven enterprises. Though they aren’t the same, it is hard to clarify the difference. Do you know how you would answer if someone asked you to describe the distinction? Do not worry; you will learn it in this article.

People are now aware of the effects of business analytics and business intelligence solutions in retail. Moreover, they are not simply restricted to retail; business intelligence and data analytics are the biggest forces in the modern era. But for better outcomes, you should get familiar with their distinctions. Let’s first define them before examining how they differ from one another.

What is business intelligence?

Business intelligence (BI) uses software and services to convert data into useful insights that influence a company’s strategic and tactical business choices. To give users in-depth insight into the condition of the business, BI tools access and analyze data sets and show analytical findings in reports, summaries, dashboards, graphs, charts, and maps.

Are you wondering why business intelligence is a must in modern business? Well, in the data-driven era, understanding analytics is everything, and business intelligence reporting ensures that. The best business intelligence companies provide the best results.

Spreadsheets have been completely phased out in the modern business intelligence space. BI instead utilizes new technologies like SQL databases, cloud platforms, and machine learning to assist organizations in making more self-aware, evidence-based choices.

Does business intelligence require coding?

Coding is necessary for business intelligence (BI) to process data and generate insightful findings. The data modeling and warehousing phases of the BI project lifecycle involve coding. However, the other phases of the BI lifecycle do not necessitate coding. Anyone who has some programming experience can begin a career in BI.

The difference between business intelligence and business analytics

The emphasis on the timing of events is the main distinction between business intelligence and business analytics. Business intelligence focuses on the data’s representation of recent and historical events. The focus of business analytics is on future events that are most likely to occur.

Business analyst vs business intelligence salary

Compared to business analysts, business intelligence analysts make greater money. Payscale claims that whereas business analysts make $70,644, BI analysts make USD $71,050 annually.

What is data analytics?

The study of examining unprocessed data to draw inferences about such information is known as data analytics. Many data analytics methods and procedures have been mechanized into mechanical procedures and algorithms that operate on raw data for human consumption.

The phrase “data analytics” is broad and covers many data analysis techniques. Data analytics techniques can be applied to any type of information to gain insight that can be utilized to make things better. Techniques for data analytics can make trends and indicators visible that might otherwise be lost in the sea of data. The efficiency of a firm or system can then be improved by using this knowledge to optimize procedures.

Check out the top data analytics trends

Data intelligence vs data analytics

In order to ascertain what transpired in the past and why, data intelligence gathers and examines information on actions, events, and other information. Data science and analytics approaches are used with this same data to forecast what will happen in the future, and business decisions are made based on that data.

Does data analytics require coding?

Advanced coding knowledge is not necessary for data analysts. They should have knowledge of data management, visualization, and analytics software instead. Data analysts need to have strong mathematics skills, like most data-related occupations.

Which language is used for data analytics?

Python and SQL are the most often used programming languages for data analytics. Some analysts may utilize R for numerical analysis, computation, and analysis. However, coding is not the main difference. So, what is it?

Difference between business intelligence and data analytics

A business intelligence analyst finds business-focused insights through data, unlike a data analyst who exclusively uses analytics to find solutions to problems. Except for the tools employed, which may differ slightly, the definitions, procedures, types of data, and analyses for the two jobs are comparatively identical.

Let’s see all differences between business intelligence and data analytics:

Data analyst vs business intelligence analyst

Let’s see what is the difference between a data analyst and a business intelligence analyst:

Data analyst vs business intelligence salary

Who gets paid more business analyst or data analyst? Data analysis and business analysis entail in-demand abilities that frequently command significant pay. Business analysts in the US will get an average base salary of $77,218 in 2021, while data analysts will earn an average base salary of $69,517, according to Coursera.

Which is better data analyst or business intelligence?

Both business analysts and data analysts support data-driven decision-making in their respective enterprises. Business analysts are more likely to address business problems and suggest solutions, whereas data analysts typically work more directly with the data itself. Both positions are in high demand and often pay high.

Conclusion

We have now examined the history and significant distinctions between business intelligence and data analytics. Business intelligence and data analytic tool development have evolved in light of the current technology market trends.

The ability to perform data analytics is a feature of modern business intelligence tools, and it is up to enterprise customers to decide which solution is best for their particular company needs.

According to the most recent data trends, both business intelligence and data analytics are crucial to the expansion of the company. To assist them to perform their function effectively, the organization is conducting the necessary study on both BI and data analytics.

Are you looking for the best business analytics and business intelligence solutions in retail? Well, it is not surprising. According to Fortune Business Insights, the retail business intelligence market is anticipated to grow at a CAGR of 17.7% from 2018 to 2028, reaching USD 18.33 billion.

The retail sector has historically been slower than other sectors to adopt new technologies, and this trend continues with the adoption of BI technology. BI software for financial reporting and consolidation, customer intelligence, regulatory compliance, and risk management has advanced significantly in several areas, such as financial services. Retailers are, however, catching up swiftly and starting to understand the various BI applications for their particular industries.

What is retail business intelligence?

A retail business intelligence (BI) system is used to gather, process, and analyze data about the retail industry to deliver pertinent insights to:

• Improve client retention and customer satisfaction.
• Plan and optimize your assortment.
• Marketing campaign planning.
• Find new prospects for sales, etc.

Considering retail firms manage a tremendous amount of data, from supplier data to customer buying behavior, employee information to inventory details, every interaction and data point offers a possibility to make your retail business more successful and lucrative.

Retailers can employ a variety of various solutions to enhance their BI. Business analytics and business intelligence solutions in retail consist of:

• Data mining
• Text analytics
• Predictive analytics
• Prescriptive analytics
• Dashboards
• Visualizations

Business analytics and business intelligence solutions in retail need crucial integrations. You require necessary integrations to comprehend how clients act, how different sales channels perform, and whether marketing efforts are successful:

• Customer relationship management (CRM) software
• Ecommerce platform
• Point of sale system
• Customer service software
• Marketing campaign management software
• Inventory management software
• Supplier management software

After integrating these systems into your ecosystem, the benefits of business analytics and business intelligence solutions in retail will appear.

Check out the effect of machine learning in retail

Benefits of business analytics and business intelligence solutions in retail

What are the benefits of Business analytics and business intelligence solutions in retail? These are some of the best benefits of intelligent systems in retail:

• Find emerging trends
• Improve business operations
• Identify customer locations
• Enhance efficiency in the supply chain
• A better understanding of consumer demands
• Improve merchandising
• Improve inventory management
• Optimize store floor plans
• Competitor tracking in social media
• Informed decision making
• More effective marketing

Let’s take a closer look at the advantages of business analytics and business intelligence solutions in retail. How analytics is helpful for a retail business?

Finding emerging trends

BI will become even more crucial in recognizing new and developing customer patterns, enabling organizations to adjust without facing significant challenges.

Retailers can use business intelligence to find industry-specific trends that need to be considered.

Improve business operations

Retail companies can better manage their operations by using BI solutions. It aids in their monitoring of company activities. This enables quick repairs to be made in the event of mistakes.

For example, a retail business can utilize the BI tool to handle late deliveries and determine their cause. The company’s operations might be greatly improved with this knowledge.

Identify customer locations

Businesses may see where customers are physically located (states, cities, etc.) and how they found their websites and products, such as through recommendations or email marketing.

Enhance efficiency in the supply chain

Supply chains have become more complex as retailers take on additional merchants and start selling more of their own brand products.

Retail business intelligence can help you make sense of the data collected from your daily operations.

This enables retailers to develop more accurate forecast models and to pinpoint the main logistical bottlenecks that the supply team has to address to satisfy organizational KPIs.

A better understanding of consumer demands

Retailers must be able to adapt to the constantly shifting preferences of today’s customers to compete in the market, from desiring more socially and morally responsible products to needing bulk purchases completed quickly.

Businesses can acquire meaningful insights to map changes in client demand and modify their strategy with an efficient business intelligence solution.

Improve merchandising

Retailers can determine which products are selling well and which are not using BI. Making judgments regarding what to stock in stores and how to price things will be easier with the help of this information.

Improve inventory management

Retailers can use BI to determine whether an item is in limited supply so they can replenish it before customers start shopping elsewhere. It can also be used to keep an eye on when things are about to expire, so they aren’t thrown out too soon.

Check out how machine learning can drive retail success

Optimize store floor plans

A floor plan that may entice customers to shop for longer is one of the key reasons retailers use BI. Businesses should select a floor layout that makes shopping easy for customers.

Retailers can verify whether the chosen floor layout is adequate for the floor size and product types with BI software. They can use BI solutions to analyze various data sets (such as the number of stops made, the length of visits, etc.) and suggest a floor plan that will make it simple for customers to identify the products they want.

Competitor tracking in social media

Businesses can track KPIs, look at item scores, and monitor social media sentiment using retail business intelligence. Merchandisers may also utilize this information to track sales and the performance of certain products, businesses, and brands.

Informed decision making

Businesses can use BI to combine many data sources for a complete picture of what is happening throughout their organization. Enhanced BI solutions also enable this process across intricate franchise networks. This allows better-informed decision-making processes and contributes to developing a consolidated, holistic vision.

More effective marketing

Retailers can determine which marketing strategies are effective and which are not using BI. This information can boost sales, generate revenue, create new campaigns, and determine where to spend money on marketing.

What are business intelligence challenges?

Business analytics and business intelligence solutions examples

You understand how business intelligence helps retail companies collect and evaluate business data from across the organization so they may make wise decisions. So, what are examples of analytics used in retail sales?

Let’s look at some real-life business analytics and business intelligence solutions examples to show how this business solution is the retail industry. These are some of the biggest companies that use retail business intelligence:

• Amazon
• Starbucks
• Walmart

Let’s explore how the retail industry uses business intelligence.

Amazon

The business employs business analytics to promote items, make logistical business decisions, and personalize product suggestions.

The main factor behind the efficient operation of Amazon’s extensive supply chain is in-depth data analysis.

Starbucks

Starbucks forecasts what products and promotions a customer is likely interested in using a retail business intelligence software. The business lets clients know about the deals it thinks they’ll want to take advantage of.

Starbucks can enhance sales volume and bring in current consumers more regularly thanks to this approach.

Walmart

The retail behemoth uses BI technologies to understand how internet behavior affects in-store and online activities.

Using BI techniques, Walmart can comprehend the buying habits of its clients. For instance, they can learn how many customers used the Walmart app or website to search for a specific product before purchasing it on the same day. They can identify the busy times of the week and the user exit points.

Check out why business intelligence is a must in modern business

Best business analytics and business intelligence solutions in retail

The followings are some of the best business analytics and business intelligence solutions in retail:

• Alteryx

Alteryx

The self-service data analytics software provider Alteryx focuses on data blending and preparation. Users may clean, organize, and analyze data with Alteryx Analytics in a repeatable procedure.

Business analysts find this tool especially helpful when connecting to and purifying data from data warehouses, cloud apps, spreadsheets, and other sources. The platform provides capabilities for various analytical tasks (predictive, statistical, and spatial) to be performed inside a single user interface.

Amazon Web Services

Amazon QuickSight is a cloud-based business intelligence tool with embedded machine learning that is serverless and embeddable. It is some of the most known business analytics and business intelligence solutions in retail.

The tool enables the creation and publication of interactive BI dashboards that support natural language querying. It doesn’t require any infrastructure and can scale automatically to thousands of users. The pay-per-session pricing model promoted by QuickSight allows customers to only pay when users access dashboards or reports. You can use any device to access a dashboard.

IBM

Under two separate product lines, IBM provides a broad range of BI and analytic capabilities.

Users of the Cognos Analytics platform can access data to build dashboards and reports because it is an integrated self-service solution.

Incorporating automatic pattern recognition, natural language inquiry and generation support, and advanced analytics capabilities, IBM Watson Analytics provides a machine learning-enabled user experience. Both on-premises and as a hosted solution through the IBM Cloud, IBM’s BI software is available for deployment.

Microsoft

Microsoft is a significant player in business intelligence and analytics. Power BI, the company’s main product, is a cloud-based service provided by Azure Cloud.

On-prem capabilities are also available for individual users or when power users are creating intricate data mashups using internal data sources. Because users can create dashboards, prepare data for analysis, and uncover data using the same design tool, Power BI is exceptional.

The platform’s active user community helps expand the tool’s functionality and interacts with Excel and Office 365.

MicroStrategy

MicroStrategy combines self-service data preparation and visual data discovery in an enterprise BI and analytics platform. To connect to any enterprise resource, including databases, mobile device management (MDM) systems, enterprise directories, cloud apps, and physical access control systems, MicroStrategy offers native drivers and out-of-the-box gateways.

Its embedded analytics tool allows MicroStrategy to integrate various other websites and software programs, including chatbots, CRM tools, portals, and voice assistants like Alexa.

Oracle

A wide variety of BI and analytics solutions are available from Oracle, and they can be used either on-premises or in the Oracle Cloud.

The company’s Business Intelligence 12c solution includes conventional BI capabilities. With Oracle Data Visualization’s more sophisticated features, users can automatically visualize data as drag-and-drop properties, charts, and graphs. The program also enables users to save pictures of analytical moments in real-time through story points.

Salesforce

Depending on the position, sector, and features offered, the Salesforce Einstein Analytics platform has various variants. It is some of the most used business analytics and business intelligence solutions in retail.

Users can respond to inquiries using the product’s automatic data-finding capabilities that leverage clear and intelligible AI algorithms. Users can also modify analytics to fit their use case and strengthen findings with exact advice and detailed direction. With third-party apps, configurable dashboards, and customizable themes, Einstein also enables the creation of sophisticated experiences.

SAP

The enterprise and business-user-driven editions of SAP’s BI and analytics solutions are extremely comprehensive.

BusinessObjects Cloud and BusinessObjects Enterprise are cloud-deployed versions of the company’s flagship BI portfolio built on top of the SAP HANA Cloud. Additionally, SAP provides a range of conventional BI features for reporting and dashboards. The BusinessObjects solution houses the vendor’s data discovery capabilities, while the SAP Lumira tool set offers extra capability, such as self-service visualization.

Sisense

Organizations can easily extract business insight from complicated data of any size or format, thanks to Sisense. Without scripting, coding, or help from IT, consumers may aggregate data and discover insights in a single interface thanks to the solution. It is some of the best business analytics and business intelligence solutions in retail.

Additionally, it has extensive analytical capabilities, including a dashboard and visualization front-end. Organizations that want to evaluate significant amounts of data from many sources should use Sisense.

Tableau Software

Tableau is regarded as the key participant in the market and provides a comprehensive visual BI and analytics platform. The three primary distribution channels for the company’s analytic software portfolio are Tableau Desktop, Tableau Server, and Tableau Online.

Tableau is accessible on-premises or in the cloud and links to hundreds of data sources. Users may see and share data using Tableau Public, and the provider also provides embedded analytics tools.

Tellius

Tellius is a platform for AI-driven decision intelligence that enables quick data insights. The business uses automation and augmentation to speed up customers’ time to insight.

Users of the Tellius Platform can query their company data, examine trillions of records, and derive automated insights by combining AI and machine learning with a search interface for ad hoc exploration. Live Insights, which provides AI-guided insights from cloud data warehouses without relocating data, was just introduced by the firm.

Is artificial intelligence better than human intelligence? Check out the cons of artificial intelligence

Retail BI implementation cost

The price of a retail BI installation project, which includes creating an OLAP cube, self-service reports, dashboards, and a central data warehouse with data marts for storing retail data, may be as follows (Software license fees are not included):

• $80,000-$200,000 – for retail companies with 200-500 employees
• $200,000 – $400,000 – for retail companies with 500-1,000 employees
• $400,000 – $1,000,000 – for retail companies with 1,000+ employees

What is retail analytics software?

Retail software is computer software that is usually downloaded through the Internet and installed on PCs after 2005. (also known as cloud-based).

In the past, this software was distributed using tangible data storage media that was sold to end users, but today, very few businesses still distribute their software via tangible media. Usually, restricted licenses (like EULAs) or the Software-as-a-Service (SaaS) business model are used when selling software.

What is an example of retail software?

The goods sold on IBM PCs and its knockoffs in the 1980s and 1990s, including well-known programs like Lotus 123, Word Perfect, and the different components of Microsoft Office, are the most well-known examples of retail software.

 How do build the best business intelligence strategies?

What are the types of retail data analytics?

There are four different kinds of retail data analytics, which is crucial in giving modern merchants critical knowledge on running their companies.

The retail data analytics types are as follows:

• Descriptive analytics
• Diagnostic analytics
• Predictive analytics
• Prescriptive analytics

Conclusion

To ensure that retailers can take advantage of business intelligence and business analytics, choosing and applying the appropriate BI tool is crucial.

Businesses and franchises may turn data-driven insights into successful outcomes by combining data with a centralized view, establishing and tracking KPIs, and using easy-to-customize dashboards.

Do you know business intelligence analyst, data architect, cloud computing, and data engineer jobs are hot and on the rise?

Data management enables a business process to be more efficient. The majority of contemporary organizations are aware of the value of data. This frequently means depending on the reports produced by the third-party software platforms they use daily for small firms. It is important to combine this data into a single, standardized source at some point. Data management is a business process required to organize and secure this valuable information properly.

What is a business process, and why data management is vital?

A method for describing how data is gathered and processed within a company is what data management is all about. With the need for governance surrounding the citizen developer movement, it is a subject that is receiving more and more attention.

Data consistency, dependability, and security are all ensured by an efficient data management program. Typically, the program has a governance committee and a group of data stewards. In an organization, these teams collaborate to establish, create, apply, and enforce data procedures.

Gartner defines data management as:

“Data management (DM) consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.”

How can data analytics improve business processes?

The integration of business processes with data is not a new idea. Concerning many of the current hot themes in data management, it appears to be seeing a rebirth, similar to many other basic architectural components.

Understanding that all of your clients will value connecting to business priorities and the business processes that support them is critical when dealing with various clients in different industries with quite diverse data management projects underway. Here are a few instances where business processes were used in various circumstances: 

• Big data analytics
• Master data management
• Data governance
• Data quality

Master data management

The discipline of master data management (MDM) aims to provide a “single version of the truth” for key business components such as customers, products, suppliers, etc. A “single version of the truth” is a compilation of multiple viewpoints on one reality, much like the well-known tale of the blind men and the elephant. If you are unfamiliar with the traditional fable of blind men and the elephant, it is about a group of blind men who each touch an elephant to get a feel for it.

Each man has a unique idea of what it means to be an elephant based on what he touches: the trunk, the tusk, the tail, and the hide. The “single version of the truth” is a superset of all of their experiences, but each is correct in its own way. A comparable situation is presented by master data management.

Consider a typical master data domain like Product. While multiple user groups within the business have access to a comprehensive view of a “Product” with a superset of attributes, each user group understands what “Product” information contains and how it should be used.

Each supply chain organization can view, add, modify, and/or delete certain data elements that make up the idea of “Product.” Identifying these stakeholder groups and working with them to comprehend their usage and requirements around the relevant data domain is crucial for the success of MDM.

Data governance

A structured process model can be useful when managing data in the data governance domain, particularly concerning people and processes. Figuring out how and by whom data is used throughout the business process can assist in establishing the correct data stewardship and ownership. It can assist in settling disputes if there are ownership issues.

Data quality

Similar to this, the business process is crucial in the domain of data quality. Data can be cleaned, verified, and enhanced in various ways, and numerous tools and techniques are available to support data quality in these ways. However, data quality strategies are destined to be ineffectual if used in a vacuum without considering how business processes are used.

The example of a lake that harmful substances have contaminated is a frequent analogy used to explain this situation. Biologists can try to purify a lake’s water, but their efforts will be in vain if they don’t consider the streams supplying the lake with toxins. The clean lake will once more contaminate the tainted water from the streams.

Big data analytics

Rich information from big data analytics can be provided for various sources, enhancing traditional data sources like a data warehouse. To generate a “360 picture of the consumer,” it is possible to use customer data such as social media sentiment analysis, buying trends, footfall analytics, and more. But if this analysis is carried out in a vacuum, it won’t be very useful. For instance, if we have data on customer sentiment, it’s crucial to comprehend where the client is in the product’s lifecycle when this emotion is conveyed.

Have they just bought the product, started a service complaint, returned the item, etc.? To fully comprehend their experience, it is essential to link their sentiment to where they are in the purchasing lifecycle.

Everything you should know about big data services

Customer journey maps are frequently developed to understand better the customer lifecycle and how data is changed at each stage. When big data analytics are connected to business processes, their value increases.

How can big data be used to understand or optimize business processes?

For a while, “Big Data” has generated buzz across industries. Every executive has been putting new plans into practice to benefit from big data. Truth be told, the idea of big data is always changing and continues to be a driving force behind a number of digital changes, including artificial intelligence (AI), data science, and the internet of things (IoT). But how exactly can the business world benefit from big data? Here are a few illustrations we wanted to provide to motivate your staff for upcoming tasks.

Customer management 

One industry that makes extensive use of big data is proper customer handling. Numerous data models have been created to evaluate customer data with great success. The analysis’s findings are efficiently streamlined to improve company choices. Applications for data analytics include:

• Creating effective pricing strategies
• Assessing the level of service and client satisfaction
• Evaluating the success of customer-related strategies
• Supply chain management improvements and Maximize customer value
• Acquiring new clients and maintaining current ones
• Carrying out precise prediction analysis 
• Certifying client data
• Providing and predicting accurate consumer classification and behavior 

Managing the waste

A significant share of business resources is being wasted. Businesses may effectively improve their waste management procedures with the right data management. The precision that big data analytics provides to business intelligence is its main advantage. This accuracy helps firms make informed decisions about trash management. The measurement is at the center, making it simpler to identify the business operations that generate the most waste. So, if you want to use big data to manage waste, the advice that follows will help you get the most out of it:

• Choose the data that your company wants to collect
• Take measurements at various times throughout the chosen process
• Utilize specialists and specialized software to examine the facts and consequences.
• Make the necessary changes to waste reduction.

Manufacturing methods

Big data analytics are used to improve industrial methods’ precision and effectiveness. Many modern manufacturing companies are embracing the Industrial Internet of Things (IIoT), which is already powered by data analytics and sensor technology. Manufacturers with processes requiring massive data sets are leading the adoption race, as was to be expected.

AI in the manufacturing market will rise by 14 billion dollars in 5 years

For instance, computer chips typically go through 15,000+ testing before being issued. Predictive data models are being used, which has decreased the number of tests needed and saved millions of dollars in production costs. Small manufacturing companies are likewise reorganizing their operations using data analytics. In the manufacturing industry, big data can be applied to:

• Product customization
• Assessing the quality of components and raw materials
• New product forecasting, testing, and simulation
• Increase in energy efficiency
• Evaluation of supplier performance
• Risks chain management in the supply
• Locating flaws and monitoring product attributes 

Developing a product

Any product’s development has historically involved extensive data collecting and analysis. It primarily explains why using big data to prepare a product has substantial commercial benefits. Before releasing any product to the market, developers must gather and analyze information about the competitors, customer experience, price, and product specs. Answering the above questions can be vital when developing a new product:

• Which trends are dominating the market?
• What deals and prices are being offered by rivals?
• What benefits and drawbacks do products from rivals have?
• What issues are we trying to solve with our products?
• What goods or services might astonish customers?

More process analysis of substantial data is required to fully address the abovementioned problems. Data management offers a more accurate and comprehensive approach to product development than traditional methods. This strategy guarantees that every product created is suitable to meet a market requirement.

Customer surveys, crowdfunding and manufacturer websites, marketing blogs, online product reviews, product associations, retailer catalogs, social media platforms, and other sources can all be leveraged to extract data using data analytics. When exploring the latest trends, you can also use market automation tools, for instance, we’ve listed 13 marketing automation tools that can help you boost your sales.

Finding new talent

One of the crucial parts of a firm is its human resources (HR) department. Big data analytics can be used in HR to manage and recruit talent with accuracy and thoroughness. Predictive data models, for instance, can help evaluate a worker’s performance. We have also discussed how important AI’s impact on recruitment is before. However, most companies still base these choices on insufficient information, costing them a sizable fortune over time. Big data can be used to generate more effective personnel management strategies for the following data types:

• Delays in production and delivery
• Absenteeism among workers
• Data on training, work production, employee error rates, and profiles
• The workload of employees and staffing levels
• Employee incentives and performance reviews
• Evaluation of revenue per employee
• The six-sigma data

Big Data’s application in talent management has many benefits, including assisting management in identifying productivity issues and locating individuals with the right needs and values. Additionally, it promotes creativity, aids in management predictions, and aids in understanding the skills and requirements of various people.

Who is responsible for data management?

The IT department often implements a data management system. Typically, a CDO or the project lead is in charge of this.

A business, however, has the option of outsourcing the execution of data management. This is advised for businesses that lack a full-time Chief Data Officer (CDO) with the necessary skills or whose IT team lacks the time or resources to execute the system.

Companies who want to swiftly deploy their data management system or have complicated data or requirements that will make the implementation difficult can consider outsourcing data management.

What are the 3 types of business process analysis?

According to Mark Von Rosing, the author of the book called “The Complete Corporate Process Handbook: Body of Knowledge from Process Modeling to BPM,” business processes should be organized into three subsections:

• Operational process
• Supporting process
• Management process

Operational business processes

Asking yourself, “how does, or will, your business create income” will help you identify your operational processes. Operational processes are the procedures and duties that directly contribute to the creation of outputs from inputs.

Items like labor, unprocessed equipment, and money are examples of inputs. The finished product or service and the degree of client pleasure results are examples of outputs.

Generally speaking, if your process fits into one of the categories listed below, you can classify it as operational.

• The process of developing or producing the finished good or service
• The promotion of the aforementioned good or service
• Even after the sale, the support and customer care you provide

Consider the scenario where you are a neighborhood greengrocer who serves your neighborhood with fresh veggies. All involved tasks—buying the fruit from the supplier, boxing it up, and distributing it to your customers—represent operational processes.

It’s important to remember that there may also be sub-processes, such as storage. Although it might not seem like it, this is an operational procedure because it is connected to your final product.

One of your top strategic priorities should be ensuring that your operational procedures integrate as effectively as possible.

Supporting business processes 

These make up the engine room’s cogs. The supporting processes are the items that operate quietly in the background to make sure the ship can continue to sail. This indicates that they are not self-sustaining but rather exist to support the internal employee population throughout the organization. They add value, but not in monetary terms.

The payroll department, for instance, may not always bring in revenue, but without them, your employees wouldn’t get paid. The same is true for someone who cleans houses or does the dishes; even if they may not make much money from their job, you would undoubtedly notice without them.

These are either or both, strategically significant and required processes that enable the effective execution of operational processes.

Business management processes

The coordination of the aforementioned procedures happens here. This calls for planning, oversight, and general supervision.

This entails, among other managerial responsibilities, ensuring that the team is fulfilling its goals, that the workplace is safe and compliant, and employee complaints are addressed. It also entails spotting possible risks or prospects for your company, such as talent in one of your employees who could benefit from training or a potential new client who could help your company get a good deal.

Management exists to maximize income potential and modify the firm as needed, even though, like supporting operations, it does not always result in direct money.

The resilience of a corporation is mostly a function of effective management procedures.

Business process management tools

A business process management (BPM) tool is a software program that supports you as a manager through all phases of business process management by assisting you in designing, modeling, executing, monitoring, and optimizing business processes.

Regardless of your present procedure’s effectiveness, there is always room for improvement. You can aim to save expenditures overall or the amount of time it takes to develop a certain asset. You can improve current business processes by using strategies like process standardization or automation.

AI and computer vision are becoming key tools for shop-and-go platforms

One of the best methods to boost productivity is through business process automation. Allow your automation platform to handle repetitive tasks rather than perform them manually.

The best business process management methods must be established as part of process standardization. Establishing defined stages will minimize failure rates while cutting expenses and time spent on repeat processes instead of doing them randomly.

Benefits of using a BPM tool

The question remains, why should YOU use a business process management (BPM) tool to optimize your business processes? In the following section, we summarize the most common benefits:

• It saves time: Time is a limited resource in all facets of a business, whether you need to build a new product or fulfill client requests that have already been made. You can automate processes and free up resources with the correct BPM technologies.
• It reduces costs: You will automatically save money if your staff is able to complete jobs more quickly. Use as many of these tools as you can to get the most out of automating business process management.
• Brings better and higher quality outputs: BPM and workflow management systems are the answers if you want to provide improved quality and consistency across all of your company’s outputs.
• Reduces failure rates: Your failure rate will drop through automated and standardized operations, improving your company’s bottom line. The advantages of the best BPM solutions can be used to the advantage of all enterprises. The top business process management tools are reviewed in the section that follows. These solutions can help you cut costs and time while improving the quality of your outputs.

Conclusion

Business processes offer a crucial context for how data is used inside a company, which is essential since data is only valuable when presented properly. Business process models provide insight into how and by whom information is used, which directly affects big data analytics, big data management, master data management, and other data management projects.

Importantly, it assists in determining company priorities. Prioritizing business-critical data is a crucial step in any data management discipline because it is difficult to manage all information in an organization closely. The business process creates the backdrop for setting priorities.

Does this information, for instance, support the revenue-generating sales cycle? Does the organization as a whole use this data across a variety of processes? Does this knowledge contribute to a more effective supply chain? If you can answer “yes” to questions like these, you can figure out the crucial information that underpins business success.

AI is the key to being a competitive business

Using process models to more completely comprehend how data is used in a company setting where cost-benefit analysis is constantly the driving factor helps grasp the benefits and drive efficiencies that cut costs. We urge you to consider incorporating business process models into your upcoming project.

How decision intelligence changes the way companies make decisions

Our hyperconnected world has become so complex that existing decision-making processes within organizations are no longer sufficient. In a study, about 65% of executives from Fortune 500 companies said that, as a result, decision-making in their organization has also fundamentally changed. The perception that high-quality company decisions are made is reported by just 57% of respondents.[i]

Moreover, human nature is not very efficient at making good decisions. Most of the time, whether we like it or not, our judgments are usually based on emotions and influenced by unconscious biases. People want to act rationally, but they can’t because they have natural limits to how much information they can absorb and process.

We also tend to settle for the minimum acceptable requirements we need to find a satisfying solution – a phenomenon that is known as “satisficing” (a combination of the words “suffice” and “satisfy”): It’s just a lot easier and faster to sacrifice some things to obtain satisfaction rather than considering all the necessary information to find the optimal solution to a problem.

What is Decision Intelligence?

Time for companies to rethink decision-making. The tool to make proven groundbreaking decisions for your business is Decision Intelligence (DI). It enables organizations to make future-proof decisions faster and more efficiently using advanced technologies such as AI, machine learning, or process automation.

The great breakthrough is: Consideration is given not only to raw data but also to a multidimensional set of data that includes text, images, video, and audio. This way, cognitive technologies cannot only deeply analyze vast amounts of data but also evaluate their correlation, making it possible to derive reliable forecasts and identify decision needs that you might otherwise miss.

The term “decision intelligence” was first introduced in Lorien Pratt’s book “Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World” before being adopted by market researcher Gartner, who has named DI one of the most important technology trends in 2022 and further developed as a strategic business tool.[ii]

How does Decision Intelligence improve decision-making?

By using decision intelligence, we can make better and more informed decisions, for example, by matching hazy feelings with validated data. Beyond that, AI-powered decision-making comes with five key benefits:

• Identify complex interrelationships 

By 2025, there will be 175 zettabytes (ZB) of data worldwide, predicts the International Data Group.[iii] For humans, ingesting and processing such massive amounts of data is almost impossible. 

• Make decisions faster 

Slow decision-making hinders progress and profitability. With the help of AI systems, companies can decisively shorten complex decision paths and respond more quickly to changing parameters, reducing the risk of unforeseen events.

• Improve personal judgment 

External factors such as cognitive or emotional biases influence factual judgment. The truly optimal decision for business success is thus often overlooked. By using Decision Intelligence, we can rationalize our decisions because we can make factual judgments based on bias-free data analysis.

• Decisions become measurable 

Decision Intelligence elevates decisions to an essential strategy tool for sustainable business success. Based on concrete business goals and key figures, AI systems can be used to derive suitable optimization measures and future scenarios.

• Decisions become scalable 

A company’s multi-layered data sets are usually scattered across different departments. AI-driven decision-making processes enable companies to correlate critical influencing factors from different sources and data perspectives.

What makes DI so impactful for businesses?

Every day, companies have to make countless decisions. Decision Intelligence enables you to leverage your data strategically and consistently to make the optimal decision for your business goals at any time and across the entire value chain. The reasons for this are self-evident: AI-supported decision-making can reduce unnecessary costs arising from slow processes and high failure rates, and decisions are made transparently and measurably. These reasons greatly increase a company’s knowledge management in the long run.

In other words: The ability to consistently and logically create value by reproducing optimal decisions, again and again, is the perfect ground to drive an effective strategy for reaching new levels of business growth. In particular,

1. Generating customer growth

With the help of DI-driven predictions and insights, companies can make reliable predictions about the effectiveness of their actions, identify cost-saving potential, and optimize internal processes.  

2. Increasing sales

Data-driven customer analytics allows you to identify high-value customers, deliver targeted marketing campaigns, and optimize the entire customer journey to attract new customers faster. 

3. Reducing costs

With Decision Intelligence, organizations can identify the factors that affect their revenue, predict how pricing, cross-selling, and upselling impact sales, and forecast when leads will convert to buyers. 

4. Maximize profit margins

AI-powered forecasts and trade-offs also will help you set prices and discounts or balance your staff capacity to maximize profit margins. 

How does AI-based decision-making work?

For routine business tasks, production and customer operations, an end-to-end automation is the fastest and most profitable solution. Using programmed processes, repetitive tasks and actions can be executed flawlessly and without interruption. But beyond these predefined processes there are innumerable choices to be made which require intuition, flexibility and coordination between all personnel involved.

Just like us, machine-learning systems learn from experience and independently find solutions to new and unexpected problems as they prepare the entire process from data analysis to decision recommendation. Decision makers can make the right decisions by choosing from all proposed alternatives. In other words, using Decision Intelligence never involves leaving critical decisions to machines but rather combines human experience and intuition with automation to take decision-making to a whole new level.

How can I implement DI in the company?

The number of DI users is still relatively small. Analyst Dr. Pieter J. den Hamer predicts that 33% of large companies will begin implementing Decision Intelligence by 2023.[iv] A good starting position if you want to put your company ahead of the competition.

However, the use of AI technology in itself is not enough to outpace the competition. It usually involves rethinking your company’s culture and removing from focusing purely on IT. According to Gartner’s analysts, a company that wants to fully exploit the benefits of Decision Intelligence should make its decisions as follows:[v] 

Connected

Decisions have a mutual impact on individual personnel of an organization so the process must be much more connected at all levels. Sharing data and insights is the bread and butter of this process.

Contextual

Any alternative decision being considered must be evaluated beyond the constraints of a single event or transaction.

Continuous

Companies must respond to both opportunities and disruptions as quickly as possible. Decision-making is increasingly becoming a continuous process.

Companies set their starting point for using DI by analyzing the current state of their decision-making processes. At what point are the decision-making processes so complicated that they become unmanageable? At what point is there a huge amount of data but little insight? Where is the opportunity to merge multiple decision silos? Meetings, where decisions are made, should be monitored along with organizing interviews with decision-makers and asking them to explain some examples of how decisions are being made. This allows decision-making principles to be defined and decision-making habits to be identified.

Scale your business with Decision Intelligence tools

Following this and selecting customized technologies and tools will make it possible to review important use cases step-by-step before scaling the DI approach for the entire company.

This is where paretos steps in, Germany’s leading Decision Intelligence platform. The Heidelberg-based tech start-up makes analysis processes for companies as easily accessible and integrable as an email programme. With the help of AI-based software as a service tool, innovative SMEs, dynamic start-ups, and large corporations can carry out extensive data analyses without prior knowledge or the expertise of data science specialists.

Based on existing company data, paretos analyzes optimization potentials and visualizes correlations in a user-friendly dashboard so everyone can obtain in-depth insights without data expertise. Thanks to a modern user interface, all information can be managed quickly and easily. The automated optimization approach identifies new solutions faster and more efficiently than familiar analysis tools or manually calculated scenarios. This allows logistics companies, for example, to evaluate how CO2 consumption, delivery speed, and costs should be balanced to increase profit margins. To be able to do this, paretos takes on the task of combining all of the dynamic factors that make many digital organizational processes so complex today.

Among other things, the underlying software is capable of fully automating the most complex challenges in the business and marketing context today:

• Targeting customers using personalized product recommendations, cross-selling options, and impact analysis (Customer Recommendations).
• Dynamic pricing of products and services in response to market changes (Dynamic Pricing).
• Efficient inventory management to optimize logistics processes in real-time (Warehouse Optimization).

Using paretos, the German e-commerce retailer SNOCKS, for example, established an intelligent price management system quickly. This allows the company to control its discount prices in a data-driven manner and adjust them according to demand. Also, one of Europe’s largest parcel delivery companies achieved a prediction accuracy of up to 95% on its expected parcel volumes after just five months of using paretos’ software. The increasing volume of available analyses and automated forecasts allows companies to create more precise deployment plans and, thus, sustainably save on operating costs while incorporating CO balance targets into their decisions. 

And this is just the beginning. Altogether, the opportunities to leverage Decision Intelligence to improve the profitability of your business are endless. Now is the time to reconsider your decision-making processes.

[i] https://www.mckinsey.com/business-functions/people-and-organizational-performance/our-insights/decision-making-in-the-age-of-urgency

[ii] https://www.gartner.com/en/documents/4006925

[iii] ⁱ https://www.iwd.de/artikel/datenmenge-explodiert-431851

[iv] https://www.forbes.com/sites/eriklarson/2022/03/21/how-decision-intelligence-will-finally-change-decision-making-from-mystical-to-mundane/?sh=26c957086a74

[v] https://www.gartner.com/en/publications/what-effective-decision-making-looks-like

CaaS, or containers as a service, is a subscription-based cloud service model that enables you to manage clusters, applications, and containers utilizing Web portals, APIs, and container-based virtualization. The development community has been paying close attention to the hot issue of containerization as they try to create portable application components for multi-cloud infrastructure setups.

The rise of cloud computing jobs shows us that cloud-related technologies are a hot topic. Do you know the cloud computing basics? Learning the pros and cons of cloud computing will help you with CaaS as a concept.

What is a container as a service (CaaS)?

CaaS is simply the automated hosting and deployment of software packages in containers. Without CaaS, software development teams would have to deploy, manage, and monitor the infrastructure that supports containers. It takes specialized DevOps resources to supervise and maintain this infrastructure, consisting of cloud computing machines and network routing systems.

Software developers and IT departments can upload, organize, run, scale, and manage containers using containers as a service (CaaS), a cloud-based service that uses container-based virtualization.

To execute on any host system, software must incorporate its dependencies, including code, runtime, configuration, and system libraries. Software development teams may quickly scale and deploy containerized apps to high-availability cloud infrastructures thanks to CaaS.

Platform as a Service (PaaS) and Container as a Service (CaaS) are two different types of cloud computing. While CaaS can deploy many stacks per container, PaaS is focused on explicit “language stack” deployments like Ruby on Rails or Node.js.

CaaS lies between Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) on the spectrum of cloud computing services. However, CaaS is frequently seen as a part of IaaS. Instead of a virtual machine (VM) or bare metal hardware host system, which are often utilized to support IaaS environments, a container serves as the fundamental resource for CaaS.

Are you wonder differences between CaaS, PaaS, and IaaS and what they mean? Check out CaaS vs PaaS, IaaS, and FaaS comparison.

The following are the most widely utilized orchestration technologies in the CaaS framework:

• Docker
• Kubernetes
• Data Center Operating System (DC/OS)

What is a container?

Containers are executable software units that allow for the standard packaging of application code, libraries, and dependencies. As a result, they may be run anywhere, including on a desktop computer, in a typical IT environment, or in the cloud.

What is Kubernetes in simple words?

A container, or containerized application, is managed by a system called Kubernetes. A container can be thought of as a portable virtual computer. You must create many containers and utilize Kubernetes to manage them to construct an application. The benefit is that Kubernetes can automatically produce and scale containers and manage storage among all the containers.

A cluster is a Kubernetes system that includes all available Kubernetes components. Virtual or physical machines (such as desktop computers or laptops) can execute the cluster. If you only have one computer running a complete Kubernetes system, that computer hosts your Kubernetes cluster. If you have two computers running Kubernetes, they serve as their hosts. Any mix of virtual and real machines may be used to execute the cluster.

What are the features of CaaS?

For small IT enterprises that are about to expand, CaaS is highly advantageous. These are some of the most important features of CaaS:

• Integrated and unified communication
• No investment
• Flexibility & scalability
• No risk of obsolescence
• No maintenance cost
• Business continuity

Let’s take a closer look at CaaS features:

Integrated and unified communication

Chat, multimedia conferencing, Microsoft Outlook integration, real-time presence, “Soft” phones (software-based phones), video calls, unified messaging, and mobility are some of the most modern unified communication capabilities.

More quickly than ever before, CaaS vendors now provide new features to their CaaS services. Because end users profit from the provider’s scalable platform architecture and ultimately, the many end users who use the provider’s service share this cost of an upgrade, it has become more cost-effective for providers to quickly add a new feature to their CaaS application.

No investment

The hardware and software deployed to deliver communication services to their customers are entirely under the management of the CaaS vendor. The user only pays for the services he receives from the provider of communication as a service (CaaS), not for the communication features installed to deliver those services.

Flexibility & scalability

Customers can contract with CaaS companies to provide them with communication services. Customers pay for the items they have requested. According to their needs, the consumer might increase their service requirement. This makes communication services flexible and scalable, as well as cost-effective.

No risk of obsolescence

To keep up with changing market demands, the CaaS vendors regularly update the gear and software that deliver communication services. Therefore, the consumer should not be concerned about the services becoming obsolete.

No maintenance cost

The cost of maintaining the technology used to provide communication services is not borne by the customer that outsources the CaaS service.

Business continuity

How long can your firm operate if a disaster hits the area where it is located? Because of this, businesses now divide their data across different data centers to preserve redundancy and aid in quick recovery from major events.

The CaaS providers adopted and implemented the same feature to ensure voice continuity or communication continuity even in the case of a catastrophic incident.

How does CaaS work?

CaaS offers the ability to deploy and host containers in numerous cloud environments automatically. Because CaaS is not dependent on a single code stack or language, it can be used in multicloud and hybrid cloud setups.

The automation offered by CaaS can greatly improve your pipeline’s efficiency. The productivity of the development and IT team grows due to the automation of various tasks. Automation can also expedite procedures and guarantee that the ecosystem is constantly updated.

Advantages of the container as a service (CaaS)

What is the main advantage of CaaS? The things that CaaS enables IT departments and software development teams to do—and refrain from doing—make it crucial.

Check out the 7 benefits of cloud computing

These are some of the advantages of the container as a service (CaaS):

• It is considerably simpler to deploy and develop distributed apps or create smaller services thanks to containers and CaaS.
• Customers of the CaaS company can contact them at any time.
• A group of containers may manage various tasks or coding environments during development.
• It is possible to define and bind relationships between containers in a network protocol for forwarding.
• Predetermined and specialized container structures can be swiftly installed in cloud capture thanks to CaaS.
• Features like log integration and monitoring can aid program performance by posting apps deployed on the CaaS platform.
• Automated measuring performance and orchestration management are already included with CaaS.
• Building distributed systems with high visibility for high availability is made possible by CaaS.
• CaaS facilitates quicker deployment, which strengthens team development.
• While CaaS can save engineering operational costs by decreasing the DevOps resources required to manage deployments, containers are utilized to avoid targeted deployment.

Disadvantages of the container as a service (CaaS)

What are the possible disadvantages of CaaS? Let’s take a closer look at the disadvantages of the container as a service (CaaS):

• There are restrictions on the CaaS, depending on the provider.
• It is risky to retrieve company data from the cloud.
• Security issues
  • Although they have inherent hazards, containers are considered safer than rivals like Microsoft Machines.
  • Containers share the same kernel as the operating system, even if they are agnostic platforms. This increases the possibility that the containers will be targeted.
  • As more containers are deployed in the cloud via CaaS, the hazards exponentially will rise.

• Performance limitations
  • Containers are visible areas and do not run directly on bare metal.
  • The additional layer between the bare metal and the application containers and their characters leaves something out. This results in a considerable loss of speed.
  • Despite the high-grade hardware available, enterprises have to deal with some functional losses in the containers.

Check out the cloud computing vulnerabilities

Container as a service examples

Like other cloud computing services, users can only select and pay for the CaaS resources they need. CaaS examples include load balancing, compute instances, scheduling options, and more options that depend on the company’s needs.

The best container as a service providers

CaaS services are provided by almost all public cloud providers, including Amazon, Google, Microsoft, and IBM.

Some of the best container as a service providers are:

• AWS
• Google Cloud
• IBM
• Docker Enterprise
• Alibaba Cloud
• Azure
• Oracle

So, what do they offer?

AWS Container Service

The container as a service AWS launched in 2015. Amazon ECS is one of the most dependable and secure container services. It is a fully managed container service appropriate for sensitive and mission-critical data apps.

Core features:

• Serverless option
• Reliable with 77 availability zones 
• Batch processing 

Google Container Engine

Regarding the top CaaS providers, Google Cloud Engine is regarded as one of the best solutions for deploying and running your apps. With this Container as a Service solution, you can reliably execute and scale your applications from a single computing environment.

Core features:

• Autopilot mode 
• Automatic scaling 
• Built-in dashboard

IBM Kubernetes Service

Another of the top platforms for Container as a Service where you can install and manage your application on distributed clusters is IBM Kubernetes Service. Using this outstanding CaaS service, you may scale your application efficiently and easily.

Core features:

• Logging & Monitoring 
• Self-healing Containers
• Secure clusters 

Oracle Container Service

One of the top companies offering containers as a service is Oracle Container Service, where you can receive fully managed containers with excellent features. Oracle Container Service makes creating, distributing and managing your applications on containers in distributed clusters simple and efficient.

Core features:

• Automate Kubernetes operations 
• Automatic upgrades 
• Cluster management tools 

Azure Container Service

Container as a service Azure is another great solution. Microsoft offers one of the best Container as a Service options with its Azure Container Service. With fully managed containers, which have fantastic functionality to scale and manage your apps, you can easily and rapidly deploy and run your application.

Core features:

• Automated rollouts & rollbacks
• Easier cluster upgrades

Alibaba Container Service for Kubernetes

Due to its extensive feature set, Alibaba Container Service is recognized as one of the top providers of containers as a service. You can quickly deploy and manage your application in container clusters by managing the infrastructure.

Core features:

• Alibaba cloud accounts 
• Networking

Docker Enterprise

The container as a service Docker is the last solution that we explain. Docker Enterprise is a complete solution for managing and scaling your applications. Another excellent choice for a CaaS supplier is this one. Effective building and management of the entire application are possible.

Core features:

• View container clusters 
• Monitoring & Scanning 

CaaS vs PaaS, IaaS, and FaaS

Let’s examine how containers as a service differ from other well-liked cloud computing methods.

CaaS vs PaaS

What is Platform as a Service (PaaS)? Platform as a Service (PaaS) refers to the provision of a platform that combines hardware and software by third parties. With the PaaS approach, platform providers manage the infrastructure while end users create, maintain, and execute their apps. Providers often offer application development, testing, and deployment tools in addition to storage and other computer resources.

CaaS is different from PaaS because it is a more basic service that simply provides one particular piece of infrastructure—a container. CaaS services may include development services and tools like CI/CD release management to be comparable to a PaaS model.

CaaS vs IaaS

What is Infrastructure as a service (IaaS)? Infrastructure as a service (IaaS) in the public cloud offers basic computing resources like servers, storage, and networks. It enables enterprises to scale resources with less risk, overhead, and no up-front cost.

In contrast to IaaS, CaaS offers an abstraction layer over the bare hardware resources. Compute instances, which are essentially computers with operating systems running in the public cloud, are offered through IaaS services like Amazon EC2.

CaaS services enable customers to operate containers directly on bare metal resources or run and manage containers on top of these virtual machines in the case of services like Azure Container Instances.

CaaS vs FaaS

What is Function as a Service (FaaS)? Serverless computing, also known as Function as a Service (FaaS), is appropriate for customers who need to run a specific application function or component without managing servers. With FaaS, the user merely supplies code and pays based on the time or the number of executions. At the same time, the service provider automatically maintains physical hardware, virtual machines, and other infrastructure.

Users can configure and manage containers using CaaS, which varies from FaaS because it allows direct access to infrastructure. To deliver container services while separating servers from users, certain CaaS services, like Amazon Fargate, use a serverless deployment strategy; as a result, they are more comparable to a FaaS paradigm.

Conclusion

CaaS is a potent contemporary hosting paradigm that uses containers and necessitates expertise with them. Software development teams that are very agile can benefit greatly from CaaS. Establishing continuous deployment on a project can benefit greatly from it. Since most contemporary cloud hosting companies offer CaaS solutions at reasonable pricing, you won’t have to travel far to find a reliable CaaS.

The much-awaited comparison is finally here: machine learning vs data science. The terms “data science” and “machine learning” are among the most popular terms in the industry in the twenty-first century. These two methods are being used by everyone, from first-year computer science students to large organizations like Netflix and Amazon.

The fields of data science and machine learning are related to the use of data to improve the development of new products, services, infrastructure systems, and other things. Both correspond to highly sought-after and lucrative job options. But, they are not the same. So, what are the differences?

Machine learning vs data science: What is the difference?

Machine learning is the study of developing techniques for using data to enhance performance or inform predictions, while data science is the study of data and how to extract meaning from it.

Similar to how squares and rectangles are related to one another but not the other way around. Machine learning is the square that is its entity, whereas data science is the all-encompassing rectangle. Data scientists frequently employ both in their work, and practically every industry quickly embraces them.

The terms “machine learning” and “data science” are quite trendy. Even though these two words are frequently used interchangeably, they shouldn’t be considered synonymous. However, do not forget that machine learning is a part of data science, even though the topic is very broad and has many tools. So, what distinguishes them then? First, let’s briefly remember what they are.

What is data science?

As the name says, data science is all about the data. As a result, we can define it as “An area of a thorough study of data, including extracting relevant insights from the data, and processing that information using various tools, statistical models, and machine learning algorithms.” Data preparation, cleansing, analysis, and visualization are all included in this big data management paradigm.

Data scientists gather raw data from various sources, prepare and preprocess it, and then apply machine learning algorithms and predictive analysis to glean actionable insights from their gathered data. For instance, Netflix uses data science approaches to analyze user data and viewing habits to comprehend consumer interests.

Check out and learn about data science:

• Most popular data science techniques
• Best data science boot camps (and why are they so popular)
• How data science conquers the customer journey (with examples)
• What did Nvidia announce at GT 2022 about data science?

Data scientist skills

Skills needed to become a data scientist:

• Statistics
• Data mining and cleaning
• Data visualization
• Unstructured data management techniques
• Programming languages such as R and Python
• Understand SQL databases
• Use big data tools like Hadoop, Hive, and Pig

What is machine learning?

Artificial intelligence and the discipline of data science both include machine learning. Developing technology allows machines to complete a task and learn from previous data automatically.

Through machine learning, which uses statistical techniques to enhance performance and forecast outcomes without explicit programming, computers can learn from their prior experiences on their own. Email spam filtering, product suggestions, online fraud detection, etc., are some of the common uses of ML.

Check out and learn about machine learning:

• Real-life examples of machine learning
• What is linear regression in machine learning?
• Machine learning benefits for business
• How to manage the machine learning lifecycle?
• The history of machine learning
• What is quantum machine learning?
• What is machine learning engineering?
• How much are machine learning engineers’ salaries?

Machine learning engineer skills

Skills needed to become a machine learning engineer:

• Computer science fundamentals
• Statistical modeling
• Data evaluation and modeling
• Understanding and application of algorithms
• Natural language processing
• Data architecture design
• Text representation techniques

Comparison: Data science vs machine learning

Machine learning focuses on tools and strategies for creating models that can learn on their own by analyzing data, whereas data science investigates data and how to extract meaning from it.

A researcher who uses their expertise to develop a research methodology and who works with algorithm theory is often referred to as a data scientist. A machine learning engineer creates models. By conducting experiments on data, they strive to obtain specific reproducible outcomes while selecting the best algorithm for a certain problem.

The key distinctions between data science and machine learning are shown in the table below:

Data scientists vs machine learning engineers

Data scientists are frequently compared to “Masterchefs.” He learns how to cook a tasty meal, where his essential tasks are to clean the information, prepare the components, and carefully combine them. They must consistently make high-quality meals that can satiate the demands of both clients and businesses looking to provide the greatest service in the industry.

Machine learning engineers will package, utilize, deliver, maintain, and operationalize, guaranteeing that it reaches their clients in the manner they want it to.

Machine learning vs data science salary

According to Indeed, data scientists make an average yearly pay of $102,069, while machine learning engineers make an average annual compensation of $110,819. Across various industries, including healthcare, finance, marketing, eCommerce, and more, both jobs are in demand.

Similarities: Data science vs machine learning

The fact that data science and machine learning touch the model is arguably their most related idea. The key competencies shared by both fields are:

• SQL
• Python
• GitHub
• Concept of training and evaluating data

Check out what programming language for artificial intelligence is the best

Programming comparisons focus on each person’s language to carry out their separate tasks. Whether a data scientist using SQL to query a database or a machine learning engineer using SQL to insert model recommendations or predictions back into a newly labeled column or field, both professions include some engineering.

Both disciplines necessitate familiarity with Python (or R) and version control, code sharing, and pull requests via GitHub.

For performing research on memory and size restrictions, a machine learning engineer may occasionally wish to understand the workings of algorithms like XGBoost or Random Forest, for example, and will need to look at the model’s hyperparameters for tuning. Although data scientists can create extremely accurate models in academia and business, there may be greater limitations because of time, resource, and memory constraints.

What is machine learning in data science?

Machine learning automates data analysis and generates real-time predictions based on data without human interaction. A data model is automatically created and then trained to make predictions in the present. A data science lifecycle starts when machine learning algorithms are applied.

The standard machine learning process begins with you providing the data to be studied, followed by you defining the precise features of your Model and the creation of a Data Model by those features. The training dataset that was first provided to the data model is then used to train it. The next time you upload a fresh dataset, the machine learning algorithm is prepared to predict once the model has been trained.

Let’s use an instance to grasp this better. You must have heard of Google Lens, an app that lets you take a photo of someone who, let’s say, has good fashion sense, and then it helps you identify similar outfits.

Therefore, the App’s initial task is to identify the product it sees. Is it a dress, a jacket, or a pair of jeans? The characteristics of various products are described; for example, the App is informed that a dress has shoulder traps, no zippers, armholes on either side of the neck, etc. Thus, the characteristics of a dress’ appearance are established. Now that the features have been defined, the app can make a model of a dress.

When an image is uploaded, the app searches through all of the already available models to determine what it is actually looking at. The app then uses a machine learning algorithm to create a prediction and displays comparable models of the clothing it owns.

There are various use cases of machine learning in data science:

• Fraud detection,
• Speech recognition, and
• Online recommendation engines.

Should you learn data science or machine learning first?

Big data should be the starting point for any attempt to resolve the dilemma of learning data science or machine learning.

Both data science and machine learning appear to be utilized equally in all relevant fields. In the world of technology, they are both among the most commonly used expressions. Therefore, it should be no surprise that choosing between data science and machine learning to learn first is one of the issues plaguing those pursuing careers in technology.

Using data science is a good start if you want to make future predictions. On the other hand, machine learning is the best option if you want to simplify and automate the present.

Which is better, data science or machine learning?

Over the past few years, machine learning and data science have become increasingly important, and for a good reason. The desire among engineers to learn more about these two fields grows as the world becomes increasingly automated and computerized.

As of 2022, there will be more jobs in data science than machine learning combined. You can work as a data science professional as a data scientist, applied scientist, research scientist, statistician, etc. As a machine learning engineer, you concentrate on making the models into products.

Data science is ranked #2, while machine learning is #17 in Glassdoor’s list of the top careers in America for 2021. But the pay for machine learning engineers is a little higher, and their jobs and salaries are expanding quickly. So can we say machine learning is better than data science? Let’s sneak a peek into the future for better decisions first.

According to the Future of Occupations Report 2020, 12 million new AI-related jobs will be generated in 26 nations by 2025. On the other hand, the US Bureau of Labor Statistics reveals that there will be 11.5 million jobs in data science and analytics by 2026, a 28 percent increase in positions.

Of course, it depends on your skills to find the “best”. Data science may be your ideal next step if you only have a bachelor’s degree and little training or expertise in AI or machine learning because there’s still a shortage of skilled Data Scientists. However, if you have the needed skills and background for ML, it can be better to take the pay rise and work as an ML engineer.

Data science and machine learning are interrelated. Without data, machines cannot learn, and machine learning makes data science more effective. To model and interpret the big data produced daily, data scientists will need at least a fundamental understanding of machine learning in the future.

Can a data scientist become a machine learning engineer?

Data scientists can indeed specialize in machine learning. Since data scientists will have already worked closely on data science technologies widely utilized in machine learning, shifting to a machine learning job won’t be too tough for them.

Data science applications frequently use machine learning tools, including languages, libraries, etc. Therefore, making this change does not require a tremendous amount of effort on the part of data science professionals. So, yes, data scientists can become machine learning engineers with the correct kind of upskilling training.

Conclusion

Building statistical and machine learning models is where data scientists put more of their attention. On the other hand, machine learning engineers concentrate on making the model production-ready.

Without machine learning, data science is simply data analysis. Machine learning and data science work together seamlessly. By automating the activities, machine learning makes life easier for data scientists. Machine learning will soon play a significant role in analyzing big data. To increase their efficiency, data scientists must be well-versed in machine learning.

A machine learning engineer works in the still-emerging field of AI and is paid marginally more than a data scientist. Despite this, more data science positions are available than machine learning engineering. So, choose wisely.

Why business intelligence is a must in the modern business? Business intelligence (BI) solutions can help organizations maintain their competitiveness by giving them a comprehensive view of all of their data. But it can also be challenging to comprehend exactly what BI is for individuals who haven’t adopted a tool yet or are just curious. Is it a fortune-teller? Let’s take a closer look.

Why business intelligence (BI) is needed?

The age of technological advancement is upon us. Our daily lives have undergone a complete revolution thanks to technological improvements, and the business world has been particularly affected. Businesses today have access to data-driven tools and tactics that enable them to understand more than ever about their customers and themselves. Still, not all of them are making use of them.

Businesses can benefit from business intelligence solutions to maintain a competitive edge and increase income streams. Organizations of all sizes and stages employ BI software to manage, analyze, and visualize corporate data.

Business Intelligence services are essential for modern businesses due to the numerous benefits they offer. Here are some of them:

Companies can use data analytics to define their operations, examine causes for good or bad events, produce knowledge they may not have, and receive advice on a potential course of action. These kinds of analyses, including tracking key performance indicators (KPIs) and creating precise reports, may all be done by businesses using business intelligence tools.

Analysts can put their insights into practice by using these tools to share their findings with stakeholders. As you can see, this is a crucial step for every business. Because of that, business intelligence analysts are in high demand.

A brief reminder of business intelligence will help us better understand the subject.

What is business intelligence and analytics?

To assist businesses in making more data-driven decisions, business intelligence integrates business analytics, data mining, data visualization, data tools and infrastructure, and best practices.

You can determine if your organization has modern business intelligence when you thoroughly understand its data and use it to drive change, eliminate inefficiencies, and react swiftly to supply or market changes. Flexible self-service analysis, controlled data on reliable platforms, empowered business users, and speed to insight are prioritized by modern BI solutions.

What is the purpose of business intelligence?

Business intelligence’s purpose is to use pertinent data to enhance an organization’s business operations. Businesses that use BI tools and methodologies properly can turn their gathered data into insightful information about their operational procedures and business plans.

Incorporating corporate data into user-friendly representations like reports, dashboards, charts, and graphs is what business intelligence (BI) software does. Business users can access various data types using BI technologies, including semi-structured and unstructured data like social media, historical and current data, internal and external data, and third-party and internal data. Users can examine this data to learn more about the company’s performance and make predictions. So, is it a fortune-teller? Not exactly.

How business intelligence works?

Data warehouses have always served as the foundational source of information for BI tools.

A data warehouse combines information from various sources into one main database to facilitate reporting and business analytics. The warehouse is queried by business intelligence software, which then displays the findings to the user as reports, charts, and maps.

Online analytical processing (OLAP) engines can handle multidimensional queries in data warehouses. For instance, if you wonder how are sales in our eastern and western regions compared to last year, BI is here to help you.

Some business intelligence (BI) software can link with solutions for particular industry verticals, like retail, travel, and media services. Using dashboards, intricate analytical processing, and potent visualizations, BI reporting and BI analytics may be able to assist these users in finding answers to guide their daily business. Because it enables executives to make prompt, data-supported decisions,

BI reporting is a crucial component of business intelligence.

But how exactly does business intelligence work efficiently? Most businesses have data scattered across several locations, but they cannot manage this data or combine these various data sources.

BI systems can use many data sources to quickly and accurately present information to decision-makers, eliminating the need for an IT department to run intricate reports. These data sources could come from supply chain information, marketing or sales analytics, operational performance, or customer relationship management software (such as Salesforce).

Typically, BI software can combine all of these sources to offer historical, present-day, and future perspectives that support corporate planning. Businesses should think carefully about their operations and strategies, particularly their current data integration, reporting, and information sharing procedures.

Are you looking for the best way to build your business intelligence strategies? We explain techniques, roadmap, and examples of BI strategies in this article.

Importance of business intelligence

Why is business intelligence crucial for firms in the modern era? A strong BI strategy and system should be adopted for the following primary reasons:

New customer insights

Businesses invest time, money, and resources into business intelligence since it improves their capacity to watch and assess the most recent client purchasing trends.

Your business’s bottom line will increase after you use BI to comprehend what and why your customers are buying from you. With this knowledge, you can develop new goods and improve existing ones to fulfill their wants and expectations better. It is one of the reasons why business intelligence is needed.

Better data quality

Data is rarely perfect, and there are numerous ways for discrepancies and mistakes to surface, especially when using a poorly constructed “database”.

Businesses that care about gathering, maintaining, and producing high-quality data often have greater success. 

Businesses can use BI software to combine data from many sources to get a more comprehensive picture of their operations.

Visibility

Due to a BI system’s increased visibility of these functions, business intelligent enterprises have more control over their processes and standard operating procedures. The days of reading quickly through annual reports with hundreds of pages are long gone.

Business intelligence illuminates every aspect of your company, making it easier to spot areas that need development and enabling you to take a proactive rather than a reactionary approach.

Actionable information

A powerful business intelligence system can spot important organizational trends and patterns. A BI system also enables you to comprehend the effects of different organizational processes and changes, make wise decisions, and take the appropriate action.

Efficiency improvements

BI systems contribute to increasing organizational effectiveness, which in turn raises production and potentially boosts income. Businesses may readily exchange important information across departments thanks to business intelligence platforms, reducing the time spent on reporting, data extraction, and analysis.

Organizations can minimize redundant roles and responsibilities by facilitating information sharing more effectively. This frees up employees’ time to concentrate on their work rather than processing data.

Check out how business intelligence creates collaboration in the workforce

Sales insight

Both sales and marketing teams use the Customer Relationship Management (CRM) program to keep track of their customers. CRMs are made to handle all customer interactions.

Check out the best CRM software for small businesses in 2022

There is a plethora of data and information that can be evaluated and applied to strategic goals because they house all consumer contacts and interactions. BI systems assist businesses with various tasks, including finding new customers, keeping track of and retaining existing ones, and offering post-sale services. It is one of the reasons why business intelligence is required.

Real-time data

The data is vulnerable to human mistakes and runs the risk of being out of date before it is even presented for evaluation because executives and decision-makers must wait for reports to be assembled by several departments.

Spreadsheets, visual dashboards, and scheduled emails are a few ways BI systems give customers access to data in real-time. Utilizing Business Intelligence solutions allows for large volumes’ swift and accurate assimilation, interpretation, and distribution.

Competitive advantage

In addition to all of these wonderful advantages, business intelligence may help you learn what your rivals are doing, enabling your company to prepare for the future and make informed decisions. It is one of the reasons why business intelligence is important.

Importance of business intelligence in decision-making

All the advantages mentioned above contribute to the decision-making process. Making judgments based on assumptions rather than facts is one of a business’s biggest blunders.

Businesses can use business intelligence to gather the information they need to make critical decisions, such as which goods to promote more aggressively and which to phase out. All business-related information can be gathered, including financial, production, and customer satisfaction data.

Real-world BI use cases

By giving teams the freedom to analyze their own data and come to better conclusions, BI may improve business processes by giving them a comprehensive perspective on their objectives. Now that we are aware of BI’s significance. Now let’s examine some BI real-world applications.

Amazon

Amazon employs BI technology and tools to sell things, personalize product recommendations, and make logistical business decisions. In-depth data analysis ensures the smooth operation of Amazon’s massive supply chain.

Amazon’s supply chain is significantly impacted by data and business intelligence technologies ranging from inventory allocation to delivery route optimization.

Coca-Cola

Coca-Cola gains from its social media information, which includes 105 million Facebook likes and 35 million Twitter followers. The company uses AI-driven picture recognition technology to track down when images of its drinks are shared online. This data, together with the prevalence of BI, enables the business to understand more about who is consuming its beverages, where they are, and why they are making online mentions of the brand.

Personalized advertising, which is four times more likely to be clicked on than generic advertising, is given to consumers using the data.

Netflix

This online entertainment company has a sizable BI advantage thanks to its 148 million customers. 

Netflix uses data in several different ways. The company’s method of creating and evaluating new programming concepts based on previously watched shows is the most well-known business intelligence application. Netflix uses BI to get users or customers to engage with its content.

Over 80% of all streamed content comes from the service’s recommendation algorithm since it pushes targeted content effectively.

How business intelligence systems are implemented?

Business intelligence implementation is not difficult, but it does require careful planning and strict adherence.

It is recommended that you do not neglect these steps:

• Create a plan for implementing business intelligence.
• Identify and appoint the BI implementation team.
• Define KPIs.
• Find a reliable software provider.
• Choose appropriate BI tools.
• Consider the infrastructure.
• Execute data migration (if needed).
• Create a feedback loop.
• Implement BI on a larger scale.

It is also crucial for the implementation process to distinguish between business analytics and business intelligence.

Comparison: Business intelligence vs business analytics

Business analytics, commonly known as BA, and business intelligence are closely related.

Some people think the primary distinction lies in the passage of time: business analytics aids in business planning for the future, such as by using predictive analytics to determine why things are happening and how they will develop in the future, whereas business intelligence assists in day-to-day operations and how things appear in the present.

Most people who work with business analytics have a background in math or statistics or have completed an online master’s program in the field. Experts in business intelligence should also have a strong mathematical foundation and analytical thinking.

Business analytics and business intelligence frequently function effectively together. Business analytics may continue where BI left off and find methods to enhance a company’s performance in the future because BI helps businesses manage and optimize their daily operations.

Why business intelligence is the future?

Since its inception, the business intelligence sector has advanced significantly. From its inception in the 19th century, when data-driven judgments replaced gut instincts, to on-premises databases and difficult IT reporting projects that only experts could translate in the 20th century, to the present-day self-service models we are familiar with and use.

Technology, applications, and trends in business intelligence are getting easier and easier to access.

Business intelligence is projected to become considerably more automated and aggressively used in the future, with fewer interface restrictions and barriers to the free flow of data. Future BI trends are part of a rapidly changing framework necessary for the development of contemporary businesses.

Some of the most anticipated BI trends for the future include:

• Augmented analytics,
• Natural Language Processing (NLP),
• Data cognition,
• Auditable AI, and more.

Did you notice these are all related to AI? If you wonder how AI transforming business intelligence, we have already explained it.

According to Mordor Intelligence, the Business Intelligence Market was estimated to be worth USD 20.516 billion in 2020 and is projected to increase at a CAGR of 12.5 percent over the forecast period to reach USD 40.50 billion by 2026. (2021-2026).

Is business intelligence a good career?

Like data scientists and analysts, business intelligence analysts are one of the most sought-after positions globally and sweep both the business and technology markets. The success of a company and the development of an enterprise both depend on a skilled business intelligence analyst.

Check out the most wanted business intelligence analyst skills in 2022

According to PayScale statistics, the yearly pay for a business intelligence analyst ranges from $48,701 to $93,243 in the United States, with a standard salary of $66,645 per year. We can easily assume the demand continues to rise, and business intelligence is one of the best career options in the world.

Other highly sought-after positions include some of the following:

• Blockchain engineers
• Artificial intelligence careers
• Data architect
• Cloud computing jobs
• Data engineer jobs, 
• Machine learning engineers 

Conclusion

BI enables the combination of data from various sources, analysis of the information, and dissemination of the information to pertinent stakeholders. Because of this, businesses can view the big picture and make wise business decisions.

Making every business decision always comes with certain inherent risks, but those risks aren’t as noticeable or concerning when using a solid BI system. Business Intelligent firms can advance with confidence in a world that is becoming more data-driven because they are ready to meet any challenge that may come their way. So it is not a fortune-teller; it is a fortune-maker!

Business intelligence challenges are shaped by many factors, including diverse data infrastructures, data management challenges, adaptation issues to new capabilities, and the changing data literacy levels of the workforce. Business intelligence (BI) teams must ensure that appropriate data governance and security protections are in place while they need to show how business intelligence can benefit employees, including those who aren’t experienced with data-driven approaches. Another set of business intelligence challenges centers around changes in how BI tools are used in organizations.

Traditional BI includes curated data and applications driven by IT. The traditional approach often provides business users with well-defined workflows and information through reports and custom portals. Modern BI initiatives are driven by business units that use self-service BI, data preparation, and data visualization tools to capture insights.

Common business intelligence challenges

Business intelligence challenges start with getting approval and funding for a business intelligence solution and developing a solid BI strategy that meets business needs and can deliver the promised return on investment (ROI). Alongside traditional querying and reporting, BI strategies often include mobile BI, real-time BI, augmented analytics, and other specialized applications, increasing deployment and management challenges. Decision-makers need to achieve the correct mix between governance and agility. A competitive advantage may be offered by quicker retrieval times. However, this must be worry-free about data security and privacy, as well as the possibility of business users sustaining misleading insights.

Integration of data from different sources

The growth in data sources requires many organizations to aggregate data for analysis from various databases, big data systems, and business applications, both on-premises and in the cloud. The most common way is to designate a data warehouse as a central location for BI data and distribute it from there. There are also more agile approaches: for example, using data virtualization software or BI tools to integrate data without loading the data into a data warehouse. However, this is also a complex process.

While BI tools can instantly combine data from different data sources, it still requires technical skills and understanding to make this happen. This limits scalability, increasing the time needed to analyze data and deliver BI insights. Creating a data catalog with lineage information for data sources and users can help speed up the process.

Poor data quality 

BI applications are only as effective as the accuracy of the data they are built on. But ironically, data quality is one of the most important aspects of business intelligence that is often overlooked. Before starting any BI project, users need access to high-quality data. However, many organizations in a rush to collect data for analysis neglect data quality or think they can fix errors once they have resolved data collection issues. The root cause of this fallacy may be a lack of understanding of the importance of proper data management across the organization.

Data silos (and their inconsistent data)

Silo systems are a common business intelligence problem. Data completeness is a must for using BI to accelerate and improve decision-making. However, it is difficult for BI tools to access siloed data with different permission levels and security settings. BI and data management teams must break down silos and harmonize their data to achieve the desired results. This is one of the most difficult tasks because much descriptive work involving job functions is required.

Inconsistent data in silos can produce multiple versions of information. Business users may therefore encounter different and misleading results for key performance indicators and other similarly labeled business metrics. To avoid this, it’s a good idea to start with a well-defined data modeling layer and set clear definitions for each KPI and metric.

Creating a data-driven culture

Surprisingly, one of the biggest business intelligence challenges that still persists today is the inability to reflect the data-driven culture across the organization. Building a data-driven culture is a challenge, not just at the executive level but also at the forefront where the business interacts with the world around it. Building this type of corporate culture requires organizations to be successful on two fronts: equipping employees with the right tools and empowering them to apply the insights these tools generate into business processes.

BI managers need to engage business leaders from all parts of the organization to help bring about a cultural shift that prioritizes the use of data analytics to inform decision-making. It is important to involve mid-level managers in this process to facilitate this change. 

Training and change management programs related to business intelligence initiatives require the involvement of managers to be successful. For example, developing a BI dashboard with global data on headcount, new hires and layoffs, wages, and other metrics requires working closely with the company’s HR team. This way, manual reporting processes that take hours can be automated.

Managing the self-service BI tools

Uncontrolled self-service BI deployments across different business units can lead to a chaotic data landscape with disconnected silos and conflicting analytics results for business executives and decision-makers.

Most modern BI tools have a data security architecture that protects the storage and sharing of user-generated analytics. However, it is recommended that BI and data management teams pre-arrange datasets in data warehouses or other analytical repositories to help prevent inconsistencies.

The key to enriching the self-service experience is providing these tools with access to compiled data and content that users can use to create much better data streams and mixes.

Alongside standardized metrics and dashboards, businesses need to enable users to define and publish their own metrics. For example, self-service BI users can publish dashboards with overlapping KPIs or metrics defined differently from one dashboard to another when any central governance policy does not restrict the freedom to explore data and publish findings. It should be noted that too much control can hinder analytics innovation and agility.

In addition, business intelligence tools are often modified to include custom extensions that meet specific business needs. While this is a useful capability, it hinders the ability to implement standard product upgrades. To avoid this issue, BI teams must work with end users to understand their needs and provide the necessary data and dashboards using out-of-the-box functionality.

Low adoption

End users often choose the easiest route; they want to continue using familiar tools such as Excel or SaaS applications. In other words, instead of using BI tools to analyze the data for insights, they export the data and then perform the analysis elsewhere. This end-user resistance to innovation results in suboptimal low adoption rates and unexpected usage patterns. Logs of user activities and user requests must be continuously monitored to identify potential adoption issues and issues with business intelligence tools. BI teams should also aim to deliver continuous functionality enhancements to drive user adoption.

Ineffective data visualization and dashboards

Data visualizations sometimes fail, making the information they store difficult to decipher. Similarly, a BI dashboard or report is only valuable if it is easy for end users to navigate and understand the data presented. But organizations often focus on getting their BI data and analytics processes right without thinking about design and user experience.

BI managers need to work with a UX designer from the very beginning to develop dashboards and reports with advanced features but an uncomplicated interface. BI teams should encourage successful data visualization design practices in self-service BI environments. These steps are especially important for mobile BI applications on smartphones and tablets with small screen sizes.

Data science techniques, applications, and tools allow organizations to extract valuable insights from data.

The evolution of data science and advanced forms of analytics has created significant change for companies. The conditions were created for the emergence of various applications that provide deeper insights and business value.

While data science was once considered the risky and even more nerdy side of IT, it has now become the cornerstone of the working principles of any organization.

How are data science techniques used today?

Modern data science techniques offer the capabilities needed to crunch and analyze large data pools for a wide variety of applications, including predictive modeling, pattern recognition, anomaly detection, personalization, speech-based AI, and autonomous systems.

Many organizations today rely on data science-based analytics applications, mostly focused on areas that have proven their worth over the past decade. By using the power of data, organizations can gain competitive advantages against their competitors, serve their customers better, and gain the ability to react more effectively to rapidly changing business environments that require constant adaptation.

Let’s take a closer look at the most popular data science techniques that have already become the cornerstone of the business world:

Anomaly detection

Anomaly detection, one of the most popular data science techniques, uses statistical analysis to detect anomalies in large data sets. While it is a simple practice to fit data into clusters or groups, then spot outliers when dealing with small amounts of data, this task becomes a real challenge when it is petabytes or exabytes.

Financial services providers, for example, are finding it increasingly difficult to detect fraudulent spending behavior in transaction data, which continues to grow tremendously in volume and diversity. Anomaly detection applications are also used to eliminate outliers in datasets to increase analytical accuracy in tasks such as preventing cyber-attacks and monitoring the performance of IT systems.

Pattern recognition

Recognizing repetitive patterns in datasets is a fundamental data science task. For example, pattern recognition helps retailers and e-commerce companies detect trends in customer purchasing behaviors. Organizations need to make their offerings more relevant and ensure the credibility of their supply chain to keep their customers happy and prevent customer churn.

Giant retailers serving tens of millions of customers today have long used data science techniques to discover purchasing patterns. In one of these studies, a retailer noticed that many customers shopping in anticipation of a storm or tropical storm bought a particular brand of strawberry biscuits and took advantage of this invaluable information to change its sales strategy. This resulted in increased sales. Such unexpected correlations are made possible by recognizing data patterns. The insights created from data help create more effective sales, inventory management, and marketing strategies.

Pattern recognition also helps improve technologies such as stock trading, risk management, medical diagnosis, seismic analysis, natural language processing (NLP), speech recognition, and computer vision.

Predictive modeling

Data science makes predictive modeling more accurate by detecting patterns and outliers. While predictive analytics has been around for decades, data science techniques today create models that better predict customer behavior, financial risks, and market trends. It also applies machine learning and other algorithms to large datasets to improve decision-making capabilities.

Predictive analytics applications are used in various industries, including financial services, retail, manufacturing, healthcare, travel, utilities, and many others. For example, manufacturers use predictive maintenance systems to help reduce equipment failures and improve production uptime.

Aircraft manufacturers rely on predictive maintenance to improve their fleet availability. Similarly, the energy industry is using predictive modeling to improve equipment reliability in environments where maintenance is costly and difficult.

Organizations are also leveraging the predictive ability of data science to improve business forecasting. For example, formulaic approaches to purchasing by manufacturers and retailers have failed in the face of the sudden shifts in consumer and business spending caused by the COVID-19 pandemic. Innovative companies have overhauled these fragile systems with data-driven forecasting applications that can better respond to dynamic customer behaviors.

Recommendation engines and personalization systems

Customers are very satisfied when products and services are tailored to their needs or interests and when they can get the right product at the right time, through the right channel, with the right offer. Keeping customers happy and loyal gives them enough reasons to choose you again. However, tailoring products and services to the specific needs of individuals has traditionally been very difficult. It used to be a very time-consuming and costly task. This is why most systems that customize offers or recommend products need to group customers into clusters that generalize their features. While this approach is better than no customization, it is still far from optimal.

Fortunately, combining data science, machine learning, and big data allows organizations to build a detailed profile of individual customers and users. Systems can learn people’s preferences and match them with others with similar preferences. This is the working principle of the hyper-personalization approach.

Popular streaming services, as well as the largest retailers today, are using data science-driven hyper-personalization techniques to better focus their offerings on customers through recommendation engines and personalized marketing. Financial services companies also offer hyper-personalized offers to clients, while healthcare organizations use this approach to provide treatment and care to patients.

Investing heavily in its recommendation engine and personalization systems, Netflix uses machine learning algorithms to predict viewer preferences and deliver a better experience. The streaming service uses a recommendation engine that influences critical data touchpoints such as browsing data, search history, user ratings on content, and device information to provide customers with relevant recommendations through a hyper-personalized homepage that differs for each user.

Emotion, sentiment, and behavior analysis

Data scientists probe data stacks to understand the emotions and behaviors of customers or users using the data analysis capabilities of machine learning and deep learning systems.

Sentiment analysis and behavioral analysis applications allow organizations to more effectively identify customers’ buying and usage patterns, understand what people think about products and services, and how satisfied they are with their experience. These approaches can also categorize customer sentiment and behavior and reveal how they change over time.

Travel and hospitality organizations are developing strategies for sentiment analysis to identify customers with very positive or negative experiences so they can respond quickly. Law enforcement also uses emotion and behavior analysis to detect events, situations, and trends as they arise and evolve.

Classification and categorization

Data science techniques effectively sort large volumes of data and classify them according to learned features. These capabilities are especially useful for unstructured data. While structured data can be easily searched and queried through a schema, unstructured data is very difficult to process and analyze. Emails, documents, images, videos, audio files, texts, and binary data are unstructured data formats. Until recently, searching this data for valuable insights was a huge challenge.

The advent of deep learning, which uses neural networks to analyze large data sets, has made it easier for organizations to perform unstructured data analysis, from image, object, and voice recognition tasks to classifying data by document type. For example, data science teams can train deep learning systems to recognize contracts and invoices among document stacks and identify various types of information.

Government agencies are also interested in classification and categorization practices powered by data science. A good example is NASA, which uses image recognition to reveal deeper insights into objects in space.

Chatbots and voice assistants

One of the earliest applications of machine learning was the development of chatbots that could communicate like real humans without any intervention. Designed by Alan Turing in 1950, the Turing Test used the speech format to determine whether a system could mimic human intelligence. So it’s hardly surprising that modern organizations are looking to improve their existing workflows by using chatbots and other conversational systems to delegate some tasks previously handled by humans.

Data science techniques have been extremely useful in making speech systems useful for businesses. These systems use machine learning algorithms to learn and extract speech patterns from data. Powered by advanced natural language processing technology, chatbots, smart agents, and voice assistants now serve people everywhere, from phones to websites and even cars. For example, it provides customer service and support to find information, assist with transactions, and engage in both text-based and voice-based interactions with people.

Autonomous systems

Speaking of cars, one of the dreams that the artificial intelligence field has been trying to achieve for a long time is driverless vehicles. Data science plays a huge role in the ongoing development of autonomous vehicles, as well as AI-powered robots and other intelligent machines.

There are numerous challenges in making autonomous systems a reality. In a car, for example, image recognition tools must be trained to identify all elements. The list goes on and on, in the form of roads, other cars, traffic control devices, pedestrians, and anything else that can affect a safe driving experience. Moreover, driverless systems must know how to make snap decisions and accurately predict what will happen based on real-time data analysis. Data scientists are developing supporting machine learning models to help make fully autonomous vehicles more viable.

Business intelligence reporting, often known as BI reporting, is a function that becomes more important every year. A broad definition of business intelligence reporting is the process of preparing and analyzing data using a BI tool in order to discover and communicate insights that can be put to use. Users of BI reporting can thus make better decisions and have better business performance.

The rise in popularity of BI reports across almost all industries is being fueled by the constant technological advancements that enable working with massive data sets accessible to more professionals. Business intelligence analyst abilities are at the forefront of business intelligence strategies as well as business intelligence reporting. So, let’s get down to business.

What is a business intelligence report?

The process of receiving or distributing information or reports to end-users, companies, or applications through BI software or a solution is known as business intelligence reporting (BI reporting).

BI software often has this feature for generating streamlined and organized reports for operations or analysis carried out on one or more sets of data. Business intelligence reporting is one of the 10 ways to use business intelligence software in your organization.

Big data is essential for corporate data, online data analysis, and intelligent reporting. Companies in our fast-paced digital age must adapt to the complexity of data and take appropriate action. The solution is business intelligence reporting.

Spreadsheets are no longer sufficient for a modern organization that wants to precisely analyze and use every piece of information received.

With the aid of the latest BI reporting solutions, reporting in business intelligence offers the opportunity to construct a thorough intelligent reporting practice. Therefore, BI can improve a business’ overall development as well as its profitability, regardless of its industry or specialty.

Reporting on business intelligence can be categorized in numerous ways. One way to distinguish between reporting types is to look at who is generating the report: controlled reporting is done by developers and other technical staff, whereas ad-hoc reporting is done by non-technical end users. The most significant components of a report, such as data tables, cross-tab reports, visualization elements, etc., can be used to classify reporting in another manner.

Types of reports in business intelligence: How many types of business intelligence reports are there?

Now that you are aware of the fundamental features of a BI report, it is crucial to realize that this is merely the first step in the reporting process. There are many different types, each with a unique use case and set of data requirements.

In order to give you an idea of the type of BI reporting you might be performing, we’ll discuss some of the various BI report types here.

Performance management BI reporting

Performance management BI reporting can be helpful in revealing information on how well people, teams, or departments performed within an organization. Since performance ultimately decides profit, senior management should pay particular attention to these business intelligence reports. However, this kind of BI reporting takes into consideration aggregate data that measures a range of KPIs and goes beyond just financial figures.

The fact that such reporting is real-time enables companies to make any necessary adjustments to their operations before subpar performance results in serious harm.

Beyond any short-term course corrections, however, performance management BI reports also help management make broad strategic decisions that have an impact on the company’s future. This is due to the fact that they frequently can determine what is responsible for the company’s success.

Is one aspect of the business wildly more successful than the rest? Are there any areas of the firm where more money should be spent or relinquished in an effort to boost productivity? These are the types of inquiries that performance management reporting aids in addressing.

Predictive analytics BI reporting

Business intelligence that goes beyond BI reporting is known as predictive analytics. It offers accurate forecasts based on recent and old data. Tools for predictive analytics are highly desired since they significantly reduce uncertainty in decision-making.

Although this kind of BI report is frequently used to forecast how events will turn out in the future, it can also be used to identify the likely reason why prior events occurred, making it a very effective BI reporting tool.

Predictive models are used in this sort of data reporting in business intelligence to find patterns and linkages in the data that highlight dangers and commercial possibilities. It accomplishes this by calculating the statistical likelihood of a particular result for a particular judgment.

Predictive analytics is particularly helpful for novice business owners because they are often less confident in their strategic planning abilities.

Predictive analytics BI reporting has numerous uses in both large and small businesses, including customer relationship management, the verification of court judgments, and consumer credit rating.

Augmented analytics BI reporting

In augmented analytics, which is frequently referred to as the future of BI reporting, data preparation and discovery are automated using AI and ML. These methods cut out some of the procedures that data scientists must-do during the data analytics process.

By lowering the possibility of human mistakes or potential biases during the data preparation process, such processes give firms access to even more precise insights than they previously had.

Although this kind of BI reporting is still in its infancy, there is a wide range of potential applications across numerous industries.

Business intelligence reporting benefits

An organization can benefit in a number of ways if reporting is handled effectively and strategically. The primary objective of BI reports is to give complete data that is simple to obtain, understand, and generate insights that can be put to use.

Let’s explore the significant advantages:

• Increasing the rate of work progress.
• Implementation to any sector or division.
• The use of both current and past data.
• Customer research and behavior forecasting.
• Operational planning and optimization.
• Cost optimization.
• Strategic decision-making using knowledge.
• Simplified purchasing procedures.
• Better data quality.
• Management of human resources and employee performance.

BI reporting best practices

• Reports should be written with analysis, the following step, in mind. Make sure to format your data in a way that makes it easy for users to quickly and intuitively analyze it, and give your columns names that are both consistent and user-friendly.
• Avoid overwhelming the end user with too many objects if you are the report developer or system administrator. Work with them to determine their needs, and provide them with a report that is as clear and uncluttered as feasible by choosing the elements that end users can view and report on with care.
• Use role-based security, authentication, and authorization to provide or prohibit access to reports, columns, and records to specific individuals or user groups while being cautious of sensitive data.
• Utilize the capabilities of the Web to build reports that are strong, interactive, and simple to use while putting as little strain as possible on the infrastructure.
• The data sources that are used the most in your business should be placed underneath your reporting layer. In this way, a reporting solution that is data-source neutral enables you to combine information from both conventional and unconventional data sources, including databases, Web services, RSS feeds, Excel, etc.

How do you gather reporting requirements in business intelligence?

You should take into account the following three techniques as you gather your business intelligence requirements.

Pain Method (Covers the past)

• Mindset: Individuals are constantly having problems obtaining the information they need.
• How it works: Ask the end users how they would want to be helped after letting them vent about their informational aches. They can also turn their problems into requirements with the help of business analytics.
• Restrictions: Users are constrained by the present circumstances.

Need Method (Covers the present)

• Mindset: I require XYZ to complete my task. I absolutely need XYZ and cannot live without it.
• How it works: Ask; What data and data artifacts are necessary for you to carry out your tasks? What if those components were removed?
• Restrictions: Need mode requirements frequently have a strong operational focus. They don’t offer creative problem-solving.

Dream Method (Nice to have; covers future needs)

• Mindset: Allow consumers to use their imaginations to the fullest. Nothing is off limits.
• How it works: Users should be asked to describe their desired future state and how analytics may help them realize it.
• Restrictions: Not all aspirations can come true. The dreams might never come true due of limitations.

Business intelligence report example

According to Databox, this is a business intelligence report example:

Department-segmented business intelligence report

Coalition Technologies‘ Jordan Brannon writes, “We have distinct metrics for each department, thus we split reports for each department.”

Therefore, their dashboard for daily reports displays the information that Brannon shares:

• “Monthly Revenue and Expenses
• Trailing Twelve Months (TTM) Revenue
• TTM Revenue Retention
• Monthly New Sales Revenue
• Monthly Revenue Retention in %
• Monthly Revenue per Service Line
• Accounts Receivable
• Applicants, New Hires, Terminations
• Lead Volume”

Keep in mind that these metrics are beneficial for several departments.

For instance, the accounts receivable data is useful for the finance department, but since it is on the same dashboard, other departments can examine it as well (if necessary).

The same is true for lead volume, which is crucial information for the sales department but may also be viewed by other stakeholders to get a fast sense of how strong the sales funnel is.

3 best business intelligence reporting tools

To start, with the help of these technologies, anyone can now perform data discovery, which was previously only possible with the knowledge of experts in advanced analytics. Additionally, these technologies provide you with the information you need to accomplish goals like growth, deal with immediate problems, gather all of your data in one location, estimate future results, and much more.

SAP Business Objects

A business intelligence tool called SAP Business Objects provides in-depth reporting, analysis, and interactive data visualisation. The platform places a lot of emphasis on areas like digital supply chain, ERP, and customer experience (CX) and CRM. The self-service, role-based dashboards that this platform offers are particularly nice because they let users create their own dashboards and applications. SAP is a powerful program with a wealth of features that is designed for all jobs (IT, end users, and management). However, the product’s complexity does increase the price, so be ready for that.

Datapine

Even for non-technical people, Datapine’s all-in-one business intelligence platform simplifies the challenging process of data analytics. Data analysts and business users may both easily connect various data sources, do complex data analysis, develop interactive business dashboards, and produce useful business insights thanks to datapine’s solution, which employs a full self-service analytics strategy.

MicroStrategy

An enterprise business intelligence tool called MicroStrategy provides hyperintelligence, cloud solutions, and sophisticated (and quick) dashboarding and data analytics. Users may recognize new opportunities, spot trends, increase productivity, and more with this service. Whether the incoming data is via a spreadsheet, cloud-based service, or enterprise data program, users can connect to one or more sources. You can access it from a PC or a mobile device. However, setup can be complicated and need a lot of application knowledge from a variety of parties.

If you want to learn more, go to the article that we explained the top 20 BI tools.

Why business intelligence is so important?

A business intelligence system always offers real-time data. As a result, there is less chance of human error while producing important data reports. The business can always be aware of how the company is doing thanks to access to real-time data.

Conclusion

With the global market predicted to reach $33.3 billion in value by 2025, business intelligence is expected to become more and more significant. This amounts to a CAGR of 7.6%, which is admirable for the post-pandemic era.

It’s important to keep in mind that business intelligence reports democratize data by offering insights to stakeholders with minimum technical experience. As a result, your company is able to make more informed, data-driven decisions at every level of management.

Additionally, all businesses have data that can be gathered. Just the creation of procedures for gathering the data is needed. Plug-and-play data dashboards can then be used to quickly visualize the data when this is done.

So why are you still waiting? Start looking at the business intelligence (BI) solutions that could help your company today!

Check out our best business intelligence companies list for those wanting to explore the BI landscape. The BI market is brimming with specific concepts and designs created to meet evolving company demands in unique ways. It is a fast-growing industry with an often bewildering number of suppliers and solutions. Spreadsheets have been completely phased out in the modern business intelligence space. They instead utilize new technologies like SQL databases, cloud platforms, and machine learning to assist organizations in making more self-aware, evidence-based choices. So what are the business intelligence companies that offer these solutions in the best way? Let’s take a closer look.

Biggest business intelligence companies (2022)

In light of the continuing COVID-19 epidemic, research reveals that businesses must quickly adapt or utilize their BI tools and data analytics techniques. AI systems and agile risk management help them make informed decisions while coping with the serious impact of the international health emergency.

Reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics are just a few examples of corporate BI tools. These are done using a variety of applications such as spreadsheets, reporting, and query software; online analytical processing; digital dashboards; data mining, data warehousing, decision engineering; process mining, business performance management, and local information systems. So, who makes them, and which are the biggest business intelligence companies?

Ernst & Young

Ernst & Young, a multinational professional services firm, provides assurance, auditing, technology and security risk consulting, enterprise risk management services, transaction support, merger, and acquisition advice, actuarial services, and real estate advisory services. The company also offers employee benefit plans (TUP), taxation, and entrepreneurial solutions. Ernst & Young serves the telecommunications industry; energy; insurance; consumer products and retail; health care; automobile manufacturing; power generation, and utility industries. It is one of the biggest business intelligence companies.

• HQ: London, GB
• Employees: 328,719

NTT Data

NTT Data is a Japanese information technology services company providing consulting. It provides digital strategy, process optimization, business intelligence strategy, organizational change management, and program management office consultancy, as well as infrastructure services, including data center modernization, infrastructure consulting, infrastructure management, managed hosting, and managed security protection consulting. NTT Data provides businesses with performance management, governance, risk, compliance, data warehousing, analytics, predictive analytics, data mining, and information management solutions.

• HQ: Tokyo, JP
• Employees: 139,500

Aon

Aon is a professional services firm that provides various risk, retirement, and health solutions. It sells commercial risk solutions, including risk advising, risk transfer, and structured solutions; reinsurance solutions such as risk transfer and claims advocacy; and capital management solutions for retirement plans.

• HQ: London, GB
• Employees: 50,000

Hinduja Global Solutions (HGS)

Hinduja Global Solutions (HGS) is a business process management firm. It combines automated, analytical, and digital services with domain expertise in back-office processing, contact centers, and HRO solutions to provide clients with transformative impact.

• HQ: Bengaluru, IN
• Employees: 41,110

RELX

RELX is a business that provides data and analytics to professionals and businesses. It has four divisions: Scientific, Technical, Medical, and Risk. 

• HQ: London, GB
• Employees: 33,220

Thomson Reuters

Thomson Reuters is a business information services firm. The company has five main divisions. 

Law firms and governments use research and workflow solutions provided by the Legal Professional segment. Solutions for corporations are provided through the Corporates division, which offers legal, tax, regulatory, and compliance products. Tax and accounting experts use research and workflow solutions geared toward easy tax preparation and tax procedure efficiency to save clients time. The Reuters News section offers news on business, finance, and international topics. The Global Print sector provides legal and tax information in print to clients worldwide.

• HQ: Toronto, CA
• Employees: 24,400

Business analytics companies in USA

If you’re wondering which business intelligence firms operate in the United States, we have already compiled a list for you.

Barrett Business Services

Barrett Business Services is a management consulting firm that offers services to small and medium-sized enterprises. The company’s management platform combines knowledge-based techniques from the management consulting industry with human resources outsourcing technology. Businesses seeking help with the administration of their employees might benefit from hiring an employment agency. This company handles all aspects of payroll, including payroll taxes and workers’ compensation insurance. Barrett Business Services also provides recruitment and staffing services. It is one of the biggest business intelligence companies in the USA.

• HQ: Vancouver, WA, US
• Employees: 127,085

Startek

Startek is a firm that offers customer experience (CX) management solutions, omnichannel CX, digital transformation, and enterprise tech services. It provides various services, including customer engagement, omnichannel engagement, social media, consumer intelligence analytics, remote work, and so on.

• HQ: Denver, CO, US
• Employees: 45,000

McKinsey

McKinsey & Company is a management consulting business that offers analysis, client education, digital services, implementation, recovery, transformation, and other functional services in the areas of business technology, corporate finance, marketing and sales, operations, and organization risk management strategy sustainability resources. The firm also provides insights and publications such as articles, white papers, and reports.

• HQ: New York, NY, US
• Employees: 38,175

ExlService Holdings

XL Group engages in the following business processes outsourcing: transaction processing and Internet and voice-based customer care services. The company also provides technical support and advisory services.

• HQ: New York, NY, US
• Employees: 31,700

GroupM

GroupM is a media investment management company that operates across the world. It runs media agencies. The [m]PLATFORM is an audience intelligence and activation solution that creates mass-scale personalized consumer relationships.

• HQ: New York, NY, US
• Employees: 28,006

Top business intelligence companies (2022)

We reviewed the biggest ones, now let’s take a look at some the most preferred business intelligence companies.

MicroSoft

Microsoft‘s major business intelligence solutions include Excel, SQL Server, SharePoint, and Power BI.

Excel enables users to discover, analyze, and visualize data in a powerful self-service manner. SharePoint allows for document sharing and collaboration while keeping reports and data secure. SQL Server Reporting Services delivers operational reporting in a browser-based environment, pixel-perfect printing, ad hoc data exploration/visualization, and more. Customers may use Microsoft Power BI, cloud-based software as a service self-service business intelligence solution for nontechnical company employees. Customers can keep track of their company’s health using live operational dashboards with just any browser or a Power BI mobile app. It is one of the best business intelligence companies.

Domo

Domo is a Utah-based technology company that aims to help businesses connect and engage their employees. Domo solves this problem by integrating the company and its data into its simple platform, which is headquartered in American Fork, Utah. Domo allows any user, from the CEO down to a contributor, to see whatever information they want, how and when they want it, regardless of format or source.

Logi Analytics

Logi Analytics is a Virginia-based firm that provides Web-based business intelligence (BI) reporting and analysis tools. Logi Info, Logif vision, and Logi Adhoc are all part of the portfolio.

Logi Info is an online log aggregator that provides dashboards, reports, and analytics to produce feature-rich data visualizations and deploy a single app to many desktop and mobile platforms. Logi Vision is a web-based data discovery tool that allows you to collect data, analyze it, create visual representations of your findings, and communicate them with others. Logi Ad Hoc is a ready-made reporting application that may be quickly linked with your most important data sources.

Pentaho

In addition to their proprietary Business Intelligence tools, the Orlando-based Pentaho portfolio has a collection of open source Business Intelligence solutions called Pentaho Business Analytics, which integrates data.

The Pentaho 5.0 platform is an open, unified architecture that allows users to access, connect, and mix any data in any setting across a broad range of analytics.

Targit

Targit is a Danish software development firm that develops business intelligence and analytics programs with subsidiary offices in the United States. The Targit Decision Suite is part of the company’s portfolio. Targit is the world’s leading BI vendor for companies using Microsoft Dynamics NAV or AX.

TARGIT Decision Suite is a BI platform that combines data discovery tools, self-service analytics, reporting, and dashboards in a single package.

Birst

Business intelligence and analytics provider Birst, based in San Francisco, provides a private or public Cloud solution.

Birst Discovery Edition provides intuitive data exploration for business users and analysts. Birst Enterprise Software Suite is a collection of analytic applications developed to meet the reporting demands, including pixel-perfect reporting, visual exploration, ad hoc analysis, dashboards, and mobile analytics.

Prognoz

Prognoz is a Russian firm developing business software in the business intelligence and business process management markets.

The Prognoz Platform supports the creation of program solutions for desktop, internet, and mobile platforms that allow for the visualization and data analysis (or OLAP), reporting, and modeling and forecasting of business processes. The platform includes self-service BI capabilities that let business users modify software without IT assistance.

Bitam

Bitam is a Business Intelligence vendor from Roswell, Georgia.

The Bitam BI solution gives organizations simple dashboards, analysis, reports, and alerts while also allowing access to data from various sources with the clarity and detail needed to run operations efficiently.

Oracle

Oracle is a company that produces Oracle Business Intelligence Enterprise Edition. Oracle Business Intelligence Enterprise Edition is a comprehensive business intelligence solution that provides a wide range of features, including interactive dashboards, ad hoc queries, notifications, alerts, and enterprise and financial reporting. It also has scorecard and strategy management capabilities.

IBM

One of the most popular and successful business intelligence solutions today is based on IBM’s analytics platform, which is designed to connect with one another and a variety of third-party solutions, including big data platforms. These are Cognos BI and Cognos Insight. It is one of the best business intelligence companies.

MicroStrategy

MicroStrategy is a firm based in Washington that offers business intelligence, mobile software, and cloud-based services. MicroStrategy Analytics is their core offering.

Best business intelligence tools/services/software/solution (2022)

It’s time to review the BI tools offered by the companies above and more. If you wonder how to use business intelligence software or how business intelligence creates collaboration, we have already explained.

Tableau

Tableau focuses on creating beautiful visualizations, but the bulk of its advertising is aimed at corporate environments with data experts and larger budgets. The software comes in a public (free) version, but it has restricted functions. The more you spend on Tableau, the more features you can use, including third-party benchmarked data. There are also versions for academic institutions available. It is one of the biggest business intelligence companies that offer the best solutions.

Sisense 

Sisense is a business intelligence (BI) software that helps firms of all sizes. Sisense is one of the few BI software systems that work with non-technical people to allow them to combine many data sources, create dashboards, generate graphical representations, and share them with other individuals. This web-based BI solution allows organizations to integrate their data into a single centralized location without needing hardware or IT staff.

SAP BusinessObjects

SAP BusinessObjects Business Intelligence (BI) is a centralized data reporting, visualization, and sharing suite that runs on the SAP Business Technology Platform. It transforms raw data into valuable insights that may be accessed anytime and anywhere using the SAP BusinessObjects Business Intelligence (BI) suite as the on-premises BI layer for SAP’s Business Technology Platform. The Analytics Platform can help businesses grow with its adaptable architecture.

SAS

SAS Analytics is a business intelligence (BI) software that can discover patterns and anomalies in data, identify relationships and other variables, and forecast future events. Users of SAS Analytics will benefit from making better-informed business decisions based on company data and market trends. Data mining, data visualization, text analytics, forecasting, statistical analysis, and more are available through SAS Analytics. It is one of the biggest business intelligence companies that offer the best solutions.

Looker

Looker is a business intelligence and data visualization solution designed for businesses with an established data analytics team. Business users may combine, drill down, and analyze their company’s data in real-time in dashboards and reports using LookML, which they created with Looker’s LookMLeditor. Users can use natural language that fits the company’s custom LookML settings to create their reports because Looker connects to current corporate databases and keeps data up to date.

Qlik

Qlik Sense is Qlik’s next-generation self-service analytics platform. It supports various analytics use cases, including self-service visualization and exploration, guided analytics applications and dashboards, custom and embedded analytics, mobile analytics, and reporting within a controlled multi-cloud architecture. Analytics capabilities are available to all types of users, such as associative exploration and search, smart visualizations, data preparation, and more.

BOARD

The ‘toolkit’ design of the ‘BOARD‘ software allows customers to create their own BI and CPM solutions without spending time or money on programming. Customers may use the intuitive drag-and-drop feature to construct reports, from simple reports and dashboards to highly sophisticated performance management applications. The segments are automatically updated in real-time with data as it changes.

Best business intelligence websites (2022)

According to Gartner, business intelligence (BI) is “the applications, infrastructure and tools, and best practices that enable access to and analysis of data.” Because of this broad definition encompasses many different topics such as data security, big data analytics, and so on.

Whether you’re just starting business intelligence or already breathing it, here are some sites you’ll want to bookmark. You can follow the latest news about business intelligence companies news with these websites.

• Dataconomy
• Datanami
• JenUnderwood
• Technologyreview
• Gartner
• Forrester
• Capterra

Websites for BI tools comparison

The number of business intelligence platforms, tools, and software stacks is overwhelming. We’ve compiled a list of internet resources where you can compare business intelligence solutions and suppliers to help individuals who are presently shopping for business intelligence tools or anybody looking to get a deeper understanding of the field.

• Gartner Peer Insights
• G2 Crowd
• Capterra
• GetApp
• Finances Online

What is business intelligence in a company?

BI is a technology-driven method for analyzing data and providing useful information to management, supervisors, and staff so that they may make well-informed business decisions.

Business intelligence analyst skills are at the forefront of BI plans, especially in preparing business intelligence strategies. Business intelligence (BI) is a collection of tools and techniques that allow businesses to make more data-driven decisions. It combines business analytics, data mining, data visualization, data tools, and best practices in order to assist organizations in making more data-driven judgments.

What kind of companies use business intelligence systems?

BI solutions are suitable for organizations that need their workers to make quick choices that most benefit from business intelligence. As a result, it’s a great place for startups, data-centric firms, and businesses wanting to expand rapidly.

Business intelligence real life examples

BI is often used by the most successful firms to generate revenue, customer loyalty, operational efficiency, ad distribution, capital appreciation, share price growth, and develop new business possibilities.

American Express

In the finance sector, business intelligence is extremely important. American Express has been using IT to develop new payment service offerings and market deals to clients in the financial sector. American Express investigations into the Australian market have allowed it to identify up to 24% of all Australian users who will close their accounts within four months. To retain clients, American Express takes action based on this data. BI also aids the firm in detecting fraud and protecting customers from card data breaches.

Coca-Cola

Coca-Cola gets value from its social media data, with 35 million Twitter followers and 105 million Facebook fans. Coca-Cola utilizes AI-powered image recognition technology to identify photographs of its beverages that are posted online. This data, combined with the power of BI, provides the firm with critical knowledge into who is drinking their products, where they are, and why they mention the brand on social media. The data aids in producing more targeted advertising for customers, which is four times more likely than a general ad to result in a click.

Netflix

With over 148 million subscribers, Netflix has a big BI edge. What is the use of business intelligence for Netflix? With what purposes does Netflix make use of information? The organization employs data in a variety of ways. For example, as a result of last viewed shows, the firm develops and validates unique program concepts based on previously watched programs. To get consumers to interact with its content, Netflix greatly utilizes business intelligence. The recommendation system in the service is so effective at targeted content promotion that it drives over 80% of streamed material.

Starbucks

Starbucks has access to a wealth of consumer information through its popular loyalty card program and smartphone app. The firm uses data and BI tools to forecast purchases and send personalized offers via the app and email-based on this information. This method attracts current customers into its stores more frequently while also boosting sales volumes.

Tesla

Several of the world’s most innovative automobile manufacturers are teaming up with BI to link their automobiles wirelessly to their corporate headquarters for data gathering and analysis. This technique connects the carmaker to the consumer and anticipates and solves problems like component damage, traffic, or road danger data, resulting in a high customer satisfaction rating and more informed product decisions.

Twitter

To combat unlawful and potentially harmful content on its platform, Twitter uses BI with AI. Algorithms rather than people discover ninety-five percent of suspended terrorist-related accounts.

An AI-based campaign management solution, like Twitter’s Machine Learning platform, uses machine learning to support fine-tuning. The use of artificial intelligence (AI) and BI also allows for deeper user customization. Within the Twitter organization, human resources personnel and its business intelligence tools monitor live video feeds and categorize them based on the theme. They utilize this information to improve search capabilities and assist algorithms in recognizing videos that people might be interested in viewing.

Uber

To improve its core functions in various ways, Uber uses business intelligence to assess numerous fundamental aspects of its organization. Surge pricing is one example. Traffic conditions, journey times, driver availability, and consumer demand are all monitored in real-time by algorithms, which means prices change as demand rises and traffic conditions change. Dynamic pricing in real-time is similar to what airlines and hotel chains use to alter prices dependent on need.

Conclusion

BI tools may have a huge positive impact on your company. They can assist you in improving inventory management, managing your supply chain, detecting and eliminating bottlenecks in your operations, and automate routine tasks. However, BI tools require data to be centrally stored in several separate systems to be most effective.

Your firm likely has a lot of data that could be utilized to increase its profitability. The problem is putting your information in such a way that you can extract insights from it. After that, create clear, concise, actionable reports and data visualizations and distribute them to key team members. Advanced software like ERP systems is required for any of this work to succeed, and there are a lot of business intelligence companies for it. So, choose wisely!

Spotify analytics for listeners is how the popular streaming service also captures the hearts of its users. Many websites allow you to check these analytics if you have ever wanted to delve deeper into your listening habits — maybe to see which songs you listen to the most or compare your preferences with others. These sites range from academic to comical, but they’ll all assist in illuminating your music appreciation in a new light. Spotify analytics for listeners is also a good example of machine learning benefits. These websites use ML and AI for analysis. Additionally, did you know Spotify is using deep learning to recommend songs you’ll love?

Spotify analytics for listeners

Are you curious about your Spotify listening habits? Do you want to know how popular or obscure your music taste is, or do you need some help organizing your playlists by finding useful information about the music you listen to?

You may accomplish a lot more with some fantastic websites and applications that have been created, especially to give you a thorough look at your Spotify listening habits.

These are some of the best Spotify analytics for listeners:

• Spotify Wrapped
• Stats for Spotify
• Obscurify
• Skiley 
• Spotify Charts
• Discover Quickly
• MusicScape
• Visualify
• Last.fm
• Run BPM

However, there is a catch. To access Spotify data, each website requires you to log in to your Spotify account and grant permission. So, if that’s not something you’re comfortable with, you might want to skip it.

You may remove access to any third-party app or site you’ve allowed by going to your Spotify app settings and removing the Remove Access option beside each application or site on the Apps page.

If you’re fine with it, let’s look at the top Spotify analytics for listeners’ services.

Spotify Wrapped

Let’s start with the most well-known and official example.

Spotify annually releases Spotify Wrapped, a slideshow in which you may view who your favorite musicians, songs, and genres were over the previous year. You also get a list of how long you spent listening to Spotify and a playlist containing your top 100 songs.

There’s nothing you need to plug into your Spotify account to discover this. When it debuts, the slideshow will show up in your Spotify app automatically, so you can see it and share it with all of your friends.

After a while, the Wrapped slideshows will be gone. However, you can locate any of your Wrapped playlists from years past by visiting the Spotify website and looking for these links:

• Wrapped 2021
• Wrapped 2020
• Wrapped 2019
• Wrapped 2018
• Wrapped 2017

Stats for Spotify

Spotify’s most basic “stats” resource is probably Stats for Spotify. From your Spotify account, you may view your favorite artists, songs, and genres from the previous month, six months, or throughout your whole listening history.

You may also jump straight to the bottom of your Top Tracks list and discover a Create playlist button that instantly lets you put all the songs on that list into a playlist.

Obscurify

Obscurify compares your score to that of other users in your area and gives you a personalized list based on their popularity. It also provides a comprehensive top 10 and a ranked list of your favorite genres.

This excellent website can generate a playlist with your top songs and automatically add it to your library. This is a great aid when you’re running out of ideas.

Skiley 

Skiley is a handy web app that collects data about your listening habits, manages your playlist by artists, genre, beats per minute, or mood, and finds new tunes appropriate for your music taste. Moreover, it provides other helpful information such as song lyrics and translations.

Spotify Charts

If you’re looking for ideas on what to listen to next, go beyond your music library and check out Spotify Charts, a website that displays the most popular songs on the site.

You may look at the Top200 or the Viral50, allowing you to filter the results by region and time. You can also export the data as a CSV file. It’s useful!

It also has a feature that narrows down your top tunes regionally and globally, according to your current needs.

Discover Quickly

Quickly sorts your playlists, top songs, and top artists by various criteria, such as popularity and danceability. It also contains all of Spotify’s wacky specific genres, including deep metalcore, acid house, and charred death. If you choose a genre, it will create a playlist of music from that genre for you. You may also pick “random genre” to generate a playlist with songs from a random category.

MusicScape

MusicScape creates a landscape based on your past listening habits. The landscape is created taking into account the mood, mode, energy, and key of your current song.

Visualify

Visualify displays your favorite tracks and musicians by month, year, and all-time in a visually attractive and easy-to-understand manner. You must first log into Spotify using your account information.

It is ideal for you if you don’t want to deal with the technical aspects of your data but still want an idea of how your music preferences changed over time.

Last.fm

When you link your Spotify profile to a Last.fm account, you may get access to useful information about your listening habits and compare them to those of thousands of other people.

You can browse through various music genres and moods to discover new bands. You may also check out certain geographical regions and nations to see which songs are the most popular.

Finally, Last.fm has a spiking list of the most popular songs right now that are trending as a tool to help you narrow down your search for the most popular ones at present.

Run BPM

Run BPM is a customizable website that uses your Spotify data to generate and filter your playlist. You can use the site even if you don’t have a Spotify account.

It uses energy levels, a happiness meter, danceability, and a BPM range to arrange your tracks. You may simply save your playlist and start a new one.

It has a user-friendly design and appealing aesthetics that permeate into its platform. It’s worth a look.

Spotify Stats: Funny side is there too

In our opinion, any kind of processing of data is fun, but these sites take it from a slightly more funny perspective.

Receiptify 

Receiptify is a software that prints out a “receipt” containing your top ten favorite tracks, as determined by the program. You may have it list your current favorite songs from the previous month, six months, or all time.

It’s a simple gimmick, but it’s perfect for sharing on social media quickly.

Zodiac Affinity

If you’re a lover of astrology, you’ll like Zodiac Affinity. It determines which five of your preferred songs are compatible with various star signs, and we have no idea what the rules are here.

How Bad Is Your Spotify

For its AI’s sassy demeanor and searing hot takes, the “How Bad Is Your Spotify” challenge went viral in 2020. The site sniffs through your favorite artists and songs, asks you a few questions, and then tortures you with merciless taunts. It might become extremely filthy.

How Bad Is Your Spotify will identify a category you match into, regardless of how strange your preferences are. Just don’t take it to heart — it’s just a machine.

Conclusion

In the contemporary age, streaming music services such as Spotify have become commonplace. They are more convenient than ever, and with Spotify playlists, users can quickly build a playlist of their favorite songs.

Several fantastic websites and applications are available to assist you in making the ideal playlist for any mood or occasion. Whether you want to analyze your listening habits, pick songs based on energy levels or BPM, or explore a visual music landscape, there is a platform that can help you.

Are you looking for the best way to build your business intelligence strategies? We explain techniques, roadmap, and examples of BI strategies in this article.

The worldwide economy has taken a significant knock in recent months, and businesses that have managed to endure are now searching for methods to use technological breakthroughs to advance. A business intelligence strategy is a roadmap that aims to assist businesses in measuring their performance and improving it through architecture and solutions. Business intelligence analyst abilities are at the forefront of BI plans, especially planning. So let’s get down to business.

Business intelligence strategies: Examples, techniques, roadmap, and more

You’ll need to get familiar with the terminology first! Business intelligence (BI) software collects business data and transforms it into practical insights that allow businesses to make educated business judgments. BI tools allow businesses to access and analyze data through reports, graphs, dashboards, charts, summaries, and maps to develop a BI strategy.

A business intelligence strategy is your roadmap for applying data in your organization. You’ll need a plan since simply adopting the appropriate technology and building a software platform won’t guarantee a profit. To develop a plan, you must first determine three things;

1. How will you use the software platform?
2. What data will you manage for analysis?
3. And how will you enable your staff to make informed, data-driven decisions?

A business intelligence strategy can help your firm profit from actionable insights. Access to sales performance benchmarks, human resources salary projections, and ensuring your shipping department understands what to ship each day are just a few examples. A planned approach that includes discovery, planning, and measured execution leads to success.

Business intelligence strategies may help you think through all the elements of putting up business intelligence technology and executing everything from planning to objectives to personnel to ensure that your new solution is a success. It answers each area of how your firm utilizes data and each step in implementing a business intelligence tool.

Business intelligence techniques

Business Intelligence is concerned with assisting in decision-making. In reality, BI tools are frequently referred to as Decision Support Systems (DSS) or fact-based support systems because they provide business users with the technology to analyze their data and extract knowledge.

Business Intelligence tools usually access the data in a data warehouse. The explanation is simple: a data warehouse already contains data from numerous production systems within the organization, and it is cleansed, consolidated, conformed, and stored in one location. BI applications may focus on analyzing the information because of this. As a result, these BI applications are used for various business intelligence techniques.

Data visualization

Because data is stored as a set or matrix of figures, it is accurate but tough to understand. Are sales increasing, decreasing, or staying the same? Analyzing several dimensions of information at once becomes much more difficult. As a result, data visualization in charts is an easy approach to grasping how to interpret the data immediately.

Data Mining

Data mining is examining huge amounts of data to detect relevant patterns and rules using automated or semi-automatic means. When it comes to data, a corporate data warehouse possesses an enormous quantity. Discovering facts that may influence business decisions is very important. As a result, database researchers employ data mining approaches to reveal hidden patterns and relationships in the data. Knowledge discovery in databases comprises all of the steps involved in transforming raw data into useful information with any necessary selections, transformations, sub-sampling, and selection of the proper way for transformation.

Multi-Cloud

Following the outbreak of the pandemic and the national lockdown that ensued, many businesses worldwide began utilizing cloud technologies in their operations. The advent of cloud technology has had a significant effect on many organizations. Even after the limitations are lifted, companies still prefer to work over the internet because of its ease of use and accessibility. Thanks to its low cost and easy-to-use features, even R&D projects are being transferred to the cloud.

We have already covered the pros and cons of cloud computing and cloud computing jobs if you are interested.

Reporting

BI technologies help business users design, schedule, and generate performance, sales, reconciliation, and savings reports. BI technology-generated reports efficiently gather and present information to aid management, planning, and decision-making. Once the report is built, it may automatically be sent to a specified distribution list in the proper format with current/weekly/monthly data.

Time-series Analysis Including (Predictive Techniques)

Every data warehouse and business data is time-based. Product sales, calls, hospitalizations, and so on are just a few examples of this. It’s critical to show how users’ behavior has evolved regarding product relationships or sales contract modifications due to marketing campaigns. Future trends or outcomes may be forecast based on previous data.

Online Analytical Processing (OLAP)

OLAP (Online Analytical Processing) is a fundamental business intelligence approach that solves analytical issues with multiple dimensions. The multi-dimensional nature of OLAP allows users to examine data concerns from various perspectives, which provides flexibility in dealing with problems. They can find latent problems by looking at things from different angles. Budgeting, CRM data analysis, and financial prediction are examples of tasks that can be done using OLAP.

ETL

Extraction-Transaction-Loading (ETL) is a specialized business intelligence approach that orchestrates data processing. It extracts data from storage and converts it to the processor before loading it into the business intelligence system. They’re commonly used as a transaction tool, which transforms data from numerous sources into data warehouses. The data is then filtered and moderated by ETL to meet the demands of the business. It improves the quality level by loading it into end targets such as databases or data warehouses, called quality verification.

Statistical Analysis

Data analysis begins with the mathematical underpinnings used to assess the significance and trustworthiness of observed connections. Distribution analysis and confidence intervals (for example, changes in user behaviors, etc.) are impressive features. The technique of using statistics to establish and evaluate outcomes from data mining is known as statistical analysis.

How to make a business intelligence strategy and roadmap?

Business intelligence strategies are on the rise. According to a recent study of over 700 business executives, 71% of firms have established a BI approach to anticipate company performance, improve client experience, gain a competitive edge, speed up data analysis, and make more data-driven decisions. So, how do they do?

Define the current state

Business intelligence strategies start with a baseline to know where you are going. Take, for example, if you realize that several departments have been using analytics. But the data has been mostly compartmentalized – marketing personnel doesn’t have access to sales information, and customer support is tracking user feedback for their internal purposes, or maybe there isn’t any analytics. It appears to function, yet how effective is uncertain.

The first step is to get the input of current BI processes’ users, as well as the IT team and department managers. As a result, you should be able to provide answers to the following questions:

• What is your vision for BI? Do you have one? Is your vision in line with your IT and corporate plans?
• Who are your BI players, and how well coordinated are they? Is there a lack of coordination between them?
• How do you plan, organize, and manage data? How can you help BI users?
• What solutions are you employing, and how? Which of them add value?
• Is your architecture in line with your company’s objectives? Are you confident that your licensing approach is the greatest option?

Then, to put it all together, compile a SWOT analysis to organize what you’ve discovered. The SWOT analysis, one of the most popular strategy-building tools, will aid you in determining your key assets and concerns for the following stage.

Create a BI vision

First, you must describe your present condition to understand your BI plan. Once you know where you are now, you’ll be able to define what is feasible. To begin, connect data from various sources to determine where you are right now.

Then, to assist you in better comprehending how BI may help your business succeed, create your objectives and priorities. After that, you’ll be on the road to establishing clear and reasonable expectations. It’s critical at this time to determine the following:

• What data will be collected?
• Who will be a part of the BI process?
• What is the best way to integrate BI with the company’s core business procedures?
• How can you provide BI solutions?
• Which BI solution should you use?
• What kind of KPIs do you need to keep an eye on?
• What is the future of BI lifecycle management?

Build a BI roadmap

A roadmap is a visual document showing the different implementation phases over time. By this step, you’ve already accumulated all of the data necessary to arrange and schedule on the map; all you have to do now is create time frames and deliverables for each activity. It is one of the most important topics for your business intelligence strategies.

A roadmap can cover only high-level activities such as “Find a BI vendor” or be focused on “Create a list of top ten best matches,” but for strategic mappings, the broad picture will be enough.

Assemble a BI team

BI specialists in charge of data discovery, analysis, and connecting it with end-users. There are numerous BI jobs and obligations for big businesses. They may be combined and concentrated in one position if you have a limited monetary constraint regarding human resources. If you want to establish your in-house team, consider the following key positions:

• BI project manager helps bridge the gaps between business and technology stakeholders by documenting, monitoring, and reporting IT service management processes.
• The BI architect established the BI infrastructure by converting business demands into a data warehouse.
• BI analyst is an analyst who uses data mining and analysis to extract valuable information.
• The ETL developer is responsible for the data warehouse’s ETL processes.
• An analyst in the field of data visualization is a person who provides informative and clear graphics to end users from the examined data.
• The system administrator is in charge of installing and maintaining the hardware.

Do you want to know how business intelligence creates collaboration?

Choose a sponsor

While a business intelligence strategy should include numerous stakeholders, selecting someone to lead the project is critical. Putting the Chief Information Officer (CIO) or Chief Technical Officer (CTO) is tempting. This isn’t always the best option. It should be sponsored by an executive with bottom-line responsibility, a broad view of the company’s objectives and goals, and a grasp of how to translate corporate goals into mission-focused key performance indicators.

CFOs and CMOs are ideal for implementation. They can lead the execution of a business case and be in charge of scope changes.

Define a budget

It’s time to consider a budget after establishing the company’s present condition. Developing an accurate budget is crucial in creating a successful business intelligence plan. Budgeting helps you distribute your resources effectively, so you have everything you need to start. Budget directly affects business intelligence strategies.

Several suppliers in the market provide various business intelligence tools that allow organizations of all sizes to use their data. Their prices, in most cases, vary from company to company, depending on their size and demands. This is why knowing your needs and how much cash you have is critical before looking for one of these alternatives. You’ll be able on this way to compare suppliers and select the finest one for yourself.

Choose a business intelligence solution

You will need help with your business intelligence strategies. Once you’ve completed your review of available data and demands, it’s time to pick a business intelligence solution and establish a data infrastructure that will last throughout the life of your strategy. Data collection and management, storage and capacity, visualization tools and dashboards, and access and governance, are all important areas to consider when setting up an IT architecture.

Do you want to know the 10 ways use business intelligence software in your organization?

Data collection and management

Keep your data gathering and organizing simple by keeping it straightforward. What do you need to know before you begin collecting data? Where will the data come from, and what kind and format will it be? Who will oversee and prepare the data? Who will ensure proper data entry and organization standards for data collection and organization? Will you have to hire any additional personnel to assist with your new data collection and management systems? 

Storage and capacity

Fully evaluate your data storage alternatives, whether they’re off-site or on-premise. Your technical business intelligence team or even a business intelligence consultant will be able to advise you on the advantages and disadvantages of each storage solution in terms of your company objectives. 

Data visualization tools and dashboards

Any successful business intelligence strategy necessitates the delivery of insight via data visualization and visual analytics dashboards. You’ll know what dashboards and visualization tools best suit your organization’s needs when you decide the scope of your business intelligence strategy and the intended internal audience.

Data access and governance

The data access and governance rates required for your new business intelligence approach should be discussed with your CIO, CDO, or another technical team in charge of the BI program.

Consider:

• Should you provide more access to certain individuals or executives than others?
• What data will each user or employee have access to, and who will be allowed to change the actual data?
• What safeguards do you need to secure your business intelligence solution from external security risks?
• Will the selections you’ve made in access help or hinder your company’s goals and efforts to become a data-driven organization?
• How can we ensure appropriate data sharing and governance in the face of inevitable changes in employee attitudes?

You can check the top 20 BI tools.

Document a BI strategy

It is a strategy document intended to serve as a resource for the entire organization and as a point of reference for the strategy presentation. It includes these elements:

• Executive summary
• BI strategy alignment with corporate strategy
• Project scope and requirements
• BI governance team
• Alternatives
• Assessment
• Appendices

Develop a “Data Dictionary”

Large data dictionaries may be time-consuming and difficult to maintain, so they are now considered a faux-pas in Agile development. It’s too easy for large data dictionaries to become cumbersome and hard to keep up with. That said, for business intelligence to flourish, there must at the very least be a general agreement on data definitions and mathematical computations. The absence of a nomenclature agreement is an issue affecting many businesses today. For example, finance and sales may use the term “gross margin” differently, resulting in a mismatch between them. To prevent this from happening, get all of your SMEs to sit down and hammer out the definitions. Then pick the repository that’s best suited for your company to store this data.

Training

Providing a company-wide BI strategy in most situations entails giving new tools to non-business intelligence and data analytics users. 

Employees at all levels should feel confident in their ability to use the new solution to inform their everyday decisions without difficulty. Employees shouldn’t struggle to use your business intelligence solution; most of that ease and confidence come from effective, thorough training.

Launch and measure

Congratulations, you’ve completed the process! After all of your research, planning, question asking, aligning, and collaboration, you’ve created a business intelligence strategy! Remember to track your progress and keep measuring after each phase of your plan; we suggest informing workers when you meet your company goals and achieve them in an evidence-based manner. Take them with you on your journey to success, and measure that success meticulously so that you can tell stakeholders and everyone else in your team about it.

Effectively implementing a new business intelligence solution is not easy. Still, with the right approach, you can keep track of your timeline and goals while simultaneously getting more done for your organization.

Measuring effectiveness with business intelligence strategies

For most BI managers, assessing success is a postscript. Getting permission for a project and delivering results without creating another project to evaluate team performance is difficult enough. And if you do have the time and inclination, exactly what do you track?

These are some options for it:

• Usage tracking
• Surveys
• Social media analysis
• Spreadmarts
• Cost efficiencies

Real-world business intelligence strategy example

Let’s see some BI solutions in action:

New York Shipping Exchange: BI Reduces IT Dependency

The New York Shipping Exchange (NYSHEX) is a shipping-technologies firm striving to improve the process of exporting goods from the United States.

• Challenge: To make sense of the whole company’s performance, NYSHEX would need to manually extract data from its proprietary and various cloud applications and then import it into Excel. This was a time-consuming process; few individuals had access to the information, and most report requests were passed on to the engineering team to fulfill.
• Solution: They invested in BI, centralized their data, and provided everyone in the company with analysis tools that even non-coders could use.
• Results: In 2019, the firm more than tripled its shipping volume from Asia to the United States due to business intelligence and other efforts.

Expedia: BI Builds Customer Satisfaction

Expedia is the parent business of several top-tier travel firms, including Expedia, Hotwire, and TripAdvisor.

• Challenge: Customers are critical to the company’s goal, strategy, and success. The online experience should provide a similar level of satisfaction as a good trip.
• Solution: The firm had a large quantity of data to aggregate manually, leaving little time for analysis. The client satisfaction team used business intelligence to analyze customer data from the company and link findings with ten corporate objectives that were directly linked to the goals. Owners of these KPIs collect, organize, and analyze data to identify trends or patterns.
• Results: Customer support can access real-time performance data and take corrective actions if necessary. In addition, the information may be utilized by other departments. A travel manager, for example, might utilize BI to discover high volumes of unused tickets or unbooked reservations and devise methods to modify behavior and enhance overall savings.

Sabre Airline Solutions: BI Accelerates Business Insights

Sabre Airline Solutions offers booking solutions, revenue management, web, mobile itinerary applications, and other technology to travel sector businesses.

• Challenge: The travel sector is fast-paced, to put it mildly. Clients in the industry required advanced solutions that could give real-time information on consumer habits and actions.
• Solution: Sabre created an enterprise travel data warehouse (ETDW) to store its vast quantity of data. With a 360-degree view of company health, reservations, operational performance, and ticketing in user-friendly environments, Sabre executive dashboards provide near real-time insights.
• Results: The ability to scale, a user-friendly graphical interface, data aggregation, and collaboration have resulted in increased income and client happiness.

Conclusion

Businesses need a BI strategy to advance and maintain their competitive advantage. Companies must acknowledge the value of information customers give so that they may change their long-term vision and gain a fresh perspective to stay up with changing consumer behavior in the market.

We’ve addressed what a BI plan is and why it’s significant. This raises an issue: Do you really need one? If you want to stay on top of changing client behavior, maintain your company’s competitive edge, and remain one step ahead of your competitors, we would say yes.

The question of the day is, “What is data fabric?” Data-driven decision-making and increasing data practices are becoming more widespread in the business world. The epidemic may have compelled them to act, but they’ve recognized the value of data and will never go back to making judgments based on hunches. So, the phrase “data fabric” has become synonymous with enterprise data integration and management over the last few years. Data fabric is an end-to-end data integration and management solution that includes architecture, data management, integration software, and shared data for managing information. Let’s have a closer look at it.

Data fabric definition: What is data fabric?

An architecture that allows the end-to-end integration of disparate data pipelines and cloud platforms through smart and automated systems is known as a data fabric. Over the last decade, advances in hybrid cloud, artificial intelligence, the internet of things (IoT), and edge computing have created an abundance of big data, adding to enterprises’ difficulties in managing it.

As data volumes have grown, organizations need to manage and control them. This has made the unification and governance of data environments a higher priority. This growth has presented numerous issues, such as data silos, security risks, and decision-making bottlenecks. Data management teams are leveraging these tools to unify their disparate data systems, embed governance, improve security and privacy measures, and increase worker access to data.

“By 2024, 25% of data management vendors will provide a complete framework for data fabric – up from 5% today.”

Gartner

Data fabric is a solution that allows organizations to manage their data—whether it’s in different types of apps, platforms, or regions—to address complex data issues and use cases. Data fabric makes it simple for users to access and share information in a distributed data environment.

Simplified: What is data fabric?

Maybe we can better understand what is data fabric with this example. Consider a self-driving vehicle as an example. Consider two situations. At first, the driver is in control and pays close attention to the route, while the car’s autonomous component takes minimal or no action. The driver is slightly negligent in the second scenario, and the machine instantly changes to a semi-autonomous mode and makes required adjustments.

The two examples above summarize how data fabric works. As a passive observer, it begins monitoring the data pipelines and offering better alternatives. When the data “driver” and machine learning are comfortable with repeated scenarios, they automate improvisational activities (which take too much manual labor), leaving leadership free to focus on innovation.

Data fabric architecture explained

A key feature of data fabric architecture is that it is used across all data structures and sources in a hybrid multicloud environment, from on-premises to cloud to edge.

Data fabric aims to make an organization’s data as useful as possible – and as quickly and safely as possible – by establishing standard data management and governance processes for optimization, making it visible, and providing insights to numerous business users.

Businesses that utilize this sort of data architecture have certain similarities in their architecture that are unique to a data fabric. More specifically, they include the following six layers:

1. Data Management layer: This is in charge of data management and security.
2. Data Ingestion Layer: The layer stitches together cloud data and establishes connections between structured and unstructured data.
3. Data Processing: The data processing layer cleanses the data, ensuring only relevant information is presented for extraction.
4. Data Orchestration: The data fabric’s most essential layer, which performs critical tasks such as transforming, integrating, and cleansing data to make it useful for teams throughout the company.
5. Data Discovery: This layer exposes new ways to aggregate diverse data sources. It may, for example, discover methods to link data in a supply chain data mart and a customer relationship management system, allowing for the creation of new product offers to clients or improvements in client satisfaction.
6. Data Access: This layer enables data consumption, ensuring appropriate authorization for certain teams to follow government rules. This layer also aids in presenting important data via dashboards and other data visualization technologies.

Data fabric must-haves

We explained what is data fabric and the following are the features that a good data fabric solution should have:

• Autonomous data engineering: This is done by monitoring real-time data and analyzing it to make just-in-time query optimization for efficiency and usage consumption that can anticipate the demands of the data consumer in a single architecture, lowering the complexity of data management.
• Unified data semantics: A data warehouse for all consumer data to establish corporate meaning and obtain a single-source-of-truth (SSOT) point. Regardless of architecture, database technology, or deployment platform.
• Centralized data security & governance: A single security policy can distribute access and apply Zero Trust principles uniformly across the infrastructure, regardless of whether data is stored in the cloud, across clouds, in a hybrid scenario, or on-premises.
• Data management visibility: The capacity to monitor data reactivity, availability, dependability, and risk in a centralized location is crucial for businesses.
• Agnostic to platform and application: Consumers and data managers will be able to choose from various analytics solutions, thanks to the ability to integrate with the data platform or BI/ML application.
• Future-proofs infrastructure: Modernize legacy systems to maximize investments while limiting the disruption of new technologies and data types. New infrastructure builds are seamless with current infrastructure, and existing infrastructure is not disrupted.
• No need for data movement: Intelligent data virtualization creates a unified view of data collected from many sources without copying or transporting it.

But be careful. These requirements may cause you to confuse data fabric with a data lake and data mesh. Because of that, we have prepared some comparisons for you.

Comparison: Data fabric vs data lake

A data lake is a repository for data and data assets, whereas a data fabric is a method for extracting and utilizing such information. The two phrases are synonymous; many experts believe that using a data fabric to extract the most value from stored data is the greatest way. However, there are significant differences between them.

A data lake is a repository of data in its raw form that has not been sorted or indexed. The data might be anything from a simple file to a large binary object (BLOB), such as a video, audio, image, or multimedia file. When the data is extracted, it’s evaluated and manipulated to make it usable.

The term “data fabric” refers to a system used by an organization’s data across all storage and usage scenarios and that uses the same set of protocols, processes, organizations, and security.

Comparison: Data fabric vs data mesh

Although the terms data fabric and data mesh are sometimes used interchangeably, they represent distinct ideas. In general, a data fabric and a data mesh are similar in that they are techniques for recognizing how businesses manage large quantities of stored information. A data fabric approach aims to regulate data by constructing an administrative layer on top of it wherever it is kept. The latter differs from the former in that aspects of certain types of data management are handled by teams or groups within the organization who utilize that information.

On the other hand, a data fabric is a technology-centric architectural approach that addresses the difficulty of data and metadata. In contrast, a data mesh focuses more on organizational change, emphasizing people and procedure than architecture.

Data fabric advantages

Gartner has noted specific gains in efficiency for data fabric providers as they gain more market adoption. It can “reduce the time for integration design by 30%, deployment by 30%, and maintenance by 70%.”

While it’s clear that data fabrics may boost productivity across an organization, the following business benefits have been shown:

Intelligent integration

Data fabrics employ semantic knowledge graphs, metadata management, and machine learning to connect disparate data sources and endpoints. This helps data management teams group related datasets together while also integrating net new data sources into a firm’s data ecosystem. Automating parts of data workload administration leads to the efficiency mentioned above, but it also aids in the breakdown of silos across IT systems and centralized governance processes. The overall quality of your information improves as a result of this functionality.

Democratization of data

Data fabric architectures allow for self-service applications, broadening access to data beyond more technical resources like data engineers, developers, and data analytics teams. Lowering data bottlenecks allows for greater productivity, allowing business users to make faster business decisions while freeing technical users to focus on activities that better utilize their talents.

Better data protection

The open-data movement also does not imply giving up on data security and privacy protections. It requires the establishment of additional data governance barriers around access controls, ensuring that specific data is only accessible to designated people. Data fabric designs also enable technical and security teams to implement data masking and encryption across sensitive and proprietary material, minimizing the chance of data sharing or system breaches.

Data fabric risks

The worry of data security when data is passed through the data fabric from one location to another has become a major concern for businesses. To guarantee safety from security breaches, the infrastructure for data transportation must include secure firewalls and protocols. Data security at all stages in the data cycle is essential as cyber assaults on firms increase.

Data fabric examples/use cases

Data fabrics are still in their early days when it comes to adoption, but their data integration capabilities enable firms to perform a wide range of use cases. While the tasks that a data fabric can handle may not be vastly diverse from other data solutions, it distinguishes itself by the scope and scale of operations it can manage because it eliminates data silos. Companies and their data scientists may create a comprehensive view of their customers by integrating various data sources, which is particularly beneficial for banking clients.

What is data fabric, and what does data fabric adoption offer your company? Consider the following use cases for further information:

• Customer profiles,
• Fraud detection,
• Preventative maintenance analysis,
• Return-to-work risk models,
• Enterprise innovation,
• Preventative maintenance,
• Slaying silos,
• Deeper customer insights,
• Enhanced regulatory compliance,
• Improving data accessibility across healthcare organizations and academic institutions, and more.

Best data fabric companies and tools

The major objective of data fabric is to provide integrated and enhanced data – in the proper time, in the appropriate format, and to the correct data consumer – for operational and analytical purposes. Here are some of the best solutions:

IBM

In both on-premises and cloud environments, IBM offers a variety of integration methods for nearly every business use case. The company’s on-premise data integration suite comprises tools for traditional (replication and batch processing) and modern integration synchronization and data virtualization). IBM also provides several prebuilt functions as well as connectors. The cloud integration solution from mega-vendors is widely regarded as one of the finest in the market, with new features being introduced ongoing.

Denodo

Denodo is a prominent supplier of data virtualization tools. Denodo, which was founded in 1999 and is based in Palo Alto, California, provides high-performance data integration and abstraction across a range of big data, business intelligence, analytics, and unstructured and real-time data services. Denodo also offers unified business data access to businesses wanting to use BI solutions such as statistics and single-view apps. The only data virtualization platform on Amazon AWS Marketplace is the Denodo Platform.

K2View

The K2View Data Fabric is a unified platform for data integration, transformation, enrichment, orchestration, and delivery. The product was created to enable real-time activities while integrating fragmented data from various business units into their micro-DBs to offer a comprehensive perspective. One micro-DB is maintained for each instance of a company entity, with web services component sizing and exposing the information in the micro-DBs for use by external applications. K2By utilizing a distributed architecture, the K2View Data Fabric can handle hundreds of millions of micro-DBs at once.

We explained what is data fabric and everything about it in this article. If you are interested in the topic, please drop a comment below to start a conversation.

Finding suitable CRM software for small business nowadays is no easy feat, with hundreds or even thousands of solutions to choose from. CRM software aids businesses in boosting sales, boosting development, and providing exceptional client experiences. There are a variety of CRM systems on the market, each with its own set of characteristics and benefits. We’ve put up a list of the best CRM software for small businesses today to help you make an informed decision. If you’re still unsure about how to choose marketing automation software, don’t worry, we’ve got you covered.

Importance of CRM software for small business

A CRM is essential since it’s a data-driven platform that can help small firms save time and effort in day-to-day operations. A good CRM, like HubSpot, ensures that everyone has access to the same critical business information that may provide important insights into individual consumers, sales, marketing, customer support, and emerging trends. According to LinkedIn’s State of Sales Report for 2020, 97% of salespeople consider sales technology (such as CRMs) to be “extremely essential” or “critical.” Furthermore, according to the site’s survey data, nearly three-quarters of all employees are satisfied with their current job.

CRMs can also aid in team management. Because everyone knows what everyone else is working on, leads may be given to members of your sales staff who have the most time to develop them. This will ensure that there are no bottlenecks in your sales pipeline.

Users may employ a marketing-focused CRM, or in some cases, the marketing capabilities built into a more comprehensive CRM to help them carry out a variety of marketing activities. These include search engine optimization and A/B testing for e-commerce-based advertising campaigns. CRMs can also report on the success of your initiatives, and more sophisticated ones can even make market predictions.

A sales-oriented CRM can keep the selling process on track using lead management and contact management capabilities. Customers may be manually assigned to a sales representative and, in some situations, automatically assigned. All connections may then be tracked and evaluated, allowing the sales team to see where each lead is in the process and when it’s ready to proceed to the next stage of the sale process. Managers can also keep track of what their people are doing at all times without having to wait for updates by using real-time monitoring capabilities.

CRM software cost for small business

Depending on the number of employees you have, the features you require, and the CRM provider you pick, your CRM software may cost anything from free to hundreds of dollars each month. The wide range in pricing can be intimidating for small businesses that don’t know how far they can stretch their IT budget. However, when it comes to purchasing CRM software, you don’t have to give up quality to save money. So, let’s have a closer look at the best CRM software for small businesses.

Best CRM for small business 2022

Modern sales rely on customer relationship management (CRM) platforms, which are the heart of modern business. Although CRMs are less common in small organizations, almost every major corporation employs them, particularly microbusinesses and startups.

3 best CRM for startups

CRM for startups aids in the development of customer connections. Simply said, a strong CRM may be the most efficient approach to boosting income.

Zoho CRM

Zoho is a cloud-based collaboration software that includes CRM and project management features. The suite’s many editions cater to small businesses, allowing them to use Zoho for everything from invoicing to HR. Zoho offers many sales/marketing tools, including website visitor tracking, lead scoring, sales signals (with pop-up notifications about leads), and more. Keep in mind that some of the most sophisticated capabilities are only available in the professional and corporate versions.

Pricing: Free version, Standard ($12/month), Professional ($20/month), Enterprise ($35/month), and more.

 Agile CRM

Agile CRM is a CRM software with many of the same functionality as more established versions, such as custom appointments, drag-and-drop marketing automation, and reports via email. This program also supports widgets, a huge plugin library, API-powered connections, and more. On the other hand, the free edition is somewhat restricted in terms of features. It is one of the best CRM software for small business.

Pricing: Free for 10 users; from $9.99/month/user for paid plans

Insightly

Insightly is for both Android and iOS, Insightly is accessible on web and mobile versions. It also works with Google G Suite and Microsoft Office 365.

The platform has a reputation for having a simple integration between CRM features, such as managing contacts and customer data, tracking opportunities (i.e., sales leads), and assigning responsibilities to team members with useful to-do lists.

Pricing: Plus is $29 per user/per month, Professional is $49 per user/per month, Enterprise is $99 per user/per month, billed annually.

A 14-day free trial is available for the Plus and Professional plans.

3 affordable CRM for small business

A CRM system can be a really useful tool if used correctly. You may use it to manage all of your touchpoints with current and potential customers, capture their unique preferences and utilize that data to develop customer loyalty and increase sales truly. Furthermore, you won’t break the bank or spend countless hours learning how to do it.

Nimble

Nimble is a simple CRM focused on social media that provides smart social search and powerful market segmentation tools. It works with Office 365 and G Suite, so you may quickly import and organize contacts from the platform you’re already using.

All of the features you’d expect in a CRM are present in this one, with a distinctively modern user experience and agile, I’ll say nimble, simplicity of use.

Nimble is a social media management tool that allows you to simplify your social media operations by collecting postings in one location. Nimble gathers postings in one place, allowing you to see how people engage with your brand immediately and in real-time. This package also includes Nimble’s Contact Record feature, which may integrate cross-channel contact and lead data into a single coherent profile.

Pricing: Ranges from $9/user/month to $19/user/month.

Vtiger 

Vtiger offers an all-in-one package with sales, marketing, and customer service capabilities for those who want to get started quickly. For individuals looking to dip their toes into the water, Vtiger provides a sales-only CRM. It also has project and inventory management, phone systems, social media integration, and internal collaboration. This makes it ideal for small companies that need a low-cost CRM with all of the bells and whistles.

The ability to link contacts to opportunities, quotations, cases, projects, and invoices is at the heart of Vtiger. The top menu contains all of the software’s major features called Modules. To keep things simple and focused, only that app should be opened by clicking on any one of them. However, you can create contacts, tasks, possibilities, projects, and other activities from any Module. It is one of the best CRM software for small business.

Pricing: Depends on the package you select (Sales CRM, Help Desk, or All-In-One), but pricing can range anywhere from free to $30/user/month

Capsule CRM

Capsule CRM is a simple-to-use CRM software that allows users to track customer and sales pipeline connections. It can also be used on mobile devices. The following five elements make up the user interface: dashboards, people and organizations, sales pipeline, calendars and tasks, and finally, cases. However, it lacks certain campaign capabilities and reporting options.

Pricing: They have a free package (up to 2 users) and a Professional package (£12/per user/month).

3 best CRM software for small business free

It doesn’t get any more cost-effective than free, and that’s what we recommend below.

HubSpot Free CRM

HubSpot Free CRM features a variety of free tools, including contact organization, Gmail and Outlook integration, email tracking, meeting scheduling, and live chat.

It’s also completely painless; you’ll never have to worry about your customers not receiving their notifications. Best of all, you may keep track of the entire sales pipeline on a clear, visual dashboard. I was just looking at the sales pipeline for a local client and was shocked at how simply they could see whether or not leads and conversions were progressing in real-time. It is one of the best CRM software for small business for free.

Sendinblue

You can learn more about the company’s services and pricing on its website, where you’ll also discover a list of Sendinblue features and plan comparisons. You’ll notice that Sendinblue provides one of the most feature-rich free plans we’ve seen today upon visiting the company’s pricing page.

For just $0/month, you get a sales CRM, unlimited contacts, email marketing, and marketing automation with a generous limit of up to 300 emails sent per day.

It also includes a drag-and-drop email builder with a library of ready-to-use templates so you can create and send out visual campaigns to leads at every phase of the client journey.

Sendinblue also has an API that allows you to integrate the platform into your website or application. You get access to Sendinblue’s Transactional Platform, which includes some of the best transactional email capabilities available. This makes Sendinblue a fantastic solution for tiny SaaS firms, internet retailers, and other transactional companies of any size, even though larger corporations may find they outgrow the platform’s other features.

EngageBay

EngageBay is one of the most cost-effective all-in-one customer relationship management (CRM) solutions on the market, starting with one of the most generous free plans and significantly low pricing on its paid versions.

Plus, with its additional capabilities, such as email sequences, landing pages, live chat, and more – all of which are available in the free edition – it’s clear that the platform goes above and beyond.

Top 10 CRM software

Small company CRM software is a tool for small businesses that need to manage interactions with both existing and potential clients. Sales, marketing, and customer care are all possible using CRM systems.

With more and more inexpensive, Web-based goods on the market, many small firms are searching for customer relationship management (CRM) software to manage interactions with both current and potential clients.

Many businesses want to upgrade from basic email marketing or contact management solutions. On the other hand, CRM systems may provide a variety of capabilities for sales, marketing, and customer service. Of course, there are alternatives to CRM that might be more beneficial. Our comparisons of CRM vs marketing automation and ERP vs CRM can help you make an informed decision. We’ve also compiled a list of the best marketing automation tools for your convenience. Anyway, on to the subject at hand. Here are the best CRM software for small businesses.

Zendesk Sell

Zendesk Sell is a platform that allows you to share information and work together more effectively across departments. Zendesk Sell is the ideal solution to promote team collaboration across divisions. If your customer experience strategy extends beyond the sales department, you’ll need
a CRM software capable of handling it. Sales, marketing, and support teams frequently use Zendesk Sell to centralize data from numerous contact points to prevent duplicated efforts or embarrassing misunderstandings.

Zendesk Sell’s starting plan is perfect for small firms and single entrepreneurs. It features basic CRM tools such as a configurable sales pipeline, basic reports, and a team document repository. As an admin, you can assign particular team members to pipeline deals that notify employees outside the sales team of any changes or action items.

Pricing: Starting from 19 EUR/User/Month

Salesforce

Salesforce is undoubtedly one of the most popular and effective CRM software for small businesses. It works for firms of all sizes, including those that are attempting to succeed through contact management. Salesforce is an excellent alternative if you want to expand your deals, boost productivity, and fill your pipeline with high-quality leads.

To begin, use WPForms to gather leads from WordPress to Salesforce. Then guide visitors through a customized consumer journey and utilize the smart marketing platform for emails, mobile, social media, and digital advertising to drive more sales.

Pricing: Lightning Essentials package ($25/month/user), Lightning Professional ($75/month/user), and more.

Freshsales

Freshsales allows small- and mid-sized businesses to interact with their customers, understand their needs, and convert leads into sales quickly and easily.

Freshworks automation software, the Freshsales suite is an all-in-one CRM solution that combines sales and marketing.

Freshsales’ Freshbot is a cloud-based service that enables organizations to create, manage, and analyze contact lists for marketing automation, customer support applications, sales lead management solutions, and more. The software’s intelligent Freddy AI assistant provides transparent insights into client engagement, allowing businesses to find leads, close deals, and nurture client relationships. The program also has an easy-to-use desktop and mobile interface with immediate access to client records and communications.

Pricing: Package for Growing Teams ($25/user/month).

HubSpot 

The free version of HubSpot’s CRM software is quite popular among small enterprises. The program includes all of the basic capabilities offered by CRM software, allowing you to keep track of your company’s activities, including contacts and company profiles, assigning & tracking deals, and managing all of that data in a detailed dashboard (accessible to all team members). Do you already use HubSpot for your sales? Then this CRM software may also be used to increase your inbound sales. Not to mention, HubSpot has a variety of integration choices with popular platforms like SalesForce, Shopify, and Microsoft Dynamics.

Pricing: Free of costs or upgrade to a paid version (starting at 41 EUR / month).

Microsoft Dynamics 365

If you’re a Microsoft user, you’re undoubtedly familiar with Microsoft Dynamics 365, which is an all-in-one platform for sales and marketing. This CRM has a lot of CRM capabilities and integration with Microsoft products as well as LinkedIn.

The only disadvantage of this gadget is that it might be tough to use for beginners, and mobile app capabilities are limited.

Pricing: Different modules and licenses are offered. Sales Insight i.e. (€ 42.20/user/month)

Salesmate CRM

Salesmate CRM is designed to assist you in speeding up the sales processes and providing a personalized experience for your customers. Maintain control of all relevant sales activities by managing your contacts effectively. This tool also allows you to send out email campaigns, and you’ll be able to see what happens to them after they’ve been sent. Salesmate recognizes these demands as a Small Business since it offers flexibility and scalability. Prospect & Lead Engagement, Sales Pipeline & Activity Tracking, and Sales Automation & Sequences are just a few of the unique functions accessible with this CRM software.

Pricing: Starts at $12/user/month.

Salesflare

If you despise completing a CRM and want one that actively assists you in the market, Salesflare is an excellent alternative. It pulls up all the information necessary and keeps itself updated automatically.

Salesflare is a B2B-focused business-to-consumer (B2C) advertising platform. While it may appear to be basic, it still packs a punch. You can even use it to deliver personal email sequences at scale.

Pricing: $30/user/month

Capsule CRM

Capsule CRM is a simple-to-use CRM software that tracks both relationships and sales pipelines. It can also be used on desktop computers as well as smartphones. The dashboard, people and organizations, sales pipeline, calendars and tasks, and cases are the five categories of the user interface. However, its campaign capabilities and reporting are somewhat limited.

Pricing: They have a free package (up to 2 users) and a Professional package (£12/per user/month).

Streak

Streak is a powerful Gmail marketing tool. Streak, which may be used as a browser plugin, is ideal for smaller groups since they can work right out of their Gmail inboxes. This software makes tracking views, scheduling emails, sending mass emails, and establishing separate email threads for various teams (e.g., sales, HR, service) simple.

Pricing: From $15/user/month.

SugarCRM

SugarCRM is a fantastic alternative for small businesses with specialized requirements that larger CRMs can’t meet. The software’s fields, modules, and page designs may all be altered or modified to the user’s liking, and no coding knowledge is required. You may also modify core SugarCRM features such as opportunity trackers, forecast builders, and job schedulers; however, those changes will likely need more technical expertise.

SugarCRM’s flexibility and scalability complement its basic features to help your company reach peak productivity. Real-time reports and dashboards give you live insights into the sales pipeline to help you monitor your team’s performance and evaluate business decisions on the fly. The entire history of a customer’s journey is also included in the platform, assisting with accurate predictions. SugarCRM’s user-friendly quote management system supports a variety of currencies – a bonus for businesses dealing with foreign clients.

Pricing: Sugar Professional (£41.60/user/month, ten user minimum) all the up to Sugar Market (£800/month)

Conclusion

Reminding that there are plenty of more successful options to select from, these are the best CRM software for small businesses that we’ve found. Please leave a comment if you have anything to add.

We gathered all of the essential business intelligence analyst skills for you. If you want your company to stay successful and increase brand recognition far beyond the competition, you need BI. Keeping this in mind, jobs like Business Intelligence Analyst are becoming increasingly popular! That’s why you come in! You can take advantage of the growing popularity of Business Intelligence to get a BI Analyst job that will teach you a lot while also taking your career to new heights. Cloud computing jobs are also on the rise. We have already explained cloud computing job requirements, trends, and more in this article. But what abilities are necessary for a Business Intelligence Analyst?

What are the business intelligence analyst skills needed for a successful career?

Business Intelligence Analysts analyze data to help firms improve their earnings by keeping track of current market trends. Analysts have access to a variety of data. A company’s database, web crawling software, or going to another firm’s data and checking in may all supply information. The collected data is then used to construct a picture of the present market and the best route for a business to follow in the future.

Using data to meet organizational goals puts Business Intelligence (BI) in the limelight.

BI is a broad term that includes the operation and management of data processing tools and systems, such as data visualization tools, data modeling tools, decision-support systems, database management systems, and data warehousing systems.

A bachelor’s degree in business, management, accounting, economics, statistics, information science, or a closely related discipline is generally required for BI analysts. For higher-level or high-profile employment, more advanced degrees are required. This sector deals with a lot of data, a changing market, and predictions. Having excellent analytical, organizational, and forecasting abilities in this industry is quite advantageous.

What are the business intelligence analyst’s jobs?

The business intelligence analyst assesses both company data and data from rivals and others in the industry to discover methods to improve the company’s market position in today’s data science workforce. Analysts with a good understanding of the company’s systems, procedures, and functions will evaluate their firm’s operations, processes, and functions to identify where it may improve efficiency and profit margins.

Business intelligence analysts should also consider new methods to create new policies around data gathering and analysis techniques and ensure data use. At times, business intelligence analysts may be tasked with employing other data experts, such as data architects. Business intelligence creates collaboration in the workforce. We already explained how business intelligence creates collaboration in this article.

What are the business intelligence analyst qualifications?

A BI Analyst must possess a wide range of abilities. Here are some examples:

Data preparation

The purpose of data preparation is to allow you to extract useful information from your data. Firstly, to get any insights from the data, the data must be gathered, cleaned, and arranged consistently. Using many data preparation tools, you may collect data from several sources and then transform it into the same dimensions and measurements. And as a BI Analyst, you should be conversant with at least some of these data preparation technologies, such as Tableau Prep, Improvado, Alteryx, etc.

Business intelligence analyst education requirements

It is one of the most terrifying business intelligence analyst skills. The most popular requirement for BI analysts is a bachelor’s degree. To acquire the skills you’ll need on the job, consider majoring in statistics, business administration, computer science, or a closely related discipline. Some BI experts pursue an MBA or a data science graduate degree.

Data tools

Business intelligence analysts use various technologies to access, analyze, and visualize data. They might require knowledge of Structured Query Language, or SQL, a tool for retrieving data from databases. Other tools that business intelligence analysts may utilize are Tableau and Power BI, which allow them to pull information from data sources and generate visualizations like graphs.

Data mining

The technique of data analysis is known as data mining. This transforms raw data into useful information that may be utilized to make decisions. The subject of data mining necessitates an understanding of various technologies, including machine learning, databases, statistical analysis, computer science algorithms, etc. The Rapid Miner, Oracle Data Mining, Konstanz Information Miner, and others are some of the tools that are particularly valuable for data mining. It is one of the most important business intelligence analyst skills.

Data analysis and modeling

The ability to comprehend and convert data into insights is a must. The BI BA must be able to think conceptually and employ high-level data models to map the real world of the organization conceptually. They also need a clear knowledge of how data travels from operational source systems throughout the company, through various transformation techniques, and where decision-makers utilize it.

Industry knowledge

Data analysis is only as good as the data you’ve gathered from your company’s system. If you want to give useful reports that provide practical insights, you must understand the basics and intricacies of your industry. This implies looking beyond just focusing on your company’s goals and objectives but also understanding the various KPIs required in every field.

It would be best if you also stayed up to speed on the latest developments in business intelligence. You may do this by reading the news every day. If you want to go even deeper, consider taking training courses about what’s new in the field or even subscribing to reports and surveys from major players in the sector.

Data visualization

As a BI analyst, presenting the data is essential to your work. Data visualization skills are crucial for people aiming to be Business Intelligence Analysts. It would help if you understood the different charts that may represent the data, such as Area Charts, Bar Charts, Heat Maps, TreeMaps, Scatter Plots, Gantt Charts, and so on. These charts allow decision-makers to better understand the data by displaying it and seeing how it evolves gradually. It is one of the most crucial business intelligence analyst skills.

Communication

The objective of business intelligence analysts is to turn raw data into information that others can grasp. They must be capable of describing data, explaining their interpretation of it, and outlining actions the firm may undertake due to the analysis. Translating complex technical matters to those unfamiliar with the terminology and processes involved may be a part of this procedure.

Problem-solving

The job of a business intelligence analyst is to interpret data to discover problem areas, then propose solutions based on that data. The responsibilities include devising practical recommendations for improving operations and influencing better decision-making.

Business acumen

Knowing how to use business intelligence software and analyze data isn’t enough to increase the effectiveness of a company’s analysis. You’ll also need an understanding of the firm you’re working for it. This will allow you to conduct analyses in line with the company’s goals.

To develop your business acumen, you must study the company’s business model, grasp its short- and long-term objectives, pinpoint its critical problems, and identify its major rivals. The goal is to think about an executive and operational level so that you can use data more effectively and have a detailed approach to decision-making.

Critical thinking

For business intelligence analysts, critical thinking is a must. It simply implies the ability to methodically examine data to understand ideas at a deeper level and find practical applications for them. This involves asking yourself — do you really need business intelligence software?

In terms of using BI, critical thinking will allow you to identify anomalies in data, assess their impact on the business, and propose effective solutions to issues you encounter. It’s also important to assist you in evaluating your work so that you may improve it later.

Programming

A business intelligence analyst might find programming helpful since it allows them to create scripts or sets of instructions that may automate data-related activities like finding and changing specific data. This can help them organize their tasks and speed up their workflow. Business intelligence analysis frequently utilizes SQL and Python, a sophisticated programming language.

Data reporting

Knowing how to structure and present a technical report, either in Microsoft Excel or HTML, is a must-have. Soft skills such as communication and reporting findings from data are critical for your job as a business intelligence analyst. You should be able to communicate the insights obtained from the data to senior management at the company, such as stakeholders and board members, to make important decisions based on them. It’s also crucial to remember that most decision-makers tend to be technical specialists, so it’s critical to make clear technical ideas using simple language.

Business intelligence skills for resume

Here’s a list of job-related BI skills you may use in resumes, cover letters, job applications, and interviews. There are the most wondered business intelligence analyst skills.

The required abilities will differ depending on the position for which you’re applying, so be sure to examine other lists of abilities.

• Adapting to changing priorities
• Assessing client/end-user needs
• Attention to detail
• Business strategies
• C/C++
• Client relations
• Coaching
• Coding
• Collaboration
• Computer science
• Consulting
• Coping with deadline pressure
• Creating reports
• Creating and running what-if simulations
• Data architecture
• Data controls
• Data management
• Data modeling
• Data visualization
• Debugging data output irregularities 
• Defining data access methods
• Delegating
• Designing enterprise-level reporting
• Designing/modifying data warehouses
• Evaluating business intelligence software
• Extract, transform, load (ETL) testing
• Facilitating the creation of new data-reporting models
• Finding trends/patterns
• IBM Cognos Analytics
• Innovation
• Insights
• Java
• Leading cross-functional teams
• Maintaining technical documentation for solutions
• Managing relationships with vendors
• Managing stress
• MatLab
• Mentoring
• Microsoft Excel
• Microsoft Integration Services
• Microsoft Office
• Microsoft Power BI
• Modeling
• Monitoring data quality
• Motivating staff
• Multitasking
• Negotiating
• Online analytical processing (OLAP)
• Organizational approach
• Programming
• Python
• Reporting tools
• Researching solutions to user problems
• Results-oriented
• SAS
• Statistical analysis
• Statistical knowledge
• Strategic thinking
• Time management
• Training end-users
• Translating high-level design into specific implementation steps
• Web analytic tools

Entry-level business intelligence analyst salary and more: Business intelligence analyst salary in 2022

A business intelligence analyst is a skilled professional in data analysis who may earn up to almost $100,000 per year. A job in this field, which has been named one of the hottest jobs in the STEM (Science, Technology, Engineering, and Mathematics) field by American business magazine Forbes, is highly sought after due to the high demand from a variety of sectors including finance, healthcare, manufacturing, insurance, technology, and e-commerce.

According to PayScale statistics, the yearly pay for a business intelligence analyst ranges from $48,701 to $93,243 in the United States, with a standard salary of $66,645 per year. You can find detailed information about salaries below.

Do you have the skills and qualifications needed to work as a BI analyst? What do you think is the most important feature for a BI analyst? Please share your opinions in the comments.

In this comparison article, we will examine CRM vs marketing automation. To choose the ideal system for their teams and improve company processes, marketing and sales executives need to grasp the distinctions between these software types. A poor decision may slow down or even stop a company’s funnel, affecting its bottom line. That’s why we’ve got an ERP vs. CRM definition comparison for you, and it’s time to conduct another assessment.

CRM software and marketing automation tools may seem similar at first, but they work towards two distinct goals. As a result, it’s critical to know what each program tries to achieve and how incorporating them into your company might be beneficial.

CRM vs Marketing Automation: What do they mean?

“CRM” may be a little hard to understand. Marketing automation (or “email marketing automation”) is sometimes confused with CRM, although it’s a different category. Both streamline the customer journey and improve sales efficiency by automating the process.

What is CRM?

A CRM, in a nutshell, helps you manage customer interactions, contact management, and sales, as well as improve agent productivity. You may keep track of customers’ purchases, phone conversations, and email correspondence. Most importantly, a CRM allows you to enhance one-on-one interactions with clients by optimizing them.

CRM benefits

Customer relationship management (CRM) has many advantages, including:

• Prompting sales reps to contact customers at the optimum moment with ulterior motives and offering them timely alerts on account renewals, birthdays, and anniversaries assists them in keeping their days organized.
• Workflows that are integrated eliminate time-consuming everyday activities and spare staff time.
• Sync your social media pages to improve customer service and promote customer loyalty by following what consumers talk about you on Facebook, Instagram, Snapchat, Twitter, and other channels.
• To boost conversion rates and establish trust with customers, provide specialized promotional material.

What is the best CRM software?

These are the some of the best CRM software:

1. Salesforce
2. Zoho CRM
3. Odoo
4. Dynamics CRM
5. Act!

What is marketing automation?

Marketing automation allows you to analyze, automate, and streamline processes and marketing activities. You may keep track of prospect actions such as website views, blog reads, email opens, and form fills. These applications are used to plan and monitor email campaigns and mass communications. If you’re unsure how to choose marketing automation software or need more information about it, don’t worry. We’ve already compiled a handbook for you.

Marketing automation benefits

Marketing automation may provide several advantages, including:

• Prospect segmentation can improve customer engagement based on prior interactions or interests and desires.
• A unique lead scoring method of it aids in the identification of leads with the greatest conversion potential.
• At the end of each campaign, analytics generates updated data that assess performance and insights.
• Automatically send emails when prospects are most interested in a product or service. You may also create drip campaigns to schedule email series.

What is the best marketing automation software?

These are the some of the best marketing automation software:

1. MailChimp
2. HubSpot Marketing Automation
3. GetResponse
4. Infusionsoft
5. SendinBlue

Why comparing CRM vs marketing automation is important?

Although marketing automation software and customer relationship management (CRM) solutions often share features, they are two distinct concepts. Knowing the distinctions between CRM and marketing automation can help you make better decisions about when to utilize each and why (spoiler: in most situations, both should be utilized).

Marketing automation focuses on the top of the sales funnel by helping you automate repetitive tasks around creating awareness and building interest in your business. With it, you can send targeted mass emails or text messages, nurture cold leads, and monitor the results of your efforts.

CRM takes over as those at the top of the funnel get closer to buying something. These tools focus on helping you build deeper relationships with potential customers by tracking their movement in your sales funnel, logging interactions, and giving your sales team the tools they need to close the deal. CRMs support lead qualification, actions early on in the sales cycle, quote generation, order confirmation, fulfillment, etc. While the two have similar features, their purposes are significantly different. You’d use both of these solutions in a perfect world because neither does the whole funnel.

Rather than working independently, marketing automation and CRM work in tandem. Consider the two systems to be runners in a relay race. Marketing automation prepares prospects ahead of time and hands them over to your CRM so that they can sell at the optimum moment. Instead of choosing one technology over the other, focus your resources on aligning marketing automation and CRM to your company. There’s even a term for it: “marketing.”

We are living in the age of hyperautomation. You may check what is hyperautomation and how it works, but not yet. Now is the time to do a CRM vs marketing automation comparison.

CRM vs marketing automation: The difference

CRM and marketing automation solutions are often mistaken because they help you reach out to consumers and nurture relationships. The distinction is how each technology helps you address specific client demands and nurture connections with prospects throughout the buyer journey.

CRM vs marketing automation: Type of users

Marketing automation solutions are utilized by marketers, whereas salespeople use CRM software. Both provide automation, analytics, and reporting capabilities to simplify daily activities while also providing users with key metrics and insights on the success, efficiency, and effectiveness of marketing campaigns and sales efforts. Customer data may be handled by the same personnel but used for various purposes and operations.

CRM vs marketing automation: Key function

The main purpose of marketing automation is to produce leads resulting from marketing efforts. A lead may be an individual or a firm that expresses interest in your goods or services and might arrive as a consequence of referral or through direct response to your campaigns, such as promotion, publicity, or advertising.

The marketing team uses any top marketing automation software solutions to produce leads that are potential consumers. The marketing must first know everything about the lead or contact, including email address and purchasing habits.

On the other hand, CRM helps salespeople nurture leads from data gathered in the contact or lead database. Salespeople utilize CRM to analyze data, group and qualify people, follow up with offers or discounts, and similar initiatives to convert leads into buying customers.

The sales team makes use of CRM to keep current clients. The software includes tools for collecting data and feedback from various customer channels and analyzing that information to provide the sales team with customers’ behavior, interaction history with your firm, and purchasing trends. Salespeople can use their knowledge of the customer to develop targeted offers and loyalty programs to increase client retention.

CRM vs marketing automation: Goal

Marketing automation is meant to help marketers generate marketing qualified leads (MQLs) for sales. Meanwhile, CRM’s objective is to convert MQLs into sales-qualified leads (SQLs) and, eventually, into sales. MQL refers to engaged leads, whereas SQL refers to validated prospects. The objectives are different, as you can see where one comes to an end and the other begins in the sales funnel. Still, marketing automation and CRM have overlapping responsibilities to convert leads into customers.

Let’s take a look at an example. Your company’s marketing staff generates landing pages and collects clicks on your website and leads interacting with your content. These leads are beginning to communicate, but it remains to be seen how interested they are, so your team engages them by providing useful information to get them started. Contact leads are then rated depending on the actions or reactions they take. These people become MQLs when they are ready to be passed on to marketing.

The process continues when the sales team takes control, engages with the leads, and assesses their level of interest and capacity to buy. Leads that have been deemed viable prospects are now labeled SQLs. Marketing automation and CRM features and capacities come into play in this specific pathway from MQL to SQL and the overall process that covers the stages from lead to customer. Lead prospecting automation tools may now assist you in achieving the goals mentioned above.

CRM vs marketing automation: Role in the buyer’s journey

You can infer the roles played by marketing automation and CRM from the preceding example. The former raises consumer awareness of your items and services, while the latter is to get people ready to buy. The two funnels that make up the buyer’s journey are split into different responsibilities. Yet, they are also complementary and combine the two funnel systems within the sales process. The following image displays marketing automation and CRM’s involvement in each phase of the sales pipeline.

How to choose between them?

CRMs and marketing automation solutions are designed for various sections of your company.

The ideal option is Customer Relationship Management software if you want to streamline or standardize the sales funnel. It has a lot of management capabilities that cover every stage of the sales process, from when a lead enters the funnel to when a deal is closed and beyond. You’ll want to invest in a CRM tool if you aim to enhance your relationship with prospects and existing clients.

Marketing automation software is an excellent choice for marketing teams looking to save time without sacrificing quality. It manages many publication platforms simultaneously, allowing marketers to use one central location for all of their campaigns and activities. If you want to automate your marketing processes while also focusing on lead creation, you should choose marketing automation software.

Can I use CRM and marketing automation software together?

It’s typical for mid-sized and huge businesses to combine tools from various departments. This helps to keep data isolated. CRM and marketing automation solutions are ideal complements since they give a comprehensive insight into the customer lifecycle.

You’ll be able to manage the sales cycle from beginning to end, delve deep into leads, and utilize various tools by combining sales and marketing teams. You can send email marketing campaigns for existing clients and change which milestones influence sales and lead qualification.

Use the table below to see which option is suitable for you:

Conclusion

The two terms are frequently used interchangeably, and while they may seem similar, they’re not. Marketing automation software and CRMs operate under different principles in the same class. While CRMs are focused on the sales aspect of a company, marketing automation tools are more concerned with top-of-funnel marketing-related activities. Both are useful in their ways, and they are quite beneficial to firms of all sizes, whether they collaborate or operate independently.

Would you choose CRM or marketing automation if you had to pick one? Do you prefer a system with integrated capabilities or two distinct solutions? Is it better to utilize a CRM and an MA, or vice versa? Let us know your thoughts on this post in the comments section below!

Data democratization is the practice of making digital data available to the average non-technical user of information systems without requiring IT’s assistance.

End of a reign

A few data analysts with the knowledge and skills to properly arrange, crunch, and interpret data for their company had wielded enormous power over organizations. This happened due to necessity – most employees were uneducated on employing the growing tide of data effectively. With the advent of technologies that enable data to be shared and interpreted by non-data experts, things have changed. Data democratization allows data to flow freely from the hands of a few experts into the hands of countless employees throughout a business, acting as a foundation for self-service analytics.

A recent Google Cloud and Harvard Business Review poll showed that 97% of the industry leaders believe free access to data and analytics throughout an organization is essential to success. However, only 60% of respondents think their companies presently distribute access equally. According to Exasol’s findings, 90% of CEOs and data specialists are focusing on data democratization for their businesses.

What is data democratization and why is it important?

Data democratization implies that everyone has access to data. The objective is for anybody to utilize data in any manner to make smart judgments with no limits on access or comprehension.

Data democratization entails that everyone has access to data, and there are no gatekeepers preventing people from accessing it. It necessitates that we provide easy access to the data and instructions on how to interpret it so that individuals may utilize it to hurry decision-making and uncover possibilities for an organization. The objective is for anybody at any time to use data.

Until recently, IT departments controlled the data. Marketers, business analysts, and executives used the data to make commercial judgments, but they had to go through with the IT department to obtain it. This was how it’s been for most of five decades, and there are still a few people who believe it should stay that way. However, data democratization aims otherwise.

The advocates of data democratization believe that allowing everyone accesses to the same data across all business teams gives your company a competitive edge. More individuals with diverse expertise who have easy and quick access to the data can help your business discover and act on key business insights. Many experts think that data democratization is a game-changer.

The capacity to access and comprehend data instantly will lead to faster decision-making, which will result in more agile teams. Those companies with an advantage over slower data-stingy organizations would have a leg up on the competition.

When a business gives data access to all levels of the organization, it allows individuals of all ownership and responsibility to utilize such data in their decision-making. Team members are more data-driven when data democratization encourages them to go around data to accomplish tasks on time. When bad or good events occur, the responsible professionals are promptly informed, and they can examine and comprehend those anomalies to help them be proactively aware.

Finally, data democratization is a must for marketers trying to deliver the best customer experience possible. The question they should be asking isn’t whether data democratization is a necessity; rather, it’s how they can get it implemented quickly and effectively for their company.

How to democratize data?

Data democratization implies a financial, software, and training commitment from management. But data democratization can’t be dissociated from data governance. Data democratization is an act of a data governance strategy.

Breaking down data silos is a necessary step to user empowerment. This can’t be done with generic analytics tools that can desegregate and link formerly segmented data, making it easier to access from a single location.

Ideally, according to their position, the tools will filter the information and visualizations supplied to each individual — whether they are a senior executive, a director, or a designer. Marketing managers, for example, will require data that lets them analyze customer groups leading up to a new campaign. On the other hand, CMOs will need data to evaluate marketing ROI as they create next year’s budgets.

For the most part, organizations place a high value on employee data visualization. These tools need to help people make sense of their data. Customers must understand how the information is represented graphically. These visualizations must be in line with corporate KPIs, such as metrics, goals, targets, and objectives aligned from the top that enable data-driven decisions.

Team training becomes the next crucial step with the appropriate tools in place. Because data democratization is based on self-service analytics, every team member must be trained to a certain level of competence with the technology, ideas, and procedures required to participate.

Finally, you can’t have a democracy without checks and balances, which is the final component of data governance. Data can be misused or mishandled in a variety of ways. As a result, setting up a data center of excellence is necessary to ensure that data usage is kept on track. Companies should encourage the adoption of data usage in line with their capacity to own data accuracy.

Steps for a successful data democratization

Three simple actions may be taken by businesses to begin the process of data democratization:

1. Build a robust data foundation that comprises an extensive range of internal and external data sources across the entire market, not just one brand or product. Data feeds that are constantly updated will guarantee that all information remains up to date, allowing leaders to make timely decisions based on changes in the market landscape.
2. Make data insights understandable by utilizing advanced analytics. Today, sophisticated machine learning (ML) and natural language processing (NLP)” algorithms can extract context from data by generating simplified representations of text and applying macros (or rules) to those representations to determine meanings. NLP can analyze a data point’s tone and connect it with taxonomies’ unique characteristics, allowing you to go deeper into the information.
3. Scale the insights within a user-friendly experience. The future of data accessible to everyone is accompanied by tools that enable individuals across a company to access simple-to-understand. These data-driven narratives address issues and solve problems. The key is for these tools to be attentive to user requirements; this is lacking in most of today’s data visualization and dashboard tools.

Benefits of data democratization

The advantages of data democracy become more apparent as organizations comprehend and effectively tackle the risks listed above:

• Improved decision-making: Businesses can benefit from a first-to-market position by taking advantage of current trends and consumer needs. The data is accessible to all employees, which allows the entire organization to make comparable and aligned judgments.
• Employee empowerment: Teams and individuals can have greater confidence in taking on a company problem with access to data. Data scientists devote about half of their time to making data usable. Reducing internal processes and diverting data teams toward more strategic activities may save time and effort.
• More data investment ROI: Empowering everyone in your company to utilize data to make informed judgments will guarantee you get the most out of every data point you invested.
• Better customer insights: There’s a plethora of external data on the market and customers. Understanding this data allows you to make better consumer-centric decisions that lead to a superior customer experience and greater market share.
• Unparalleled flexibility: When the market or consumer changes, the data will reflect it. Then you will be able to make proactive rather than reactive judgments.

Why do some organizations approach data democratization with caution?

Some organizations are still concerned that non-technical team members could misinterpret data, and these staff would make poor judgments due to their incorrect understanding of the data.

Another argument supports the notion that as the number of people who have access to data rises, the risk of data security breaches and difficulties in maintaining data integrity increases.

Although there has been significant progress in recent years, data silos still exist. This reality still makes it difficult for people from various departments to access information and view it.

Another worry about data decentralization is the potential for duplication of effort across several teams, which might be more expensive than a centralized analysis team.

Once a silo user, always a silo user?

Changing company cultures is easier said than done. A significant part of the difficulties that complicate data democratization stems from employee and team habits, which can be evaluated within the scope of company culture. Moreover, this situation often arises from the past decisions and approaches of the management. Teams are sometimes organized independently. They don’t share internal or external data to make decisions, and there isn’t a strong culture of sharing insights across functions.

These ongoing habits have increased the need for data scientists, analysts, and other technical experts to interpret data for many companies. Some of these companies have been so clogged with requests that decision-makers have come up with workarounds or stopped looking for data as part of their procedure. It may be tough to transform entrenched cultural habits, which will require a comprehensive overhaul of the company process.

Finally, as the technology gathers more and more data, the quantity of data sets has increased. Unless that data is gathered and contextualized, most people will not comprehend it. Data dashboards and visualizations have popped up as possible solutions to these challenges. 

What are the most significant business intelligence benefits and the technologies used to exploit them? You’ve undoubtedly heard the adage “work smarter, not harder.” That phrase might as well have been coined for business intelligence software (BI). Business intelligence software (BI) is made up of various data analytics tools used to analyze and manage data from your company’s operations. Business intelligence has different advantages, including sophisticated visualization tools that allow companies to keep track of sales, shipments, and productivity. It also offers extensive data analysis with brightly illustrated reports that are simple to understand but let’s deep dive into the benefits and also disadvantages of BI.

What is the importance of business intelligence benefits?

Business intelligence (BI) is a type of software that takes company data and displays it in useful ways such as reports, dashboards, charts, and graphs to users. The ability to access different types of data — past and current, third-party and in-house, and semi-structured and unstructured data – is one of the Highlights Of BI Tools. BI users can utilize these tools to examine a variety of data. Data, data warehouses, and data access are the three main basic components of a business intelligence software.

In today’s data-driven world, businesses are overwhelmed with information, and those who wish to work smarter are investing in technologies to manage and comprehend it. The era of big data has arrived. We are, in fact, generating so much data that 90% of the data ever collected is now being generated. Adopting modern technology may be difficult, but BI software usually provides a positive return even if the outcomes aren’t immediately apparent.

The core idea at the heart and soul of business intelligence is “test, examine the data, modify.” It’s all about using data to grasp reality better so your company can make better long-term decisions (rather than relying only on gut instinct or corporate inertia).

Although a company-wide business intelligence system is time-consuming and expensive to establish, the advantages of business intelligence greatly outweigh the costs when put in place and used correctly. So, what are these advantages?

What are the business intelligence benefits?

The advantages of business intelligence and analytics are numerous and varied, but they all have one thing in common: they provide power. The power of information. They may change your company and way of conducting business significantly, no matter how small or large the unit they touch.

Revenue growth

Increasing revenue is a critical objective for any business. Business intelligence solutions may be used to enhance questions about why things occurred and discover sales drawbacks by making comparisons across different dimensions and detecting sales shortcomings. Revenue is more likely to arise when businesses listen to their customers, watch their rivals, and improve their operations. It is one of the most important business intelligence benefits.

Cost management

It’s critical for a business to have its financial budget in order. BI software increases cost control and lower through the optimization of operational efficiency. It identifies places in your company where you might save money. Doing and maintaining an inventory, for example, tends to raise costs.

The solution uses unprocessed data to solve issues such as what sections of your business incur the most costs and how to spot them. This technique may be used in marketing. The BI tools analyze which list sources provide high returns from the study of direct mail and other marketing activities. It automates sales operations, procurement, inventory comprehension, and HR analytics using artificial intelligence (AI). Do you know how AI transforming business intelligence?

Risk reduction

The main reason for risks is human involvement in Excel spreadsheets. For example, reporting on spreadsheets may require manual data entry; errors may be made when data is copied and pasted into the incorrect cell or formula is incorrectly constructed. Experienced individuals can make mistakes while cutting, copying, and creating formulas. Because there is no audit trail, identifying and locating mistakes is difficult. This can have a significant impact on things like budget and remuneration calculations. 

Document or process controls are also missing, making it difficult to determine who created the spreadsheet, who updated it, and when, why, and how it was performed. Maintaining a spreadsheet that costs your business person hours is also a time-consuming job of maintaining a spreadsheet.

Business Intelligence tools may connect to various data sources and provide reporting solutions. Having information, insights, and facts allows you to make educated business decisions that minimize potential hazards.

Improved customer experiences

Your company may improve client experiences by monitoring KPIs such as customer satisfaction or success or the time it takes for technicians to respond. We also see this on big e-commerce sites that recommend items and services. If your firm sells a product or a service, you may use business intelligence tools to assist you in many ways.

Improved employee experiences

Employee satisfaction is not a difficult concept to grasp. Employees want to feel understood, acknowledged, and connected. Sharing dashboards with staff and allowing them to track progress and accomplishments is a fantastic method to achieve it. You may even send automated updates to your staff to keep them engaged and interested. There’s a direct link between employee engagement and employee happiness, and business intelligence tools can help you build that bond!

Data visibility

While it may be simple, statistical analytics are more difficult to comprehend and operate. It’s a struggle to make data and analytics more accessible while also giving data-driven insights from throughout your company. Data visualizations have evolved into a de facto standard for modern BI systems due to their crucial function in large data and complex analytics projects. Visualization tools allow you to look at an otherwise huge amount of information quickly.

With sophisticated techniques such as bar, pie, and fever charts, heat maps, sparklines, and infographics in today’s applications, correlations, patterns, and trends are highlighted. Bar charts, pie charts, sparklines, and infographics are just a few examples of sophisticated methods that may be used to show relationships between data. It is one of the most efficient business intelligence benefits.

Speed to decision

Previously, getting answers to data-driven questions necessitated a lengthy time because only specialists had access to and understood the data. The response might take days or even weeks to reach the appropriate member. Search operations and transactional interactions are now done in seconds thanks to BIs’ data warehouses with the help of human analysts, who can quickly interpret them.

BI software also enables real-time decision-making, allowing you to make fast decisions, especially in the business world. Suppose a competitor drops their product prices, for example. In that case, you must be prepared for actions and strategies to counteract that will not affect your profitability within a year. It would help if you made a decision right now. Fast commitments should only be taken after careful analysis provided by your BI tool.

Performance measurement

Feedback is essential for monitoring and assessing the success of a company’s operations. This information informs the appropriate individuals on what to do and where to focus their efforts to achieve business goals.

BI solutions, in addition to financial statements and sales results, cover a variety of metrics that are frequently accessed, including employee performance/productivity, employee happiness, BI integration system usage, and decision-making or task-completion timeliness. It is one of the most important business intelligence benefits for your team.

Knowledge collaboration

The advantages of using BI tools are numerous. They allow for effective collaboration and communication among members to maintain data up to date with a single source of truth. It improves consistency and monitoring of changes. Notifications let everyone know when modifications or new updates are made to a file. If you wonder how business intelligence creates collaboration, go to this article.

Predictive modeling

Predictive modeling is a type of data science that aims to make educated predictions about the future by using data mining, machine learning, and a variety of statistical methods for modeling. The predictive models utilize certain trends discovered in historical and transactional data to identify business possibilities and threats.

Data-driven decisions

Having accurate data on the customer, client, or employee responses to initiatives allows your business to measure their performance, adapt quickly, and see whether similar efforts will help achieve corporate objectives.

Boost ROI

When organizations focus on activities that do not align with the corporate strategy, they will almost certainly incur significant expenses. BI allows you to create metrics and KPIs that are consistent with the company’s strategy, providing insight into business performance and ROI.

Identify trends and patterns

One of the primary advantages of business intelligence and analytics is the capacity to make informed data-based judgments. This advantage complements the fact that analytics give organizations methods to discover trends and patterns that will help them optimize resources and procedures. Users can obtain a deeper understanding of their companies due to business intelligence and analytics. It is one of the most important business intelligence benefits for the future.

Data mining

The ability of business analytics tools to integrate with the appropriate performance monitoring and analysis solutions allows them to be used in an advanced way. Data mining is a method of analyzing data to identify trends and draw insights. It consists of five phases: collection, warehousing and storage, organization, analysis, and presentation. Some BI systems can carry out all of these steps for an organization without the assistance of business analytics tools, big Data analytics systems, or data warehouse platforms.

Improve inventory management

The benefits of the business intelligence systems may extend into purchasing, procurement, and inventory management. Users may produce reports to determine when new goods should be purchased. BI also keeps track of items being released, allowing you to anticipate future buying patterns better and minimize inventory waste.

Business intelligence disadvantages

Business intelligence, like everything else, has disadvantages:

Data breaches

There’s always the risk of leaks with any data analysis system, and one of the most serious worries is that a security breach might expose your company, clients, or workers to harm.

High prices

For some organizations, investing in business intelligence tools might be costly.

Difficulty analyzing different data sources

The more comprehensive your BI, the more data sources you’ll need. A wide range of sources can aid in providing comprehensive analytics, but systems may have difficulty working across different platforms.

Poor data quality

You have more information at your fingertips than ever before in today’s digital world, but this may be a problem. A deluge of data might render a lot of what your BI solutions evaluate irrelevant or useless, blurring analysis and slowing down procedures.

Resistance to adoption

Not all of the drawbacks of BI are related to the software. Employees or departments not wanting to connect it to their operations is one of BI’s most serious roadblocks. If your firm doesn’t implement these systems in all areas, they won’t be as efficient.

Conclusion

There are several advantages and a few drawbacks to business intelligence software. It is an expanding market with several demonstrated advantages when properly implemented. Users may gain focused insight into your company’s past, present, and future to help them make healthy business decisions. BI software collects, organizes, mines, and visualizes critical KPIs. It minimizes waste and guesswork while also improving sales intelligence efficiency. This powerful combination of features and benefits of business intelligence software provides users a competitive advantage that can make all the difference in today’s market. If you wonder top 20 BI tools, we already prepared a list for you.

Which business intelligence benefits do you like the most? Let us know in the comments!

These days, branding is a highly calibrated art form. It used to be that there was a lot of guesswork. Thanks to data monitoring tools, and the many competent professionals trained to use them, those days are a thing of the past. 

Data monitoring tools work by helping compile and process vast swaths of information into comprehensible information sets that, with the help of the right professionals, can be used to significant effect. 

In this article, we look at six tools that you can use to track and improve your brand’s campaign. 

How to measure brand engagement: The best data monitoring tools

“Brand engagement” is a fluid term, the success or failure of which is defined first by what you hope to achieve. For example, suppose you are launching a social media campaign to generate awareness. In that case, the health of your brand engagement could be defined simply by how often consumers interact with or share your content. 

On the other hand, if you are trying to shift your brand identity into, say, being a company known for customer service or environmentally friendly business practices, the monitoring process can get a little bit more complicated. 

Here, you might look at customer satisfaction scores, exit surveys, churn rates, and other factors that illuminate harder to quantify considerations. Below, we look at tools that can help with all stages of brand campaign performance monitoring. 

HootSuite

Broadly considered one of the most user-friendly brand performance monitoring tools on the market, HootSuite allows users to survey the health of their ad campaign on virtually any social media platform at a glance. 

The viewer interface can be tweaked to provide basic insights like overall engagement or dialed in to focus on more specific factors such as keyword performance or the health of your hashtag game. 

Altryx Designer

Altryx Designer is a data mining tool built for analysts who don’t necessarily have a background in coding. This makes it somewhat more accessible. The system can also be integrated into a range of other applications, making it much easier to cleanly port unstructured and structured data into the system to reach conclusions.

It can be highly refined to zero in on your brand’s specific goals, and it can be combined with hundreds of other analytic tools to provide a more customized user experience. 

RapidMinder Studio

RapidMiner Studio is a data mining application that boasts a robust free version, with the option for paid additional services that increase your analytic capabilities. The tool is known for blending structured and unstructured data and can harvest information from nearly any source. 

User friendly and known for its accessibility, RapidMiner Studios includes a vast suite of visualization tools that make the information piles easy to view in the user’s preferred manner. 

Sisense for Cloud Data Data Teams

Sisense for Cloud Data Teams is a robust tool known for its features that make its information accessible and easy to comprehend for the layperson.

Capable of distilling data from nearly any source, Sisense is a dependable application for harvesting massive quantities of info in a conducive format for collaboration—making it the perfect option for data-driven teamwork. 

Tibco Data Science

Tibco Data Science is a tool that distinguishes itself by being at once comprehensive and accessible. With over 16,000 different data set options, it can provide a robust look at any aspect of your brand management that you are hoping to gain insights from. 

It features communication integrations that make the tool highly conducive to collaboration and relies on artificial intelligence to help automate processes. 

SAS Data Mining and Machine Learning

SAS Data Mining features a point and click interface defined for accessibility to people who come in at a wide range of different skill levels. It includes collaborative functionality designed for easy teamwork and integrates with many compatible software programs for enhanced performance. 

SAS Data Mining can harvest from many sources and uses machine learning and automation to make information sets both manageable and easy to understand. 

The right data monitoring tool for you

As the list shows, different objectives will shape the tool you reach for. Offices that operate in a highly collaborative environment may go for a much different tool than those with only one or two data specialists. Similarly, a business that already has a matured tech stack will need to look for a tool that has compatible integrations. 

Choosing the right option is an art form that can significantly affect your workflow. As you peruse your options, remember that accessibility is critical. While the data analyst may have a good idea of what they are looking at, implementation will ultimately be everyone’s job. 

The key to finding success with your monitoring tools hinges on the program’s ability to do the most good for the widest number of employees. The data scientists will use the information to conclude, but the rest of your staff takes those conclusions and uses them to refine your brand image. 

What is the importance of business intelligence clouds? Businesses of all kinds are interested in improving their operations and taking strategic decisions to new heights of value. Business intelligence is what helps them do it.

Cloud solutions enable organizations to access the right data in the correct format and at the right time, regardless of their size or location. On the other hand, cloud computing has advanced with improvements in technology and is becoming a business requirement rather than a technological accomplishment. According to the BI and Data Management in the Cloud: Issues and Trends research, Cloud BI adoption has risen from 29% to 43% over four years, with around half of those interviewed expressing an interest in using public cloud technologies for cloud BI analytics, and data management.

What is cloud BI (Business Intelligence Cloud)?

Cloud-based business intelligence, or cloud BI, is the art of taking raw data and turning it into usable insights either partially or entirely within a cloud environment. Cloud BI allows businesses to get all the information to make data-driven decisions without dealing with hardware costs or hassles. All SaaS-based business intelligence is cloud BI.

Companies are still looking for techniques to interpret data and efficiently manage their operations. Business intelligence (BI) software is at the forefront of these efforts. Cloud BI is a quick way to get access to information, so it’s gaining popularity. The major advantage of Cloud BI is that it allows users to access data from anywhere. Still, cloud investments also provide greater flexibility, faster reactions to market changes, and the ability to expand.

For the previous decade, the cloud was seen as a cost-cutting technique. Still, it is now enabling a digital business model that allows firms to have a remote workforce and interact with consumers from wherever in the world. According to the research, the global Cloud Computing Industry was valued at $321 billion in 2019 and is anticipated to reach $1025.9 billion by 2026.” Do you know cloud computing jobs are also on the rise? Let’s check cloud computing job requirements, trends, and more.

According to a DELL report, organizations that invest in big data, cloud computing, mobility, and security are seeing greater than 53 percent revenue growth over their rivals. The spread of the epidemic and subsequent changes in company and consumer behavior have prompted mid-market firms to value cloud computing. Businesses require cloud computing solutions to operate with a remote workforce, enhance customer service, and increase revenue and earnings.

Cloud computing and business intelligence go hand in hand. Business intelligence is about providing the right information to the right people at the right time, and cloud computing allows for lightweight, agile access to BI solutions. Cloud BI applications offer flexibility and portability, allowing them to be used on various devices and web browsers. Traditional software hurdles such as the need to access the application on-site are overcome. One of the other ideal fits for business intelligence is Artificial Intelligence. So, how is AI transforming business intelligence?

Cloud-based, Software-as-a-Service (SaaS), and virtualization technologies are faster, scalable, on-demand, secure, and come at a lower cost than traditional means of IT administration. Cloud services are mobile-friendly so that users may access them from any place using their smartphones.

What is Software as a Service BI (SaaS BI)?

Business intelligence (BI) software as a service (SaaS BI) is an access model for business intelligence. Applications are generally installed outside the company’s firewall at a hosted location and accessed by an end-user with a secure Internet connection. The technology is also known as on-demand BI or cloud BI. Vendors sell it under a subscription or pay-as-you-go basis rather than the more common software licensing model with yearly maintenance charges.

SaaS BI enables organizations to utilize BI tools without installing, operating, and maintaining them on-premises, allowing clients to focus on producing BI reports and analytical questions. SaaS technology enables businesses to expand their BI systems as usage increases without requiring any equipment purchases.

Cloud-based Business Intelligence (SaaS BI) might be a viable option when there isn’t enough money to buy BI software and related hardware. Because there are no upfront purchase expenses or additional personnel requirements needed to run the BI system, the total cost of ownership (TCO) may be lower than with on-premises software. However, overall SaaS BI costs will vary depending on how much usage the tools get.

The idea of software as a service (SaaS) is already well-known among businesses. However, there are certain issues to consider when using SaaS BI. For instance, the analysis tools may not include all of the functions that on-premise software products have – making them less complicated to use and less functional. Sending corporate data across a firewall raises concerns for some IT managers. Some suppliers have built private analytic clouds that operate behind a customer’s firewall to alleviate those concerns.

Benefits of business intelligence clouds

Cloud BI solutions are gaining ground in the business world, with many firms realizing the advantages of data analytics. More than ever, companies need accurate data and reliable insights to make better decisions. The SaaS providers function as the primary interface to the company user’s community. Cloud BI is a method of delivering BI capabilities as a service. There are wonderful benefits, but did you know the 5 risks of the clouds rapid expansion?

Here are some important advantages of Cloud computing for business intelligence:

Cost efficiency

Cloud BI solutions provide businesses with robust data analytics and reporting tools to enhance sales and marketing efforts. These applications also support company-wide collaboration initiatives by providing Sales and Marketing personnel with self-service analytics capabilities. Cloud business intelligence systems generate the most economic value for organizations by allowing workers to conduct their analyses without requiring IT resources, giving it scalability and flexibility to lower maintenance and security expenditures. IT departments can now focus on creative solutions and systems that generate significant company growth.

Flexibility and scalability

Business users will be able to improve financial control over IT projects and have more adaptability to scale usage up or down as needs change with Cloud BI solutions. With Cloud BI solutions, technical users will have greater flexibility in adding new data sources and experimenting with analytical methods. Business users will be able to maintain better fiscal management of IT initiatives while also having more freedom to adjust usage as needed. Furthermore, resources in the Cloud may scale up and down automatically and swiftly, allowing for thousands of simultaneous users. This means that customers can quickly increase their software usage without investing in additional hardware or software.

Reliability

The cloud-based business intelligence solutions are more credible and flexible, with a shorter time to market. They improve as they attain scalability and redundancy through various duplicate platforms like Birst, Domo, Tableau Online, etc. Data storage is available in reliable and secure locations, with many users able to access resources worldwide. The platform’s redundancy, scaling, and the vendors also handle distribution.

Enhanced data sharing capabilities

Cloud apps enable data access to be shared across locations and facilitate cross-border data sharing because they are delivered over the Internet and outside a company’s firewall.

No capital expenditure

The Cloud’s low TCO (total cost of ownership) is a key selling point. Companies pay for a service that they utilize with the Cloud. With this plan, Cloud computing allows businesses to manage better their CAPEX (capital expenditure) and OPEX (operations expenditure). As a result, BI’s advantages may be expanded faster to more people in the company.

Data security

A common misconception is that cloud-based information management solutions are less safe. Thanks to various data security methods and features, cloud-based business intelligence systems are incredibly secure. Most analytics tools employ data encryption, data segregation, security patches, multi-tiered caching, and other security measures to provide regulatory compliance. The solutions also employ more sophisticated technologies such as user fingerprint and voice recognition authentication to guarantee data security.

You can do them in all green. Learn more about what is green computing applications and save the world a little bit.

Business intelligence cloud deployment models

These are the three types of Clouds that may be used to deploy a Cloud-based Business Intelligence solution:

1. Public Cloud
2. Private Cloud
3. Hybrid Cloud

Public Cloud

A Public Cloud’s infrastructure costs are spread across Cloud tenants, making it the most cost-effective option for BI. It’s a great choice for small and mid-sized businesses on a tight budget or people dealing with large data sets.

Private Cloud

If you’re concerned about regulatory compliance or data security, a BI system should be deployed in a private cloud. The private cloud is the most costly cloud alternative because it specializes in providing dedicated storage and processing capacity only for your organization’s use.

Hybrid Cloud

If you can’t afford to put your complete BI system in the Private Cloud but must adhere to stringent regulations (HIPAA, GLBA, GDPR, etc.), a hybrid cloud is the way to go. This computing setting combines the features of a public and a private Cloud. If you pick this path, you may store and analyze sensor data in the Private Cloud while working with huge data in the Public Cloud.

Go to this page to learn more about hybrid cloud computing benefits and use cases.

How to choose a business intelligence cloud?

There are more options for moving your business intelligence to the cloud every day. Cloud BI software has many features, but here are a few to consider.

Data management

A cloud BI software should be able to pull data from various sources, clean it for high-quality results, and turn it into a consumable format. Data integration is an important aspect of business intelligence.

Advanced analytics

More advanced analytics solutions, such as data mining and root-cause analysis, will become increasingly important in multiple sectors. As a result, cloud BI tools should be able to provide.

Reporting and visualization

Regardless of their analytics expertise, every user can understand the data insights they obtain using these reports and visualizations.

Collaboration

The more businesses can share useful information among departments and teams, the better. The more your cloud BI software enables you to exchange analytics, the better easily.

3 best business intelligence clouds (Cloud BIs)

Here are some of the most popular Cloud Business Intelligence software:

SAS

SAS is one of the most well-known and popular business intelligence solutions providers. Visual analytics deliver real-time reports of your network diagram, decision trees, and other BI analysis without requiring you to code or customize anything.

Industries such as communications, health care, manufacturing, banking, education, and others may benefit from this BI solution’s innovative collaboration tools. You can quickly identify data anomalies and predict company results. Many SAS BI users utilize analytics reports to make more informed business decisions. Analytics solutions are an excellent instrument for data mining, data visualization, forecasting, and statistical analysis in SAS BI environments.

Adaptive Insights

Thanks to a vendor partnership with Adaptive Insights, you can use their software-as-a-service (SaaS) to manage your financial planning, consolidation, reporting, and more. The dashboard for reporting, data analysis, and data mining pulls in sales data, human resources information, marketing insights, and other professional services to provide a bird’s eye view of your company’s operations.

Adaptive Insights’ Business Intelligence Solutions are designed for medium- to enterprise-level organizations in numerous sectors, including business services, software, and technology, healthcare, non-profit, education, retail, etc. Adaptive Insights has assisted thousands of organizations worldwide in improving their financial statements and cash flow plans, budgets, and expenses plans.

IBM Cognos Analytics

It’s no surprise that IBM is a major player in the cloud-based BI sector with Cognos Analytics. IBM’s Cognos also links to SPSS, its predictive analytics product, to help businesses make better decisions.

IBM’s Business Intelligence solutions are used in every sector, and its BI applications are utilized across the board. For a scalable and user-friendly solution that reduces data analysis time by 50% while also increasing ROI by 20%, IBM uses dynamic queries and comprehensive data.

If you wonder about more BI tools, you can check the top 20 BI tools list.

Do you want to enhance the quality of your organization’s marketing but aren’t sure how to choose marketing automation software? We’ve got all the answers. Marketing automation is a major requirement for today’s marketers. It’s quickly becoming a must-have tool for commercial organizations that want to improve customer satisfaction, increase return on investment, and work more efficiently. Competitive firms—those that wish to enhance client happiness, optimize ROI and work more productively — recognize the need for marketing automation. Assess your software needs and objectives before selecting a solution. Then look at the marketing automation software market to see which one is best suited to help you accomplish your marketing goals.

How to choose marketing automation software? First, you have to understand what it is

Businesses may use marketing automation software to automate, simplify, and analyze marketing activities and workflows, such as lead generation, segmentation, lead capture and nurturing, relationship marketing, customer retention, analytics, and account-based marketing. The use of marketing automation software can help you save time by speeding up processes while also providing customized, targeted experiences for your clients while eliminating tedious and time-consuming chores for you and your staff.

Marketing automation, when used correctly, can help you nurture leads by using well-targeted material to convert them into delighted clients. It concentrates your efforts on delivering superior client experiences.

However, marketing automation won’t solve all of your problems. Consider it when you choose marketing automation software. The software is designed to automate certain procedures in order for you to save time.

Organizations may use automation software to address a variety of marketing challenges, including:

• Using tracking and optimization to increase user engagement
• The ability to convert leads into paying customers
• Fixing a “leaky” sales funnel
• Leads that meet the requirements
• Closing marketing gaps and inefficiencies

Marketing Automation (MA) is a set of technologies and capabilities that allow for automated, triggered events such as email, text messaging, variable content, lead scoring, and conditional logic rules. CRM and marketing automation (MA) are two separate sets of services that might be combined in one platform or distributed across two systems that communicate with each other. There is no such thing as a one-size-fits-all solution. That’s part of the problem when choosing the best technology(s) for your specific requirements.

You may now communicate with consumers based on their characteristics, reactions, engagement, and channel preferences thanks to a CRM system or database of record and a mechanism to automate marketing communications. That’s far from vague; it means to speak with customers as individuals in the most appropriate way.

What is CRM?

The database of records for prospects and/or customers is generally referred to as Customer Relationship Management (CRM). They come in various shapes and sizes, so we use the word generically.

Who uses marketing automation software?

Marketing automation is the practice of automating time-consuming or mundane marketing activities. It’s intended to help you streamline your marketing approach. Several sectors, including retail, healthcare, financial, and real estate organizations, use marketing automation software.

The following are the top industries that use marketing automation tools:

B2B companies

The purpose of marketing to consumers is to gain brand loyalty and repeat purchases. Business-to-business (B2B) firms, on the other hand, sell their services directly to other companies. Marketing automation software allows B2B enterprises to manage their audience, client data, and campaign success indicators while reducing the danger of money wasted on ineffective marketing efforts.

The following are some of the major B2B marketers that have already implemented marketing automation software:

• Software and internet companies
• Telecommunications companies
• Computer and electronics companies
• Financial services companies

B2C companies

Business-to-consumer (B2C) firms employ marketing automation software to improve their multichannel marketing approach by discovering areas for improvement, tracking results, and automating tedious operations. To cut overhead, increase sales, and enhance data analysis, B2C firms also employ marketing automation.

B2C marketers that have implemented marketing automation software into their organizations include:

• Retail and e-commerce businesses
• Manufacturing companies
• Real estate companies

How does marketing automation software work?

Before you choose marketing automation software, you should know how it work. The majority of marketing automation solutions are made up of four components:

• A central marketing database: This is where data on future leads, customer interactions, and user behaviors are kept and analyzed to assist you in segmenting and tailoring your marketing messages to clients.
• An engagement marketing engine: Create, manage, and automate your marketing processes and conversations on all of your channels, both online and offline.

• An analytics engine: This is how you’ll check, measure, and improve the return on investment and revenue effects of your marketing. This engine can show you what works well, what doesn’t work at all, and where your campaign needs to be improved.
• A marketing technology stack: This stack, also known as a MarTech stack, comprises all the other applications you employ (SaaS platforms, social media tools, content management systems, communication tools, and analytics platforms) to complete your marketing objectives.

What are the most common marketing automation features?

You have to consider these when you choose marketing automation software. Although marketing automation tools may vary considerably in terms of customization, integration, and personalization, some similar characteristics are present in MA solutions for various procedures. The following are the top five most frequent characteristics of marketing automation tools:

• Email marketing automation
• Lead nurturing
• Social media automation
• Analytics and reporting
• SEO, paid media, and digital advertising

Email marketing automation

Email is one of the most-used forms of digital messaging, and it is still quite effective. Although spam has desensitized consumers to email marketing, email marketing remains one of the most efficient ways to connect with your target audience. This makes it an ideal starting point for marketing automation.

One of the most important parts of what MA vendors have to offer is email marketing, which allows you to send emails to numerous audiences in bulk. Advanced platforms automatically send emails after prospects perform specific actions, such as filling out a form or downloading content. Marketers may also utilize these technologies to create, modify, and embed forms on their websites that are relevant to the lead generation and email outreach customer journey.

The following are some of the most common features in MA software. When comparing basic MA software, please make use of them as a reference sheet.

• Segmentation and batch emails
• Behavioral trigger emails
• Forms
• Mobile Optimization
• Dynamic Personalization
• Split Testing

Lead nurturing

Some of the top MA software solutions include advanced lead nurturing. It aids companies in tracking, segmenting, and communicating with leads to convert them into paying customers. Here are the fundamental and sophisticated lead nurturing capabilities available in MA programs.

• Lead Database
• Drip Campaigns
• Task and Alert Automation
• Segmentation
• Lead Scoring

Social media automation

Standalone social networking applications like Hootsuite and Buffer might be classified as MA tools, but they can’t compete with Aritic Pinpoint or Pardot by Salesforce when it comes to functionality. When it comes to social analytics tools, many MA software provides data on how many interactions you received on each post so that you may see which sorts of content are most effective. You can also set future postings, monitor what your readers post across social media, who shares your material and with whom they share it, and more. You have to consider it when you choose marketing automation software in today’s world.

With online surveys, sweepstakes, and referral programs built into the MA software, you may engage your audience. You can also use event-triggered features to prompt prospects to share information at just the right moment while they’re engaging with it.

• Posting and scheduling
• Social listening
• Social interactions through messaging

Analytics and reporting

The greatest advantage of automating your marketing process is having in-depth analytics, which most MA tools provide. The bulk of MA solutions appears to be business intelligence software with custom dashboards displaying the company’s key KPIs in easy language.

• Website analytics
• Multi-channel analytics
• Lead funnels
• Conversion rates & ROI

SEO, paid media, and digital advertising

Most of these tools are used to manage paid and organic campaigns from a centralized marketing platform, where most other marketing efforts reside. Managers at SMBs may discover that keeping track of these activities in free or standalone software is enough, but having all that data in one place for analytics might be beneficial.

• Personalized customer targeting
• Account-based marketing
• Organic and paid search
• Custom website landing pages and lead capture forms

9 tips: How to choose marketing automation software?

We will go through some of the factors that should be taken into account before deciding on this section. Consider these when you choose marketing automation software.

User Interface

Let’s start at the beginning. First, consider how easy or complicated the platform’s user interface is. Many, if not all, platforms will claim to have an easy-to-use and intuitive platform — but this isn’t always the case.

To assess whether the platform’s user interface is simple to understand, request a demo of the platform. Consider these questions while evaluating each system:

• Is there a clear structure to the content? Examine whether these simple activities are difficult to do by creating an email or checking analytics.
• Is there any relevant information? Contextual data may be provided below when you hover over the title or in other places on platforms that support this function.
• Do you have to go through many hoops to do a simple activity? If this is the case, it will harm your productivity in the long run.

When transferring to a new platform, there is typically a learning curve. As an e-commerce firm, you’ll have other concerns, such as filling online orders and launching additional initiatives. So, be sure to test the platform’s user interface to determine how quickly you’ll be able to get up and to run.

Feature set

According to a study, marketing automation might help you improve your sales output by 14.5%. When selecting a solution with the most features, you must be as picky as possible. Here are some of the characteristics that businesses should not overlook:

• Vast email marketing support.
• Contact management capabilities and/or integration with your company CRM.
• Social media automation.
• Customer journey articulation and workflow creation.
• Landing page generation.
• A single, customisable, and comprehensive dashboard.
• A/B testing.
• Analytics.

Customer Support

Customer support is a critical service for any software firm. You should seek the most competent assistance if something goes wrong or if you have a question. It would be best if you evaluated how effectively their customer care department responds to your inquiries or concerns. You have to consider it when you choose marketing automation software. Because sometimes, softwares can be very complicated.

Here are some sample questions to assist you in making your selection:

• How can they assist you (email, live chat, or phone)?
• Is the response or help they provide quick?
• What else does their site have to offer? Do they provide a support center or an online community where you may get answers to similar questions?

When it comes to customer service, it’s great to have bots that are ready to answer your questions 24 hours a day, seven days a week, but make sure you can also speak with a genuine person who can help you immediately address concerns.

Learning Resources

If you’re buying a new marketing automation software, having internet resources accessible can help you get the most out of it. Some systems include tutorials, how-to videos, or even webinars to assist you to learn how to use the technology.

Ask yourself the following questions when selecting a platform and exploring its educational tools:

• What can you expect to learn from these resources? Some may be quick video tutorials on using the platform, while others might have comprehensive learning modules that certify you.
• What are the most effective ways to use these tools? The majority of them are free and readily available online, but this isn’t always the case.
• Are any of these resources out-of-date? You want to make sure your material is up to date for the marketing automation platform you’re purchasing at the moment.

Reviews

Taking a quick look at user feedback will indicate how the program functions in various circumstances. You’ll want to know how other SaaS businesses utilize the software and whether or not it’s how you intend to use it as well.

Look for evaluations on other sites to better understand what people think of the platform. You may look up YouTube or G2 Marketing Software Comparison to learn more about what others have to say about a specific platform you’re interested in.

Here are some indicators to look for:

• What are people saying about the program?
• Are the evaluators B2B or B2C SaaS firms?
• How many people or businesses are using the platform?

Cost/Pricing

One of the most important things to think about is the cost of the platform you’re purchasing. Some providers offer discounts that appear to be great, but when it comes down to the first payment, you discover there were a lot of hidden charges that you weren’t made aware of. Additional features or customer service might be charged as said fees.

Some services charge per number of contacts or per amount of emails you send. Some businesses also tend to provide attractive pricing in the first few months and then raise prices for the remainder of the subscription, so double-check your terms and conditions before signing up. The cost of a package should be clear, and you shouldn’t be charged anything extra for any optional/add-on features.

Company size and scalability

According to the marketing automation service provider, different pricing models are available to companies of various sizes. If you don’t anticipate handling more than a hundred leads each month, there’s no need to buy a large subscription that allows you to handle a million leads every month. Even if your selected firm does not provide a subscription that is sized correctly for your company, you may always negotiate with them over a bespoke software version tailored to your needs.

Another thing to consider while purchasing cutting-edge marketing automations systems is scalability. Every business wants to develop (and not just grow), and your marketing software should be able to expand with you. The majority of service providers, for example, utilize the software as a service (SAAS) approach, which entails purchasing online access to the system and the number of users it processed. Modern security options, such as these sophisticated solutions, don’t need you to install any software or set up any new hardware to support more users and are thus great for scaling. Make a contract with your service provider that doesn’t limit your ability to expand indefinitely.

API access

Although most service providers allow for various third-party connections by default, it’s convenient if they provide the platform’s API to you. This will enable your developers to integrate with applications that the platform does not natively support.

CRM Integration

To make the most of your marketing automation tool, you’ll need access to your CRM. Make certain that your chosen solution has built-in integration with your CRM. This is especially crucial since connecting a Marketing Automation tool with a CRM (e.g., Act! CRM) can produce numerous tangible advantages.

3 best marketing automation software

Consider these top marketing automation solutions after determining what you want from a marketing automation solution. These recommendations will help you to choose marketing automation software.

Hubspot for small business (budget: $50 – $600/month)

Hubspot is one of the most effectively designed and marketed marketing automation solutions, it has the highest number of users among all marketing automation tools.

Pros

• Hubspot has a user-friendly interface – its product is visually appealing. The sophistication of marketing automation software has made it simple for small company owners with minimal or no programming skills.
• The free trial – Starting with the 7-day free trial, most marketing automation solutions are ineffective in reducing consumers’ up-front risk (high setup charges, no free trials, and lengthy contracts). While Hubspot does require a large investment of money and time, it is one of the only tools to provide a free trial.
• Educating their clients – Hubspot’s most appealing feature is how they invest in educating their customers. The blog at Hubspot is an excellent location for company owners to learn about internet marketing.
• All of your tools can be found in one location – Hubspot is convenient because it has everything in one spot. While you may get 99 percent of Hubspot’s functionality for free (or inexpensive) elsewhere, having it all in one place might be useful.

Cons

• Requires a one-year contract – Hubspot contracts are automatically billed yearly by default. Ultimately, marketing automation is something you’ll use for many years, so this isn’t much of an issue. However, it would be preferable to have a monthly month-to-month contract.
• There are no A/B tests in the basic or PRO package – If you want to conduct split comparisons, you’ll need to invest in their $2,400 a month plan. Given that A/B testing is one of the quickest and most efficient methods for increasing online campaign performance, it appears odd that they would not offer it at lower levels to assist small businesses in achieving better outcomes (and more revenue).
• Hubspot pricing creeps severe – While Hubspot has a $200/month plan, marketing automation and CRM integration are only available at the $800/month level, including 1,000 contacts. This cost rises as your needs and contacts expand (quite quickly).
• Hubspot’s tools are available for free or at a low cost elsewhere – Hubspot provides a well-designed set of tools, but you can find them for free or inexpensive elsewhere.
• Support is paid for – Implementing Hubspot isn’t difficult, but it will cost you if you require ongoing technical assistance.

Pardot for mid-range business (budget: $600 – $3,000/month)

Pardot is an excellent marketing automation platform for organizations interested in improving the efficacy and insight of their Salesforce CRM.

Pros

• The Lead Deck – Pardot’s Lead Deck delivers real-time activity information for prospects. You can get an update on your screen and send them an email in one click if a prospect visits your services page.
• Customizable – One of the most advantageous features of Pardot is that it allows you to customize it to your own needs.
• Excellent Salesforce integration – After being acquired by Salesforce, their connection with the Salesforce CRM is excellent.
• Customer support – Customers appreciate Pardot’s customer service and training. 

Cons

• There aren’t many integrations – While Pardot does have native integration with most of the major CRMs, only 32 third-party applications are supported, which is less than other solutions, such as Marketo.
• API access – Pardot’s API is only accessible to paying subscribers of Pardot’s $2,000/month subscription service.
• A/B testing  – Furthermore, if you want to utilize features like A/B testing, AdWords integration, or dynamic email content, you’ll need to spend at least $2,000 per month.

Marketo for enterprises (budget: $3,000+)

Marketo is a useful tool, which is frequently compared to Pardot. The main advantage of Marketo (versus Pardot) is the platform’s ease of use and design.

Pros

• Easy use – The simplicity of the Marketo platform has earned it a lot of accolades. While it doesn’t have the most attractive user interface, everything is clearly labeled and simple to pick up, making adoption faster than other solutions.
• Salesforce integration – Marketo’s integration with Salesforce is second to none. While syncing Marketo and Salesforce may take time, the two tools are extremely easy to use together.
• Easy to set up – It’s quite simple to set up and get started with compared to some of Marketo’s competitors.
• Customer support – The customer service of Marketo is outstanding, and their community of users is quite active.

Cons

• Poor landing page – Marketo didn’t begin as a marketing automation platform (it was originally a lead management tool). While most of the product has been updated, there are still a few remnants, such as their landing page builder, that are clunky and difficult to use.
• Reports & analytics – Marketo’s greatest disadvantage is its poor analytics and reporting capabilities. It might take a long time to generate reports that you should be able to do with the click of a button without additional extensions.
• Pricing – Marketo has three basic pricing levels: $895, $1,795, and $3,175 per month. Each tier unlocks new capabilities so that it may cost as much as $900 to $1,400 more each month for a few extra capabilities.

Now you know how to choose marketing automation software. If you find more suggestions, comment below.

It is important to learn the best business intelligence trends for 2022 because data went viral and became enormous. And just like that, we all gained access to the cloud. Spreadsheets have given way to actionable and informative data visualizations and interactive business dashboards. The democratization of self-service analytics has leveled the data product chain. Advanced analytics isn’t just for analysts anymore.

Business intelligence trends for the “new normal”

COVID-19 has made many companies reevaluate their current practices. Although the situation appears to be less serious and more long-term changes toward a “new normal” is on the way, day-to-day business is far from settled. Some firms are coping with last year’s order decline, while others are still adjusting to the ever-present supply chain disruptions or putting their operations in place for possible future problems.

Business intelligence trends show that organizations are still working to establish themselves for the long term and are concerned with the basis of their data usage. Instead, firms are tackling the fundamental causes of their problems (e.g., data quality) and the holistic creation of a data-driven culture.

What are the best business intelligence trends for 2022?

The exponential growth in data has already shown its value in the last few years. That is why organizations are anticipated to invest heavily in data-related solutions in the years ahead.

Businesses are no longer concerned if data visualizations improve analyses; they’re asking what the best approach is to tell each data narrative, especially utilizing cutting-edge BI dashboard technology. 2022 will be the year of data security and data discovery: clean and secure data combined with a simple but powerful presentation. It will also be a time when organizations collaborate on BI and AI initiatives. So let’s take a quick look to the best business intelligence trends.

Key takeaways

• Thanks to the growing popularity of work-from-home, many businesses have made room in their financial plans for cloud and SaaS usage.
• The most significant development is the growing need for data literacy, which many businesses now recognize.
• With NLP and automation on the rise, artificial intelligence is becoming more and more popular.
• Users can automate basic data science operations with self-service BI, but AI-driven collaboration allows data scientists to create low-code applications.
• The importance of information governance and confidentiality of personally identifiable information (PII) in 2022 will be crucial for establishing end-user trust.

Master data management/data quality

The reasons why data quality and data management are becoming increasingly important in the business sector are obvious: only correct and up-to-date data may lead to correct decisions. As a result, you must be able to trust that the information is accurate to make good judgments.

Master data management aims to combine and share data from multiple systems, such as customer, supplier, or product master data.

Data literacy

The ability to comprehend and utilize data as a collaborative tool that everyone in the organization can use is becoming increasingly important for corporate success. Data literacy will be one of the key data analytics topics to keep an eye on in 2022.

The term data literacy is used to describe a person’s ability to understand, read, write, and communicate data in a specific scenario. It implies comprehending the methods and approaches used to analyze the information and the tools and technologies employed.

One of the most significant internal barriers to success is poor data literacy. By 2025, data literacy will be a must-have and an essential component in creating corporate value. The advent of self-service BI tools for handling big datasets, continuous intelligence, artificial intelligence, and augmented analytics has brought data literacy to the forefront of a flourishing data-driven culture.

Data governance

In collaboration with data quality management and metadata, data governance will be one of the most important BI trends in 2022. Role-based access, authentication protocols, and auditing are all used to guarantee the quality of company assets. Users trust accurate, unique, up-to-date data when they know it is correct, which increases revenue and reputation.

A data governance strategy outlines the blueprints for managing corporate data assets, including process, operational infrastructure, and architecture. It establishes the solid foundation on which company-wide data management may take place. It enables businesses to take advantage of data management technologies, procedures, and personnel to provide complete, accurate, secure, and understandable information.

The worldwide data governance market, valued at $1.2 billion in 2016, is expected to reach $4.9 billion by 2026 with a CAGR of 22.6 percent over the next four years.

Data discovery/visualization

In recent years, data discovery’s importance has grown. BI experts have long acknowledged the importance of empowering business users.

Data discovery, in essence, is the process of gathering data from both internal and external sources and utilizing sophisticated analytics and visualizations to combine it all. Businesses may use this method to keep every relevant stakeholder informed about the data by allowing them to explore and change information intuitively while extracting practical insights. To do so, firms of all sizes use modern technologies such as business intelligence tools that provide:

• Data integration.
• Graphical representations.
• A user-friendly interface.
• The ability to work with enormous amounts of data efficiently and intuitively.

Visualization for data has become a top-notch method to present and interact with multiple charts on a single screen, whether it’s focused on selling charts or comprehensive interactive reports. The goal is to show that data discovery is a method for decision-makers to discover insights. By employing visualizations, teams can notice trends and significant outliers in minutes.

Self-service analytics

Many organizations have wanted for a long time to use self-service analytics, and it still has a high priority. Users demand data availability at all times, on any device, and anywhere.

Businesses are no longer focused on simply providing self-service options. They also want to make data access more accessible while assuring consistent and high-quality data and results.

Modern data warehouses

The old data warehouse architecture is too complicated to enable smooth progress and is frequently too costly. Furthermore, the implementation approach is out of date because it isn’t built and optimized for how you use analytics today.

More organizations recognize the new difficulties, potential, and possibilities that data warehouses provide. Data warehouse automation is an innovation within data warehouse technology. This saves time and automates ETL operation by removing manual processes.

Alerting

Notifications have long been a feature of analysis and BI, but the application has recently evolved dramatically. Notifications have always aimed to save time by ensuring that users pay attention.

The traditional method required a firm definition of relevance, and it has thus far failed to live up to expectations. Recent advancements in alerts have been made by moving from predetermined relevance to machine-made suggestions based on user behavior. It is mainly used with database marketing.

Analytics on real-time data

The necessity for real-time data has grown tremendously, and it will continue to do so in 2022 as one of the data analytics trends. We’ve seen that since the epidemic began that fast and precise information is essential in devising effective countermeasures. Some nations have utilized data to make the most outstanding judgments possible, while businesses have adhered to ensure their survival in these frightening times.

Real-time access to data is no longer restricted to businesses; the general public now has it. We may see press conferences brimming with the most up-to-date information, graphs, and statistics that have helped shape antiviral methods. But not only that, building ad hoc analysis has allowed enterprises to stay on top of changes and adjust to significant problems that this year has presented.

In both business and personal life, forecasting and alarms are becoming more important in generating appropriate company responses and strategies for future efforts with more variables to consider. Furthermore, using live dashboards will assist businesses in accessing up-to-date information about their operations and respond if any potential concerns arise immediately. High gear for data access is becoming the standard, which is why some businesses can continue to operate and others cannot.

In 2022, real-time data will be a significant factor in all types of company analytics, and we will see far more of it in practice.

Artificial Intelligence

The integration of Artificial Intelligence and machine learning is one of the most significant advances in BI. Dan Vesset, a specialist at IDC., has called it “an era of enhanced analytics,” declaring that the connection will lead to “the handing over of power and intelligence to businesses.” This bridge connects humans and machines.

Businesses are moving beyond static, passive reports of things that have already occurred to proactive analytics with dashboards that show what is going on at any given moment and provide alarms when something isn’t functioning properly. Solutions based on AI algorithms that employ the most sophisticated neural networks can detect anomalies with a high degree of accuracy since they learn from past trends and patterns. Any unusual occurrence will be swiftly recorded, and the system will notify the user in this way.

SaaS and cloud adoption

The COVID-19 epidemic put organizations and businesses in crisis mode as they tried to make sense of the situation. With on-premises technologies unable to keep up with the demands of a primarily remote workforce, several firms were compelled to evaluate their existing BI practices. More companies plan to migrate to cloud-based BI this year, whether on a private or public cloud or via SaaS software.

After the pandemic, many businesses have adjusted their budgets to make room for cloud infrastructure to facilitate remote and dispersed workforces. Gartner predicts that by 2023, 40% of all business workloads will be hosted in the cloud, up from 20% today. Businesses now see analytics as a critical mission-critical capability, and they are not shying away from adopting data technologies.

Data security

The EU’s GDPR (General Data Protection Regulation), the CCPA (California Consumer Privacy Act) in the United States, and the LGPD (General Personal Data Protection Law) in Brazil are just a few examples of privacy legislation that have been implemented. Data security is one of the most important business intelligence trends for 2022.

Furthermore, the European Court of Justice’s recent reversal of the legal framework known as Data Privacy Shield hasn’t made things easier for software firms. The Shield was a legal mechanism that allowed businesses to move data across borders between the EU and the USA. Still, with recent legal changes causing the termination of the procedure, companies based in the United States don’t have the authority to transfer any EU data subjects.

All of this considered, businesses have been compelled to invest in security to comply with the new laws while also protecting themselves from cybercrime. It is expected that worldwide spending on cybersecurity products will reach $1.75 trillion by 2025. This is not a surprise to the professionals. During 2020 and the beginning of COVID-19, businesses were compelled to transition from physical to digital, relying on internet services while leaving a vulnerability for cybercriminals to exploit. 

Millions of cyber attackers are scouring the internet for a vulnerability to exploit in today’s world. The worst is that they strike both large and small businesses alike. There’s always the potential for a successful assault if your company hasn’t implemented certain security measures. Data security is more vital than ever due to the increasing number of cybersecurity breaches.

Collaborative BI

Isn’t it true that two departments are better than one? As connected interaction with others in real-time to discuss and exchange insights becomes more critical than ever, collaborative BI is becoming a BI trend. With distributed teams becoming the norm, remote collaboration will continue to be necessary.

Teams can interact with others in interactive reports and collaborative dashboards. Users may tag clients or other individuals in comments and share data assets through links, email, and Slack. Teams may decide the following steps, which cuts downtime to insight.

Conclusion

Many businesses are eager to take control of their data and make use of it. They also demonstrate that firms care about high data quality and efficient data usage.

This implies that businesses want to go beyond collecting as much data as possible, and instead be able to use high-quality data to make better decisions. This is also evidenced by the movement towards modernizing data warehouses.

What are your thoughts on these 2022 business intelligence trends? Did we overlook any BI trends? In the comments area below, let us know what you think!

Data as a service (DaaS) is a data management approach that uses the cloud to offer storage, integration, processing, and analytics capabilities through a network connection. The DaaS architecture is based on a cloud-based system that supports Web services and service-oriented architecture (SOA). DaaS data is stored in the cloud, which all authorized business users can view from numerous devices.

What is Data as a Service (DaaS)?

Data as a Service (DaaS) is a data management approach that attempts to capitalize on data as a company asset for enhanced business agility. Since the 1990s, “as a service” models have become increasingly popular. Like other “as a service” approaches, DaaS gives a method to handle the vast amounts of data generated by businesses every day while also delivering that critical information to all sections of the organization for data-driven decision-making.

Why do we need Data as a Service?

DaaS is a popular choice for organizations with a large volume of data. Maintaining such data may be difficult and expensive, making data as a service an appealing option. The transition to SOA has made the platform’s significance in storing data irrelevant.

Data as a service enables but does not demand data cost and separation from software or platform cost and usage. There are hundreds of DaaS providers worldwide, each with its pricing model. The price may be volume-based (for example, a fixed cost per megabyte of data in the entire repository) or format-based.

While the SaaS business model has been around for more than a decade, DaaS is a concept that is just now gaining traction

While the SaaS business model has been around for more than a decade, DaaS is a concept that is just now gaining traction. That is partly because generic cloud computing services were not initially built with large data workloads; instead, they focused on application hosting and simple data storage. Before cloud computing, transferring big data sets across the internet was also challenging. When bandwidth was limited in the past, it wasn’t easy to process massive data sets over the network.

For a long time, businesses and private users have used software as a Service. It has become standard in computing over the last decade. However, as the amounts of data generated and utilized by enterprises continue to rise accelerated, data as a service becomes essential. This also implies that data ages more quickly, making it more challenging to gather and keep relevant data, making access to the most up-to-date information even more crucial than ever before.

All company’s two primary objectives in any sector are to grow profits and decrease expenditures. Data as a service model aids with both of these goals. On the one hand, organizing work around data increases efficiency and speeds up business procedures, resulting in lower costs while also improving the top line without requiring any new invention.

The DaaS approach allows organizations to identify bottlenecks and potential growth areas in the manufacturing cycle, such as implementing predictive analytics and optimizing logistics, resulting in actual, game-changing increases in revenue. DaaS is utilized for both company purposes and customer fulfillment. Furthermore, in both situations, DaaS organizes the process and speeds up the delivery of the outcome.

DaaS can also benefit the entire organization and its customers when utilized correctly

Managing numerous data sources across multiple systems is difficult. And the time it takes to tidy, enhance, and unify data manually detracts from more beneficial activities and prevents other teams from working with that data.

Bad data can cause segmentation and routing issues for marketing and sales teams. Operations teams will have to resolve numerous conflicts due to incorrect data. The world of Big Data is rife with opportunities. However, data governance and analytics professionals must confront substantial third-party data quality and coverage concerns, resulting in incorrect modeling and a broken master database. Bad, siloed, or missing data also harms the customer experience.

How does DaaS work?

The data-as-a-service platforms are end-to-end solutions that enable the integration of various data sources and tools such as self-service reporting, BI, microservices, and apps. Users can access data using standard SQL over ODBC, JDBC, or REST. External DaaS services can also be used to obtain information. Many businesses provide simple APIs for accessing data as a service.

Benefits of Data as a Service

The potential advantages of data as a service are enormous. DaaS can also benefit the entire organization and its customers when utilized correctly.

Accelerate innovation

Data as a service may be regarded as a gateway to expansion. Development is expedited when data is at the core of a firm. That’s because data-informed methods allow for more innovation without the danger. Ideas based on reliable data have a greater chance of getting accepted by other parts of the organization and eventually succeeding once implemented if accessible to all departments and teams that need them. With access to information that promotes new ideas and encourages growth, innovative ideas may take off more quickly.

Agility boost

Many organizations may find that data as a service provides an excellent platform for treating data as a critical business asset for more strategic decision-making and effective data management. A complete corporate view may integrate internal and external data sources, such as customers, partners, and the public. DaaS can also be used to provide fast access to data for purpose-built analytics with end-to-end APIs that serve exceptional business use cases. DaaS can assist with self-service data access, making it easier for businesses to give their users easy, self-service access to their data. This can cut down on the amount of time spent looking for information and spend more time analyzing and acting upon it.

Risk reduction

DaaS can assist decrease some of the personal views that influence decision-making, putting firms at risk. Businesses founded on conjecture frequently fail. Data empower businesses reliant on data as a service provider to make the appropriate decisions and succeed. With data as a service, organizations may use data virtualization and other technologies to access, combine, transform, and deliver data through reusable data services, optimizing query performance while maintaining data security and governance. Data as a service trend benefits from these changes. In this manner, it aids in the reduction of risks due to inconsistent or incorrect data views or poor data quality.

Data monetization

For most businesses, having enough data is no longer an issue. Managing and operationalizing the data presents the most significant challenge in today’s market. While many CEOs have invested heavily in data monetization efforts, few have effectively exploited the total value of their information. DaaS is an appealing technique to achieve it.

Data-centric culture

Today’s business leaders struggle to break down data silos and provide teams with the information they require. Data as a service model provides businesses access to a growing range of data sources, promoting a data-driven culture and making data use accessible across all departments. DaaS also aids businesses in managing today’s data tide and complexity via reusable datasets that a wide range of users may use. These configurable, reusable data assets can help companies build a business-wide picture. Data as a service can assist businesses in applying data to their operations by opening up access to critical data sources.

Cost reduction

Capitalizing on a company’s wide range of data sources, extracting insights, and delivering those insights to various areas of the firm to make better decisions can significantly cut down on time and money spent on incorrect judgments. Data as a service reduces the influence of your gut and encourages data-driven decisions. It also wastes less of your resources on pointless, ill-informed efforts. DaaS can also help businesses develop customized customer experiences by leveraging predictive analytics to understand consumer behaviors and patterns, better serve customers, and build loyalty.

Challenges of Data as a Service

Security, privacy, governance issues, and possible limitations are the most common concerns associated with DaaS. Because data must be moved into the cloud for DaaS to work, further issues arise over sensitive personal information and the security of critical corporate data.

When sensitive data is transmitted over a network, it is more vulnerable than if it were held on the company’s internal servers. This problem may be overcome by sharing encrypted data and using a reliable data source.

Common concerns associated with DaaS mostly revolve around
security, privacy and governance

There are, however, some security risks that businesses must consider when adopting DaaS solutions. Wider accessibility provided by having data in the cloud also implies additional security threats that may result in breaches. As a result, data as a service providers must employ stringent security measures to keep Data as a Service going strong in the business world.

Another problem emerges if a DaaS platform restricts the number of tools that can be used to analyze data. Providers may only provide the tools they host for data management, which may be insufficient compared to the required tools. As a result, it’s critical to pick the most adaptable service provider possible, removing this issue entirely.

Pillars of advanced DaaS solutions

A data as a service solution is a bundle of solutions and services that delivers an end-to-end data management platform. Some of the critical features of a DaaS service include data processing, management, storage, integration, and analytics.

Businesses may use the first-party and third-party data they purchase to develop predictive go-to-market processes and outcomes when working with a tried-and-true DaaS provider.

A DaaS platform comprises two interconnected layers: A data access layer that supplies data points woven together and a data management layer that provides maintenance and development services for those data.

Data access layer

The data access layer uses business-related intelligence, such as firmographics, parent-child hierarchy, technographic, intent, scoops, location, contacts, and advanced insights.

Data management layer

The data management layer makes sure that the correct data reaches the right person, platform, or system. It necessitates complex operations such as cleansing, multi-vendor enrichment, routing, data brick, APIs, webhooks, modeling, and scoring. A DaaS solution also includes data services for teams with specific demands, complex analysis, or larger-scale data delivery requirements.

How to use DaaS?

DaaS is popular among businesses for achieving go-to-market success. Having precise location data is critical for enterprises that rely on physical address information, such as shipping and freight carriers. Teams can use third-party data alongside their internal consumer records to cover even the most difficult addresses, such as warehouses, small company storefronts, branch offices, and satellite structures with DaaS.

It might be challenging to prioritize new consumer categories if a product caters to a niche market. Traditional firmographics, like employee size or annual revenue, may not always define the company’s most significant accounts. Teams may also use DaaS to link detailed company and contact information with internal customer data to find new industrial segments with potential customers.

Every revenue team wants to know more about its target audience to segment and prioritize accounts. Industry segmentation of target account lists is typical, but a default industry classification such as “technology” or “manufacturing” might be too broad on occasion. DaaS enables businesses to choose a few ideal accounts and plot their relevant terms or keywords onto a company semantics graph. This displays related corporations in new or adjacent industry categories that may well suit the offered goods.

DaaS’s advanced capabilities help convert unstructured business data
into structured intelligence

Real-time enrichment may automatically include any lead with necessary business data like area of operation or annual revenue, improving the analytics and optimizing lead conversion on your website traffic. Inbound lead processing is optimized when your website traffic data is automatically cleaned, enhanced, and linked to specific CRM fields. Every department and sales representative has access to the information they require, while marketing qualified leads (MQLs) become highly trusted by sales.

Many companies use industry classification codes to determine the level of risk presented by a new client during the underwriting process. But industry codes only tell you about a company, especially if they’re broad and lumped together.

DaaS’s advanced capabilities help convert unstructured business data into structured, useable signals and intelligence, such as a company’s industry sector or how far it has progressed in its technological stack. Advanced indicators like a company’s technology competence rating or previous finance history may provide compelling evidence of creditworthiness.

Why did COVID-19 drive an increase in DaaS?

In the first quarter of 2020, a prominent global software firm boasted triple growth in desktop-as-a-service (DaaS) projects. Gartner predicts that DaaS users will increase by over 150 percent between 2020 and 2023. Setting up cost-effective, secure remote working spaces for organizations that embrace the advantages of dispersed work will be one of the major drivers behind this growth.

The number of DaaS projects grew throughout the pandemic, but those created simply to save money are more likely to fail

DaaS has always been considered an IT cost-saving solution for businesses – a business case that failed 80% of the time. However, the epidemic created a compelling and straightforward need: Organizations had to keep working with employees at home and using various devices. DaaS provided a secure and scalable option.

The future of Data as a Service model

DaaS extends a broader shift by businesses to cloud-first ways of doing things. Given the prevalence of a cloud focus in many sectors and among large and small organizations, there’s cause to think that DaaS use will continue to increase alongside other cloud services.

Even among organizations that have not previously used the cloud significantly, DaaS may help increase interest in cloud-first architecture. Typically, only enterprises capable of profitably utilizing SaaS delivery models adopted the cloud on a large scale in earlier years of cloud computing’s existence. Now, the cloud is capable enough for data workloads and intensive applications.

Gartner suggests that DaaS is still almost a decade away from reaching its actual productivity peak

DaaS is one way for businesses to use the speed and dependability of the cloud, whether they are new to it or have extensive expertise. Compared to on-premises data solutions, data as a service offers several advantages, ranging from more straightforward setup and usage to cost savings and increased dependability. While DaaS has its own set of problems, they can be addressed and managed.

Organizations already use DaaS to speed and simplify extracting insights from data and enhance data integration and governance. As a result, these businesses may maintain a competitive advantage over their rivals by implementing more effective data governance and integrity.

All these tempting advantages aside, Gartner’s hype cycle suggests that DaaS is still almost a decade away from reaching its true productivity peak. Because DaaS can become the analytics/big data center of gravity, it is expected to be more revolutionary than most other data-related advancements.

Combining data from various sources into a single, coherent picture is known as data integration. The ingestion procedure starts the integration process, including cleaning, ETL mapping, and transformation. Analytics tools can’t function without data integration since it allows them to generate valuable business intelligence.

There is no one-size-fits-all solution when it comes to data integration. On the other hand, data integration technologies generally include a few standard features, such as a network of data sources, a master server, and clients accessing data from the master server.

What is data integration?

Data integration, in general, is the process of bringing data from diverse sources together to provide a consistent overview to consumers. Data integration makes data more readily available and easier to consume and analyze by systems and users. Without changing current applications or data structures, data integration may save money, free up resources, improve data quality, and foster innovation. And while IT organizations have always had to integrate, the potential benefit has never been as significant as it is now.

Mature data integration capabilities benefit competitors who do not have them. The following are some of the advantages enjoyed by businesses with significant data integration skills:

• Reducing the time it takes to inter-convert and integrates data sets will improve operational efficiency.
• Analytics is a powerful tool for improving data quality. Through automated data transformations that apply business rules to data, you can improve the accuracy of your data and enhance your decision-making capabilities.
• Using a whole picture of data that businesses can more readily analyze, you may obtain more critical insights.

A digital firm is based on data and algorithms that analyze it to extract the most value from its information assets—from across the business ecosystem, at any moment. Data and associated services flow freely, securely, and unobstructed across the IT landscape in a digital firm. Data integration provides a complete overview of all the data moving through an organization, ensuring that it is ready for examination.

Data integration types

There are a variety of data integration techniques:

Data warehousing

Data warehousing is a data integration approach that uses a data warehouse to cleanse, format, and store data. Data warehousing is one of many integration systems that allows analysts to view statistics from several heterogeneous sources to provide insights into an organization.

Middleware data integratıon

Middleware data integration aims to use middleware software as a gateway, moving data between source systems and the central data repository. Before sending information to the repository, the middleware may help format and check it for errors.

Data consolidation

Data integration is bringing data from many systems into a single data source. Data consolidation is often aided by ETL software.

Application-based integration

An Application-based integration is one in which data is extracted and integrated using the software. The application validates the data during integration to ensure that it is compatible with other source systems and with the target system.

Data virtualization

Users may get a near real-time, consolidated view of data via a single interface even though the data is kept in separate source systems when using a virtualization approach.

Comparison: Data integration vs application integration vs ETL

The terms data integration, application integration, and ETL/ELT are often used interchangeably. While they are linked, there are several differences between the three phrases.

Data integration merges data from many sources into a centralized location, which is frequently a data warehouse. The ultimate destination must be adaptable to handle various data types at potentially huge quantities. Data integration is ideal for performing analytical activities.

The term “application integration” refers to moving information between applications for them to remain up to date. Each application has its method of emitting and receiving data delivered in significantly smaller quantities. Integration is perfect for operational use cases that need to be maintained. For example, making sure that a customer support system contains the same customer data as an accounting system is one way it can be done.

The term ETL is the acronym for extract, transform, and load. This refers to extracting data from a source system, changing it into a different form or structure, and loading it into a destination. There are two types of ETL: data integration and application integration.

Importance of data integration

With the ever-increasing volume of data, data integrity has become more vital. Data integrity is all about assuring that your data is recorded and kept as intended. And that when you look for information, you receive what you want and anticipate.

Businesses must be able to trust the data that goes into analytics tools to trust the outcomes. You get reliable results if you feed good data.

Maintaining a single location to view all of your data, such as a cloud data warehouse, can aid in data integrity. Data integration projects help to improve the quality and validity of your data over time. Data transformation methods can spot problems with data quality while it is being moved into the primary repository and correct it.

Data integration use cases

Many types of areas can benefit from data integration.

Multicloud data integration

Connecting the correct data to the appropriate people is a simple way to enhance security and speed innovation. Connect diverse data sources promptly so that businesses may combine them into beneficial data sets.

Customer data integration

To improve customer relationship management (CRM), you need data from distributed databases and networks.

Healthcare data integration

To make rapid data available for patient treatment, cohort treatment, and population health analytics, combine clinical, genomic, radiology, and image data.

Big data integration

Businesses use sophisticated data warehouses to deliver a unified picture of big data from various sources to make things easier.

How does data integration work?

One of the most challenging tasks organizations face is getting and understanding data about their environment. Every day, businesses collect more data from a broader range of sources. Employees, users, and clients need a mechanism for capturing value from the data. This entails organizations being able to assemble relevant data from wherever it is found to help with reporting and business processes.

However, essential data is frequently split across applications, databases, and other data sources hosted on-premises, in the cloud, on Internet of Things devices, or delivered via third parties. Traditional master and transactional data and new sorts of structured and unstructured data are no longer kept in a single database; instead, they’re maintained across multiple sources. An organization may have data in a flat file or request information from a web service.

The physical data integration approach is the conventional method of data integration. And it entails moving data from its source system to a staging area, where cleansing, mapping, and transformation take place before the information is transferred to a target system, such as a data warehouse or a data mart. The second choice is data virtualization, a software-based data integration type. This approach uses a virtualization layer to connect to real-world data stores. Unlike physical data integration, data virtualization does not require the movement of any actual data.

Extract, Transform, and Load (ETL) is a widely used data integration method. Information is physically taken from several source systems, transformed into a new form, and stored in a single data repository.

Data integration example

Let’s assume that a firm called See Food, Inc. (SFI) makes a mobile app in which users can photograph different items and determine whether or not they are hot dogs. SFI uses numerous tools to conduct its operations:

• To acquire new consumers, you’ll want to use Facebook Ads and Google Ads in tandem.
• Using Google Analytics to keep track of events on its website and mobile app.
• To store user data and image metadata (e.g., hot dog or not hot dog), we’ll use a MySQ l database.
• Send marketing emails and nurture leads via Marketo.
• Zendesk to handle customer service.
• Netsuite for accounting and financial management

Each of those applications contains a silo of information about SFI’s operations. That data must be combined in one location for SFI to acquire a 360-degree view of the business. Data integration is how it’s done.

How to choose data integration tools?

Compared to custom coding, an integration platform may save time to value integration logic by up to 75%. For organizations that wish to use an integration platform within their approach, the first step is to consider three essential factors: 

Company size 

SMBs have different needs than large businesses. According to industry experts, small and medium-sized businesses typically prefer cloud-based integration solutions for applications. Most recent application server architectures have moved away from on-premises servers and toward enterprise integration or hybrid integrations.

Source data and target systems 

Do you have access to the data, or are you currently using any specialized software? What data do you currently possess, and how is it structured? Is it primarily structured or a mix of structured and unstructured information?

Consider which sources you want to incorporate. Integrating your transaction and purchasing data with your CRM data is a more straightforward endeavor. Alternatively, integrating your entire multi-channel marketing stack may be more difficult, as it might include connecting all of your customer touchpoints into a single view of them.

Required tasks 

A strategy to achieve your goals is critical in any integration project.

Businesses can use integration projects for various activities, including data integration, application integrations, cloud computing, real-time operation, virtualization, cleaning, profiling, and so on. Some jobs are more specialized than others; understanding what you need and what you don’t will assist you in keeping your costs low.

Types of data integration tools

Here are the various types of data integration solutions:

On-premise data integration tools

These are the tools you’ll need to combine data from various local or on-premises sources. They’re coupled with unique native connectors for batch loading from diverse data sources housed in a private cloud or local network.

Cloud-based data integration tools

iPaaS, or integration platforms as a service, is the term given to services that aid in integrating data from diverse sources and then placing it into a cloud-based Data Warehouse.

Open-source data integration tools

These are the most pleasing alternatives if you’re attempting to avoid proprietary and possibly costly enterprise software development solutions. It’s also ideal if you want complete control of your data within your organization.

Proprietary data integration tools

The majority of these software systems are intended to be more expensive than open-source alternatives. They’re also frequently built to cater to particular business use cases.

3 best data integration tools

Now that you’ve learned about the criteria and types to consider when selecting data integration solutions. Let’s take a closer look at the top data integration tools.

Dataddo

Dataddo‘s goal is to make it easier for businesses of all sizes to get valuable insights from their data. Data integration, ETL, and data governance are just a few processes simplified by our solution. A no-code, cloud-based ETL platform that prioritizes flexibility – with a wide range of connections and fully customizable metrics, Dataddo makes building automated data pipelines simple. 

The platform seamlessly links with your existing data stack, saving you money on needless software. With a user-friendly interface and straightforward setup, Dataddo allows you to focus on putting your data together rather than wasting time learning new activities. It’s 100% managed by API updates so that you can create and forget your pipelines. If Dataddo does not already have a connection available, it may be included in the platform within ten days of submitting an inquiry.

Key features: 

• Easy, quick deployment.
• Flexible and scalable.
• Connectors that have been installed in less than ten days.
• Security: GDPR, SOC2, and ISO 27001 compliant
• Connects to existing data infrastructure

Informatica PowerCenter

The Informatica Powercenter software is a cloud-native integration service that incorporates artificial intelligence. Its simple user interface lets users take decisive transformative action, allowing them to pick between the ETL and ELT approaches. PowerCenter’s multi-cloud capabilities are focused on giving customers complete control over their data, with several pathways depending on client needs, such as Data Warehouse modernization, high-level data security, and advanced business data analytics.

Key features: 

• A metadata-driven AI engine, CLAIRE, is at the heart of this content creation system.
• High-level data security for any business.
• Interoperable with a wide range of third-party platforms and apps and other software.
• Designed to assist businesses in gaining new insights from their data.

Pandoply

Through a combination of pre-built SQL schema and rapid compatibility with any and all business intelligence platforms, Pandoply fulfills its promise of “analysis-ready data” by providing a series of pre-built SQL schema. It gives complete control over how a source is built, allowing the user to participate in the table creation process when creating a data source. Built-in performance monitoring and simple scaling for growing enterprises are additional advantages.

Key features: 

• Users and data queries are unrestricted.
• The number of data sources accessible is over 100.
• Artificial Intelligence-driven automation in a Smart Data Warehouse.
• Data schema modeling is easier.

The best database marketing examples will show the way to a successful strategy. Customer database marketing gathers client information such as names, contact information, purchase history, and so on to create tailored marketing techniques for attracting, engaging, and converting potential consumers.

Customer data is the lifeblood of marketing, and all digital marketers must be familiar with it, no matter what industry they work in. Customer data allows you to personalize your advertising, target it more precisely, and make it more relevant and successful.

Customer research is the process of gathering, organizing, interpreting, and analyzing new and existing customer data to understand more about them and promote the product or service in a more personalized and result-oriented manner. It’s a type of direct marketing that heavily relies on databases and the effective use of statistical techniques.

What is database marketing?

Database marketing is the process of gathering, analyzing, and interpreting customer data to provide more personalized customer experiences. Data from various sources, including consumer email correspondence, CRM system records, data warehouses, and, increasingly, external sources like social media, is collected by database marketing.

Businesses can use database marketing for client interactions as well as sales and business development. With so much consumer data available to businesses, database marketing is becoming an increasingly important component of the overall marketing plan.

Database marketing types

Consumer database marketing and commercial database marketing are two types of database marketing.

Customer Database Marketing

B2C marketers are looking for a creative method to build awareness in their target customers. They use competitions, freebies, discount coupons, and so on to gain such data. After the database is complete, the marketer may create tailored deals and send them directly to individuals’ inboxes or feeds via email, social media, or other channels.

Once the database is segmented, it’s simple to personalize things. People have marketed items based on their hobbies, which means customization is simple.

Business Database Marketing

Business database marketing aims to meet the demands of firms that do business with other enterprises. The data gathering procedure entails collecting data from various sources, such as industry reports, event registrations, demonstrations, etc.

The consumer marketing database is significantly more extensive than the B2B database. This is because B2B marketers are more concerned with significant target accounts.

Best database marketing practices

Businesses may follow several best practices to increase their chances of succeeding in database marketing.

Multi-channel marketing

Customers now access the Internet from multiple devices. For example, they may start their research on their laptop and continue it on the train en route to work the next day with their smartphone. Finally, they might check in again on their iPad later that evening. You should aim to provide a consistent customer experience across all devices for your database marketing campaign to succeed. Users should be able to change devices without any problems.

Analyze all data streams and sources

The most outstanding customer experience solutions, on the other hand, evaluate data from your entire technology architecture and all of your data streams. It’s critical to collect all this information, not just part of it. To fully understand your customer, utilize tools that integrate data from various sources, such as CRM systems, data lakes, internet activity, and point-of-sale systems.

Predictive analytics

With so much data at their disposal, database marketers who use the proper technology can benefit from predictive analytics, which sends alerts and notifications to consumers when they’re most likely to quit service. These findings may assist your firm in preventing problems before they develop and foster long-term customer loyalty.

We will see how you can use these in database marketing examples.

How to build a marketing database?

Let’s look at how to build a database marketing plan in more detail.

Create thought leadership articles

You should create such content around industry concerns and then utilize it in the form of content that a reader can only access by providing contact information.

Offer free trials

Businesses may use free trials to pressure their customers to give them some vital information, at which point you may target those users who selected free trials with advertisements.

Design a free tool

Using this approach, you may create a customer database for marketing. This way, you’ll be able to learn about audiences who are interested in your product or service.

Collect customer information

Marketers should include mechanisms for gathering consumer information at the time of checkout to build a marketing database.

Acquire a business contact database

Businesses could engage with a data provider to acquire the prospects’ business contact database.

Collect website visitor data

Marketers may use cookies to collect data from website visitors.

Create a Chatbot on your business page

Using a conventional chatbot or a Facebook chatbot will help you gather consumer information, but it will also assist you in expanding your subscriber lists.

Now you have the structure. Let’s compare it with database marketing examples.

Real-life database marketing examples and strategies

What are the best database marketing examples and strategies?

Customer Lifecycle Marketing (ft. Starbucks) 

Lifecycle marketing is a type of marketing that focuses on the customer’s entire experience. Each company determines its definition of the journey. There are, however, three significant phases a client goes through.

The first phase is customer acquisition when a visitor becomes a client. Customer retention follows, in which a consumer stays loyal to your business and makes repeat purchases due to their interaction with it.

Finally, there is a customer development phase where consumers branch out into additional product categories or refer other people to your business.

Omnichannel database marketing example strategy (ft. Amazon)

Omnichannel marketing is an approach to integrating all of your marketing channels into one cohesive customer journey. It is based on a database that can generate a single, 360-degree customer profile.

After this, brands may access consumer interactions on other platforms and improve customer experiences by referencing previous purchases.

Increase retargeting campaign potential with outreach campaigns (ft. Sephora)

It’s also essential to conduct routine follow-ups to re-engage your customers and collect the data necessary for triggered retention strategies.

Here’s an outstanding database marketing case from Sephora. Their loyalty program targets Sephora Insiders who haven’t purchased in the last 30 days, “Sephora Insiders.”

“Don’t forget, you get $15 off of every $75 you spend,” the ad says. The offer is clear and potent: “Remember, you’ll get a gift if you spend over $75.” Sephora can update the customer profile with the items purchased and utilize replenishment campaigns to use the following database marketing. It is an excellent database marketing example.

Dynamically create cross-sale offers based on previous search history (ft. Etsy)

Category pages are yet another excellent use of database marketing.

Many businesses neglect the category pages on their site, focusing instead on landing pages and product page design. This is a fantastic example from Etsy.

Etsy monitors customers’ previous searches. They use this information to include a variety of personalized widgets offering cross-sales and searches. This saves time during the product discovery process, but it also helps Etsy make additional money from the same shopping session by reducing searching friction.

Using demographic data to time and personalize lifecycle campaigns (ft. Target)

The example shows how businesses may anticipate consumer demands.

With baby items and new families, Target can accomplish just that. They can develop entire campaigns to serve their clients better and win a larger share of wallets by using a thorough knowledge of client preferences throughout several key development phases.

Database marketing challenges

Although the advantages of database marketing are evident, many people are unaware of how to take advantage of them. As a result, there are several difficulties, including:

The inevitable data degradation

Data loss, data rot, or simply data decay is the deterioration of data in a database over time. It changes their profile whenever a client moves to a new address, modifies their email address, gets a deal, etc. Even due to technical failures, data loss might occur.

Even if you had nothing to do with it on a mechanical level, data decay is likely to occur every day. To combat mechanical data deterioration, you’ll need a solid backup. According to statistics, around a third of your data can become invalid in a year and 2-3% in a month. That means there’s a lot of highly unreliable information and the expenditure of many pursuing solutions founded on that data.

Because data is time-sensitive, maintaining a current database is not an option; it must. One approach to address this problem is concentrating the campaign on the likely knowledge to remain static or predictable. Demographic information and contact information are examples of this.

Inaccurate data collection

It’s best to remember that consumers are unlikely to disclose information to businesses they deal with it. This implies that customers might give incorrect data about themselves. It does not have to be deliberate in all cases. Inaccurate data gathering can also result from poor writing, typing errors, missing information, and so on.

Checkboxes and drop-down menus are two other techniques to minimize the problem. According to studies, unclean data may cost organizations up to 15% of their revenue. Limiting data hoarding and prioritizing being data-driven will aid in the creation of a correct database.

Inability to capitalize the data efficiently

Step one is to gather as much accurate information as possible, which must be done regularly. A business’s primary goal is to collect data for a reason. If it cannot use the data it gathers, then the entire endeavor will be meaningless.

The firm must take full advantage of the data as quickly as possible to increase brand interaction. This is a strategy to delay the impact of logical data decay.

Database collection and maintenance is an expensive endeavor

In this case, the term expensive is used in both a subjective and an objective way. Yes, collecting and preserving a large number of data is undeniably costly.

The cost of database management is unjustifiable in the subjective view if it doesn’t provide any value. It simply implies that your data from your database isn’t worth anything because of how much work goes into it.

Conclusion

The main objective of a database marketing campaign is to develop pertinent or customized content to produce quality leads and improve conversions.

Such efforts’ success is determined by how target audiences respond to the campaign. Businesses should verify whether their database marketing has achieved the intended number of conversions.

Thousands of advertisers jostling for consumers’ attention daily necessitates a database marketing plan. The businesses that put their customers’ needs, experiences, and preferences first will always win!

What do data governance practices help for? Or we should ask first, do you know where to seek out particular data in your company, or who to contact for it?

Businesses that are still in their early phases understand the importance of data-driven choices in boosting their financial performance. A strong data governance plan may help you save time and money by raising the quality and ease with which teams access data. Following recommended data governance standards can guarantee that you benefit from a policy strategy, but first, what is data governance?

A data governance strategy focuses on establishing who has control and power over data assets within an organization. It includes people, procedures, and technology to handle and protect data assets. We explained data governance definition in detail in a previous article.

Organizations of different types and industries require varying degrees of data governance. It’s especially crucial for firms that adhere to regulatory standards, such as finance and insurance. Organizations must have formal data management procedures to control their data throughout its lifecycle to comply with regulations.

Another aspect of data governance is protecting the company and sensitive consumer data, which should be a top priority for businesses nowadays. Data breaches are becoming increasingly common, with governments passing legislation – as evidenced by HIPAA, GDPR, CCPA, and other privacy laws. A data governance strategy creates management to safeguard data and help organizations comply with regulatory requirements.

Despite the fact that data governance is a major area of concern for many businesses, not all methods deliver the intended benefits. Because of it, you need the best data governance practices for your businesses.

What does it mean to govern data?

Data Governance is the term used to describe a company’s data management, usage, and protection activities. Governing data refers to either all or a part of a firm’s digital and hard copy assets in this context. Indeed, defining what data means to an organization is one of the best practices for data governance.

Consider data governance to be the who, what, when, where, and why of your company’s data.

Why is data governance important?

The value of data is becoming increasingly crucial for businesses. Everywhere you look, digital transformation is a hot topic. You must be able to control your data to profit from your data assets and achieve a successful digital transformation. This implies choosing a data governance framework customized to your organization and future business goals and models. The framework must establish the required data standards for this journey and delegate roles and responsibilities inside your company and within the business ecosystem where it is based.

A well-designed data governance framework will support the business transformation toward operating on a digital platform at many levels within an organization. You should add these components to your data governance practices.

• Management: This will guarantee top management’s commitment to corporate data assets, their value, and their potential impact on the company’s evolving business operations and market opportunities.
• Finance: This will protect accurate and consistent reporting for finance.
• Sales: This will allow accurate knowledge of consumer preferences and behavior for sales and marketing.
• Procurement: Because of the use of data and business ecosystem collaboration, this will help to increase cost reduction and operational efficiency initiatives based on tapping into data and integrating with the business ecosystem.
• Production: This will be necessary for production use in putting automation into action.
• Legal: This will be the only option for legal and compliance as new regulation standards emerge.

Data inconsistencies in different systems across an organization may go unresolved because of ineffective data governance. For instance, customer names, for example, might be presented differently in sales, logistics, and customer service systems. Integrating data from various sources and formats into single reports and dashboards may be complex. These changes could create data integrity issues that harm the effectiveness of business intelligence (BI), enterprise reporting, and analytics tools. Furthermore, incorrect data might go unnoticed and unaddressed, which will impact BI and analytics accuracy.

Data governance framework

Data management is the process of organizing, understanding, and leveraging data to meet organizational goals. A data governance framework can help ensure that your organization follows best practices for collecting, managing, securing, and storing data.

To assist you to figure out what a framework should include, DAMA imagines data management as a wheel with data governance as the center from which ten specific data management skills radiate:

• Data architecture: Overall, the data structure and data-related resources are essential components of the company architecture.
• Data modeling and design: Data governance is for analysis, design, building, testing, and maintenance.
• Data storage and operations: Storing structured physical data assets, including deployment and maintenance.
• Data security: Data governance ensures privacy, confidentiality, and appropriate access.
• Data integration and interoperability: Data governance is for acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization, and operational support.
• Documents and content: Data governance is the practice of managing, archiving, indexing, and providing access to data from non-structured sources.
• Reference and master data: Standardization of data definition and usage and shared data reduction to improve data quality and reduce redundancy.
• Data warehousing and business intelligence (BI): Data management analyzes data and gives access to decision support data for reporting and analysis.
• Metadata: Metadata is a term that refers to any information associated with a digital item, such as title and author. It collects, classifies, keeps, integrates, controls, manages, and delivers metadata.
• Data quality: Defining, tracking, and ensuring data integrity and quality are essential aspects of data quality.

When developing data governance practices, businesses should consider each preceding aspect: collecting, managing, archiving, and utilizing data.

The Business Application Research Center (BARC) cautions that it is not a “grand slam.” Data governance can erode participants’ trust and interest over time as a very complicated, continuous effort. BARC advises starting with a minor or application-specific prototype project and gradually expanding throughout the firm based on learnings.

BARC developed the following procedure to aid in the implementation of a successful program:

• Define objectives and analyze the advantages.
• Examine the existing condition and delta changes.
• Create a route map by combining the product plan and feature roadmaps.
• Convince stakeholders and obtain funding for the project.
• Develop and implement a data governance program.
• Implement the data governance program.
• Monitor and control.

What are data governance best practices?

We gathered the best data governance practices for your organization. A data governance strategy is only as effective as the company that uses it. You should follow rigorous data governance procedures to get the most out of a data governance plan. We all know the most effective methods in creating a data governance policy.

Check out our top six data governance practices to get you started collecting, storing, and utilizing your data more successfully.

Begin small and work your way up to the big picture

People, procedures, and technology are all critical aspects of data management. Keep all three elements in mind when developing and executing your data plan. However, you don’t have to improve all three areas simultaneously.

Start with the essential components and work your way up to the final image. Begin with people, progress to the procedure, and conclude with technology. Before any component may proceed, it must build on top of the preceding ones for the whole data governance plan to be well-rounded.

The process won’t work without the correct individuals. If the people and procedures in your company aren’t managing your data as you intended, no cutting-edge technology can suddenly repair it.

Before developing a process, search for and hire the proper people. Use these data specialists to help you establish a data governance strategy. After that, you may use whatever technology best automates your processes and gets the work done correctly and swiftly.

Get business stakeholders on board

You need top-level executive buy-in to develop a data governance strategy, but getting the go-ahead is only the beginning. You also want to Engage your audience and encourage them to take action so that your data governance plan is implemented throughout your business.

The ideal approach to get executives interested in your data governance strategy is to make a business case for it. You demonstrate leadership by creating a business case, demonstrating the specific advantages they might anticipate from a data governance approach.

Define data governance team roles

When roles, responsibilities, and ownership structures are well-defined, data governance methods are more likely to be effective. The foundation for any data governance strategy is the creation of team members’ data governance functions across your company.

Data governance practices aim to improve data quality and collaboration across departments. It necessitates input and data ownership from all levels of the company. While each organization’s data governance framework will appear unique, there are undoubtedly vital players that should be included in your structure:

• Data governance council or board: The data governance team is responsible for the overall governance plan. They provide strategic input as part of the data governance strategy. This team also frequently prioritizes elements of the plan and approves new policies.
• Tactical team members: The tactical data governance team members create data governance policies and approaches based on the council’s recommendations. They develop the data processes and rules, which are later approved by the data governance council.
• Owners: The people in charge of particular data are known as data owners. This is the person to reach out to when someone requests information. For example, if you need sales data from last month, you would contact the sales data owner.
• Data users: The team members frequently input and utilize data as part of their regular job duties.

To measure progress, use metrics

It is critical to track progress and display the effectiveness of your data governance strategy, just as it would be with any other shift. Once you’ve acquired executive buy-in for your business case, you’ll need evidence to support each stage of your transition. Prepare ahead of time to establish metrics before implementing data policies so that you can build a baseline based on your current data management strategies.

Using the original metrics regularly allows you to track your development. This demonstrates how far you’ve come, but it also serves as a checkpoint to make sure your data governance best practices are working in practice rather than just on paper. A plan that works perfectly, in theory, may fail to work in reality. It’s critical to keep an eye on your data governance strategy and remain open to changes and improvements.

Encourage open and frequent communication

Whether you’re just getting started with a data governance initiative or have been using one for some time, staying in touch early and often is critical, communicating regularly and effectively allows you to illustrate the strategy’s impact—from highlighting triumphs to re-organizing after a failure.

The Chief Data Officer (CDO) or equivalent role should be given to an executive team member, such as the CIO or CDO, to take on the leadership of the data governance program. These executives are in charge of keeping track of the organization’s governance standards across teams and departments. Team leaders and data owners may provide regular progress updates to the senior management. The executive team member then delivers essential information to the rest of the leadership team and the entire organization.

Data governance is not a project; see it as a method

Creating a data governance plan can feel like starting a new initiative. You might be inclined to form a group to work on the project while the rest of the organization waits for you to finish it. This is when many organizations’ data governance plans come to a halt.

It is not enough to implement a data governance strategy once and then declare it finished. There is no defined ending date or conclusion. Instead, it’s a continuing practice added as part of your organization’s standard policy. Data governance becomes an aspect of everyday life at your company in the same way dress codes or leaves policies are.

Did you know that common data quality difficulties affect 91% of businesses? Incorrect data, out-of-date contacts, incomplete records, and duplicates are the most prevalent. It’s impossible to identify new clients, better understand existing client needs, or increase the lifetime value of each customer today and in the future if there isn’t clean and accurate data.

As data has become a critical component of every company’s activity, the quality of the data collected, stored, and consumed during business operations will significantly impact the company’s current and future success.

What is data quality?

Data quality is an essential component of data governance, ensuring that your organization’s data is suitable for its intended purpose. It refers to the entire usefulness of a dataset and ease of processing and analysis for other purposes. Its dimensions, such as completeness, conformity, consistency, accuracy, and integrity, ensure that your data governance, analytics, and AI/ML projects deliver consistently reliable results.

To evaluate it, one must consider data as the cornerstone of a hierarchy built on it. Information is placed in context over the foundation of data, and information comes next. Inferior quality data will produce inferior information quality, which will raise the hierarchy, leading to poor business judgments.

According to a study, the most common reason for incorrect quality is human error. Working on improving low-quality data is time-consuming and requires a lot of effort. Other factors contributing to bad quality include a lack of communication between departments and faulty data management techniques. Proactive leadership is required to address these issues.

Poor quality has a significant impact on your company at all levels:

• Higher processing cost: It takes ten times as long to complete a unit of work when the data is wrong than accurate.
• Unreliable analysis: Lower confidence levels in reporting and analysis make bottom-line management a difficult task.
• Poor governance and compliance risk: Compliance is no longer optional, and business survival becomes more difficult without them.
• Loss of brand value: When businesses make frequent mistakes and judgments, their brand value rapidly decreases.

How is data quality measured?

It’s easy to spot data quality, and it may be evident. It’s difficult to assess precisely because the data quality is ambiguous. To obtain the appropriate context and measurement technique for data quality, you may utilize numerous variables.

Customer information must be complete, precise, and accessible during a marketing campaign. Customer data must be unique, accurate, and consistent across all engagement channels for a marketing campaign. Data quality dimensions are concerned with characteristics that are particular to your situation.

Data quality dimensions

Dimensions of quality are elements of measurement that you may each evaluate, interpret, and improve. The aggregate scores of many dimensions represent data quality in your specific situation and indicate whether the data is fit for use.

There are six fundamental dimensions of data quality. These are the standards that analysts use to assess a data’s viability and usefulness to those who will use it.

Accuracy

Businesses should reflect real-world situations and occurrences in the data. Analysts should rely on verifiable sources to validate the measure of accuracy, which is influenced by how close the values match with verified accurate information sources.

Completeness

The data’s completeness assesses whether it can successfully deliver all required values.

Consistency

The uniformity of data as it travels across applications and networks and comes from many sources is data consistency. Consistency implies that identical datasets should be present in distinct locations and not clash. Keep in mind that consistent data may be incorrect.

Timeliness

Data that is timely is information that is readily available when needed. This aspect also entails keeping data up to date, which entails real-time updates to ensure that it is always accessible and current.

Uniqueness

Each entity, event, or piece of information in a dataset must be unique from all others. No duplicate records exist in the data set. Businesses may use data cleansing and deduplication to assist with a low uniqueness rating.

Validity

Businesses should gather data following the organization’s established business rules and parameters. All data values should also be within the correct range, and all dataset values should correspond to acceptable formats.

Data quality issues

Poor quality has a wide range of obligations and potential consequences, both minor and severe. Data quality problems waste time, lower productivity, and raises expenses. They may also harm consumer satisfaction, damage corporate reputation, necessitate costly fines for regulatory non-compliance, or even put customers or the public in danger.

How to improve the data quality?

Improving data quality is about finding the right balance of qualified people, analytical processes, and accurate technology for your company. Along with proactive top-level management, all of this can significantly enhance data quality.

Let’s start basic and follow the four-step program:

Discover

To be able to plan your data quality journey, you must first determine where you are today. To do so, you’ll need to look at the status of your data right now: what you have, where it’s kept, its sensitivity level, data connections, and any quality concerns it has.

Define rules

The data quality measures you choose and the rules you’ll establish to get there are determined by what you learn throughout the discovery phase. For example, you may need to cleanse and deduplicate data, standardize its form, or delete data before a specific date. This is a collaborative effort between IT and business.

Apply rules

After you’ve established regulations, you’ll connect them to your data pipelines. Don’t get trapped in a silo; Businesses must integrate their data quality tools across all data sources and targets to remediate data quality throughout the company.

Monitor and manage

Data quality is a long-term commitment. To keep it, you must be able to track and report on all data quality processes both in-house and in the cloud using dashboards, scorecards, and visualizations.

Following are the disciplines that can help you prevent data quality concerns and eventual data cleansing:

• Data governance
• Data profiling
• Data matching
• Data quality reporting
• Master Data Management (MDM)
• Customer Data Integration (CDI)
• Product Information Management (PIM)
• Digital Asset Management (DAM)

You should use some tools for the best results.

What are data quality tools?

Data quality tools clean data by correcting formatting mistakes, typos, and redundancies while also following processes. These data quality solutions may eliminate anomalies that increase company costs and irritate consumers and business partners when used effectively. They also contribute to revenue growth and employee productivity.

Business intelligence software addresses four crucial aspects of data management: data cleaning, data integration, master data management, and metadata management. These tools go beyond basic human analysis by identifying faults and anomalies using algorithms and lookup tables.

How to choose a data quality tool?

Consider these three aspects while selecting a data quality management software to fulfill your company’s requirements:

• You should be able to identify the information issues that exist.
• Recognize what data quality solutions can and cannot accomplish.
• Understand the advantages and drawbacks of different data cleaning solutions.

3 best data quality tool you might need

Data quality management software is essential for data managers who want to assess and improve the overall usability of their databases. Finding a suitable data quality solution necessitates consideration of various criteria, including how and where an organization saves and utilizes information, how data moves across networks, and what sort of data a team wants to tackle.

Basic data quality tools are freely available through open source technologies, but many of today’s solutions include sophisticated features across multiple platforms and database formats. It’s crucial to figure out precisely what a specific data quality solution can accomplish for your company – and whether you’ll need several tools to handle more complex situations.

IBM InfoSphere QualityStage

The Data Quality Appliance from IBM, available on-premises or in the cloud, is a versatile and comprehensive data cleaning and management tool. The objective is to achieve a uniform and correct view of clients, suppliers, regions, and goods. InfoSphere QualityStage was created with big data, business intelligence, data warehousing, application migration, and master data management.

Key values/differentiators:

• IBM provides a variety of key features that help to ensure high-quality data. Deep data profiling software delivers analysis to aid in the comprehension of content, quality, and structure of tables, files, and other formats. Machine learning may auto-tag information and spot possible problems.
• The platform’s data quality rules (approximately 200 of them) manage the intake of bad data. The tool can route difficulties to the correct person to resolve the underlying data issue.
• Personal data that includes taxpayer IDs, credit cards, phone numbers, and other information is identified as personally identifiable information (PII). This feature aids in the removal of duplicate records or orphan data that might otherwise wind up in the wrong hands.
• The platform offers excellent governance and rule-based data handling. It provides strong security measures.

SAS Data Management

The Data Integration and Cleaning Management workstation is a role-based graphical environment for managing data integration and cleaning. It includes sophisticated tools for data governance and metadata management, ETL/ELT, migration and synchronization capabilities, a big data loader, and a metadata bridge to handle big data. SAS was ranked as a “Leader” in Gartner’s 2020 Magic Quadrant for Data Integration Tools.

Key values/differentiators:

• The Data Quality Management (DQM) wizards provided by SAS Data Management are handy in data quality management. These include tools for data integration, process design, metadata management, data quality controls, ETL and ELT, data governance, migration and synchronization, and more.
• Metadata is more challenging to manage in a large organization with numerous users, and it has the potential to lose impact over time as information is exchanged. Metadata management capabilities provided by this tool include accurate data preservation. Mapping, data lineage tools that validate facts, wizard-driven metadata import and export, and column standardization features help maintain data integrity.
• Thirty-eight countries worldwide use native languages for data cleansing, with language and location awareness. The program includes reusable data quality business rules implemented into batch, near-time, and real-time procedures.

Informatica Quality Data And Master Data Management

Informatica has developed a framework to handle various operations connected with data quality and Master Data Management (MDM) to manage and track data quality. This includes role-based abilities, exception management, artificial intelligence insights into issues, pre-built rules and accelerators, and a comprehensive range of data quality transformation solutions.

Key values/differentiators:

• The vendor’s Data Quality solution is excellent at standardizing, validating, enriching, deduplicating, and compressing data. Versions are available for cloud data stored in Microsoft Azure and Amazon Web Services.
• The firm’s Master Data Management (MDM) solution guarantees data integrity via matching and modeling, metadata and governance, and cleaning and enriching. Informatica MDM automates data profiling, discovery, cleansing, standardizing, enriching, matching, and merging within a single central repository.
• Applications, legacy systems, product data, third-party data, online data, interaction data, and IoT data are examples of structured and unstructured information that the MDM platform can capture.

Data cleaning is the backbone of healthy data analysis. When it comes to data, most people believe that the quality of your insights and analysis is only as good as the quality of your data. Garbage data equals garbage analysis out in this case.

If you want to establish a culture around good data decision-making, one of the most crucial phases is data cleaning, also known as data scrubbing.

What is data cleaning, cleansing, and scrubbing?

Clean data is crucial for practical analysis. The first stage in data preparation is data cleansing, cleaning, or scrubbing. It’s the process of analyzing, recognizing, and correcting disorganized, raw data.
Data cleaning entails replacing missing values, detecting and correcting mistakes, and determining whether all data is in the correct rows and columns. A thorough data cleansing procedure is required when looking at organizational data to make strategic decisions.

Clean data is vital for data analysis. Data cleaning sets the foundation for successful, accurate, and efficient data analysis. Because the information in the dataset will be disorganized and scattered without first cleaning it, the analysis process won’t be clear or as precise. Clean data is required for effective analysis; it’s as simple as that.

Data cleaning aims to produce standard and uniform data sets that allow business intelligence and data analytics tools to access and find the relevant data for each query.

What are the benefits of data cleaning? 

Data cleaning is beneficial to your career as a data specialist. Data cleaning helps other businesses, making your position as a data professional easier.

The longer you store insufficient data, the more it will cost your firm in both money and time. This also applies to quantitative (structured) and qualitative (unstructured) data.

It’s the 1-10-100 principle:

It is better to invest $1 in prevention than spend $10 on correction or $100 on fixing a problem after failure.

These are just a few of how it will assist you in your job:

Efficiency

Clean data allows you to conduct your study faster. Because having clean data avoids the creation of numerous mistakes, and your findings will be more accurate, you won’t have to repeat the entire operation because of incorrect results.

Error Margin

Even if you are highly eager for outcomes, the results will not be accurate if the data isn’t clean. As a result, the result may or may not be accurate when you present your work. As a consequence of adopting this practice, you must become accustomed to slowing down and correcting data before presenting it. There’s less room for errors as a result of this.

Accuracy

You’ll soon learn to be more exact with the data you put in at first since data cleaning takes up so much time. Data cleaning will still be required for various reasons, but doing it helps you get used to be more precise from the start.

Data cleaning challenges

Analysts may have difficulties with the data cleaning process since good analysis requires ample data cleaning. Organizations frequently lack the attention and resources to affect the study’s conclusion due to a lack of data scrubbing efficiency. Inadequate data cleansing and preparation are often a cause for inaccuracies slipping through the gaps.

The lack of data scrubbing, which allows for inaccuracies, is not the fault of the data analyst. It’s a symptom of a more significant problem: manual and siloed data cleaning and preparation. Traditional data cleansing and preparation also take too much time beyond the shoddy and faulty analysis.

Forrester Research claims that up to 80% of an analyst’s time is spent on data cleansing and preparation. So much time is spent cleaning data that it’s easy to overlook data cleaning processes. Most businesses require a data cleansing tool to help them analyze the data more efficiently while saving time and money on preparation.

The least enjoyable activity for data scientists is the cleaning and organizing their data, according to 57% of respondents.

Comparison: Data cleaning vs data transformation

Removing data that does not belong in your dataset is known as data cleaning. Data conversion from one form or structure to another is called data transformation.

Cleaning data is one of the most critical tasks for every business intelligence (BI) team. Data cleaning processes are sometimes known as data wrangling, data mongering, transforming, and mapping raw data from one form to another before storing it. This post focuses on the techniques of cleaning up your information.

How to clean data in 6 steps?

The first step in any data cleaning project is to take a step back and assess the overall picture. Consider, what are your objectives and expectations?

You’ll need to develop a data cleanup strategy next to reach those objectives. Focus on your top metrics is a fantastic starting point, but what questions should you ask?

• What is the most important measurement you want to achieve?
• What is your firm’s objective, and what do each of your employees hope to get out of it?

The first step is to gather the key stakeholders and get them to brainstorm.

Here are some best practices for developing a data cleaning procedure:

Monitor errors

Keep track of trends where most of your mistakes originate from. This will make it easier to spot and correct incorrect or faulty data. Records are particularly significant if you’re incorporating multiple solutions into your fleet management system so that other teams don’t get bogged down.

Standardize your process

Make sure that the point of entry is standardized to help minimize duplication.

Validate data accuracy

When you’ve finished cleaning your current database, double-check the consistency of your data. Invest in real-time data management technologies so that you may clean your data regularly. Some tools even employ artificial intelligence (AI) or machine learning to improve testing for accuracy.

Scrub for duplicate data

To help save time when examining data, look for duplicates. Repeated data can be avoided by researching and purchasing various data cleaning tools that may process raw data in bulk and automate the procedure.

Analyze your data

Use third-party sources to integrate it after cleaning, validating, and scrubbing your data for duplicates. Third-party suppliers can obtain information directly from first-party sites and then clean and combine the data to provide more thorough business intelligence and analytics insights.

Communicate with your team

Share the new procedure for cleaning your data with your team to help promote its use. It’s critical to keep your data clean now that you’ve cleaned it. Keeping your teammates informed will assist you in generating and strengthening customer segmentation while also sending more relevant information to consumers and prospects. 

Finally, check and review data regularly to discover any anomalies.

When you’re done with your data, make sure it’s clean. Whether you’re using simple numerical analysis or sophisticated machine learning on huge documents, open-ended survey responses, or consumer comments worldwide, cleaning up your data is crucial in any well-executed study.

7 best data cleaning tools

There is no debate about the value of big data these days. However, if you want the best data possible, it must be as accurate as possible. This implies that your data must be current, accurate, and clean. Using one of these top data cleaning tools might help guarantee this for you.

Several variables determine the specifics of the program you pick. This includes your data source, administration procedures, programs you use, and more. Remember that low-quality data can cause a slew of problems in your company. You could waste money on duplicate records while also missing out on sales. Incorrect addresses may lead to dissatisfied customers or lost income.

Data cleansing tools help you maintain high data quality. These are the some of the best ones:

IBM Infosphere Information Server

The IBM Infosphere Information Server is a data integration platform. It has many of the best data cleaning tools available. IBM’s deal may use end-to-end solutions for a variety of services. This package deal includes standardizing information, classifying and validating data, removing duplicate records, and researching source data. Ongoing monitoring ensures that your data stays clean by catching insufficient information before reaching your applications and services. You can use USAC and AVI to clean your mailing addresses.

This platform offers several additional features, including data monitoring, data transformation, data governance, near-real-time integration, digital transformation, and scalable data quality operations.

Key benefits of IBM Infosphere Information Server

• The project’s goal is to build a comprehensive end-to-end data integration platform.
• It protects against poor-quality data from being exported to other systems.

Oracle Enterprise Data Quality

Oracle Enterprise Data Quality is an excellent data quality management solution. It’s made to supply reliable master data for integrating with your company applications. Address verification, standardization, real-time and batch comparison, and profiling are available data cleaning tools.

The following software is designed for more experienced technical users. It does, however, provide several capabilities that even non-technical persons may utilize right out of the box. Governance, integration, migration, master data management, and business intelligence are all supported by Oracle Enterprise Data Quality.

Key benefits of Oracle Enterprise Data Quality

• Data quality management software with a complete feature set.
• For commercial applications, it provides reliable master data.

SAS Data Quality

Data cleaning software from SAS, known as the SAS Data Quality Tool, is a data quality solution that works to clean data rather than moving it from its origin. Businesses may use this platform for on-premises and hybrid solutions. SAS Data Quality Tool can also utilize it with cloud-based data, relational databases, and data lakes. Deduping, correction, entity identification, and data cleanup are just a few data cleansing tools available.

With this broad range of features, SAS Data Quality is one of the most effective data cleanup solutions. That isn’t all, though. Data quality monitoring, master data management, data visualization, business glossary, and integration are all included in SAS Data Quality.

Key benefits of SAS Data Quality

• This tool works with a lot of different data sources.
• Cleans data at the source

Integrate.io 

Integrate.io is a data pipeline platform that includes ETL, ELT, and replication functionality. With a no-code graphic user interface, you can set up these features in minutes. Before moving it to a data lake, data warehouse, or Salesforce, the transformation layer may clean your data and change it into something different. Integrate.io is one of the best data cleaning solutions because of its wide range of services.

You also have access to several other helpful data integration features in addition to those offered by ETL. The easy-to-use design allows anyone in your company to establish a data pipeline. You may thus free up IT and data team time for other activities. The cloud-based platform also relieves you of routine maintenance and management duties, allowing you to integrate as much or as little as you need. This ensures that you don’t add new technology on top of what you already have. With this adaptable ETL software, you can quickly increase or decrease your usage.

Key benefits of Integrate.io

• User-friendly interface with no programming necessary.
• Data sent to data warehouses are cleaned and masked before it reaches them.
• Cloud-based

Informatica Cloud Data Quality

In Informatica Cloud Data Quality, data quality and data governance are addressed. It does so through a self-service approach that makes it one of the top data cleaning solutions. As a result, it gives everyone in your company the tools they need to access high-quality information for their apps.

Prebuilt data quality rules may be used to quickly deploy numerous services, including deduplication, data enrichment, and standardization procedures. This software package includes data discovery, transformation, address verification, reusable rules, accelerators, and AI. Artificial intelligence is essential since it will allow you to automate many aspects of the data cleaning process.

Key benefits of Informatica Cloud Data Quality

• Data cleansing, transformation, discovery, and governance platform for self-service
• Built-in data quality rules

Tibco Clarity

Tibco Clarity is a one-stop-shop for data cleaning that utilizes a visual interface to simplify data quality improvements, discovery, and conversion. Businesses may use this tool to transform any raw data into usable information for their apps.

You may use deduplication techniques and check addresses before shipping data to the target. While data is being processed, Tibco Clarity provides several graphical representations that you can utilize. This allows you to have a deeper understanding of the data set. For another layer of data quality control, define rules-based validation. After its setup, you may reuse the cleaning procedure configuration for future raw data. Thanks to this unique configuration, Tibco has earned a place on our top data cleansing tools list.

Key benefits of Tibco Clarity

• Visual data cleansing interface
• Data visualizations
• Rules-based validation

Melissa Clean Suite

Melissa Clean Suite is a data cleaning software that improves data quality in many major CRM and ERP systems. It works with Salesforce, Oracle CRM, Oracle ERP, and Microsoft Dynamics CRM. Indeed, one of the most prominent data cleaning programs because of its extensive integration with other applications.

The Melissa Clean Suite has a lot of functions. These include data reduction, contact autocompletion, data verification, data enrichment, up-to-date contact information, real-time and batch processing, and data appendage are just a few examples. Using the supplied plugins, you may integrate this solution with your CRM in minutes.

Key benefits of Melissa Clean Suite

• It works with a wide range of CRM and ERP solutions.
• Cleaning application dedicated to data

Regardless of what type of company you run, you undoubtedly deal with a lot of data. That is why you must do all possible to improve the quality of your data. This implies using one of the top data cleansing tools on the market. The services offered here provide unique advantages and have different pricing plans based on your needs.

You may also tailor your program to suit the needs of particular businesses. Depending on the software you require, you may select from various permission settings, integration choices, and administrative capabilities.

Your objective in business is to produce money, not time. This implies you’ll need to spend less time and resources dealing with duplicated records, managing an unmanageable number of records, and correcting false information.

Do you need a data steward, or do you want to become one? First of all, you have data, which is undoubtedly a blessing. However, instead of the holy grail of usable customer information that marketing, sales, and service teams desire, your staff frequently encounters messy and unreliable data in numerous databases, platforms, and spreadsheets.

Data governance focuses on broad data policies and regulations, whereas data stewardship emphasizes day-to-day coordination and implementation. A data steward connects users with IT departments to manage organizational data.

What is a data steward?

A data steward ensures that an organization’s data is high quality, secure, and well maintained. Defines data elements, creates rules and procedures for data collection and accuracy checking, and executes tests on data systems. Being a steward assures that good data quality is maintained so that they may support the business process. Typically requires a bachelor’s degree in addition to experience or training and has other responsibilities, such as reporting to a manager.

A steward is involved in moderately complex aspects of a project. In general, the work is autonomous and collaborative. Typically, it takes four to seven years of experience to become one.

Companies find that a steward is becoming an essential asset in better managing their data.

Data stewardship is active duty in data management and governance, ensuring that data policies and standards are implemented within the steward’s area. Data stewards aid the company in optimizing domain data assets.

Data steward job description

Data steward job descriptions vary depending on their clients’ demands.

They are often seen as the:

• Data stewards can be business experts in any area of data. They are in charge of ensuring that the data is of good quality and trust, developing organization-wide standards for data usage, and keeping track of all company resources.
• Trusted resources keep the firm in line with evolving data laws locally, regionally, and nationally to dodge penalties or fines.
• Education and training advocates, thought leaders of data, monitoring new best practices and technology, and keeping a pulse on current trends.

But in general, what is this job stands for?

What is data stewardship?

Data stewardship is the process of keeping track of data assets to ensure that they are accessible, functional, safe, and reputable. It covers all aspects of the data lifecycle, from creation to usage through storage and deletion. Data stewardship must provide high-quality data that can be readily accessed regularly.

It assures data quality and consistency as part of an organization’s data governance principles. It comprises the following:

• Knowing everything about the business’s data.
• Make sure that data is accessible, user-friendly, secure, and reliable.
• Understanding where data is stored.
• Keeping data transparent and accurate.
• Setting standards and requirements for utilizing data.
• Enabling the organization to utilize business data to gain a competitive advantage.
• Calling for the use of trustworthy statistics.

Comparison: Data steward vs data analyst

Data stewards collaborate with data analysts and data scientists to interpret data to reveal past trends, detect current patterns, and forecast future outcomes within an organization. Although all of these jobs deal with data, the job responsibilities of a steward vs a data analyst are quite distinct.

Data analysts and data scientists are professionals who use data analysis to extract, organize, and evaluate data to reach conclusions and insights. They create reports on the organization’s past and present performance based on the data retrieved, allowing company leaders to make data-driven decisions. Predictive data analytics is also determined using the collected information.

Data steward and data analyst salary

A data steward’s primary responsibility is to ensure that existing data structures are used effectively by users and run smoothly. They play a secondary role in developing new policies and procedures. They assist with data analytics planning.

In terms of pay, the two occupations are close. Data stewards and data analysts in the United States can earn a yearly salary of about $71,580 and $78,644, respectively.

Abilities needed for data steward and data analyst positions

The most frequent abilities needed for the two employment positions are as follows:

Data Steward

Data analysis, data modeling, data management, and DBMS, Microsoft Excel, technical writing, and presentation skills

Data Analyst

Machine learning, data analysis, statistical analysis, Python, R, SQL, math skills, data visualization

Importance of data steward

They are the quiet, unseen ones behind the scenes who construct automated systems that ensure your company’s data is accurate and up to date, resulting in improved efficiencies.

They are the company’s data management’s public face. Data stewards have a greater sense of security and trust in their data since they create a data-oriented culture and push for effective utilization of and attention to data.

Stewards begin to make greater use of their data over time as a result of dedicated data managers.

Customers are more satisfied. Fewer mistakes are made. Customers who have been misdirected are reached out to first. Thanks to them, leaders with the best closing potential are given the highest priority.

The payoffs might add up quickly. Despite this, the immediate need for a steward may be debatable, while the hard ROI value of the data itself is not readily measurable.

Data stewardship programs

A data steward may be a single individual or a team of individuals, depending on the size, industry, or organization and the importance of data demands and the data program’s maturation.

Creating a new data stewardship program or assessing an existing one may take several months. It begins with the following steps:

• Clarifying objectives and success metrics.
• Examine the present situation and gaps to achieve this.
• Developing, implementing, and maintaining a program are all necessary components of the process.
• Assuring buy-in from stakeholders 
• Creating a precise plan.
• Executing the program.
• Maintain the data stewardship program and monitor it.

Oversight and management of the information lifecycle is critical in data stewardship initiatives. It might include, but isn’t limited to, the following:

• The primary drivers are the business data efforts and operations, particularly data lifecycle management, that govern and enforce how long data is kept.
• Quality assurance programs such as the development and use of quality metrics, verification, and correction procedures.
• Various security functions, such as risk assessment and management based on data governance rules, the security unit, legal department, and risk function – all of which consider control implementation and monitoring.
• Procedures and policies that enable data access to ensure authorized users unrestricted access to necessary data at the right time and format while maintaining data confidentiality and integrity.
• Data stewardship programs are designed to identify data required by their respective functions and understand how the function will utilize data to achieve business goals following data governance rules and the company’s data owners.

A modern business requires data management. As a result, careers in this field are desirable. Whether you wish to work in data stewardship or data analytics, you must devote time to studying statistics, develop your abilities, and acquire competencies that will assist you to succeed in job interviews and obtaining a lucrative position with a top recruiter in the field.

If you’re an employer, you’ve already determined that a data officer is required. Now it’s time to create a job advert and find a steward to fulfill your organization’s needs.

Workflow automation refers to using rule-based logic to start a sequence of operations that can run independently without human involvement. Automated processes can send emails, establish reminders, schedule activities, activate drip campaigns, and more without anyone touching a single button after necessary guidelines and logic are established.

What is workflow automation?

Every activity has a procedure to go through it. On the other hand, many workflows are clogged with time-consuming, error-prone, and costly manual operations. Workflow automation techniques take on those operations with swift and accurate automation. Workflows automate various business activities, from direct to human-assisted to case management, and provide insight into each phase.

Workflow automation not only frees people from the tedious grinds such as manual data entry but also provides different advantages and capabilities to almost every department in an organization.

Why do you need workflow automation?

Automation is crucial for many reasons, including improved operations and efficiency, as well as accuracy. Workflow automation allows employees to work on other activities that require a human touch more efficiently. Productivity also implies cost savings for businesses.

Workflow mapping gives a top-down look at workflows, providing more insight into workflows. Increased visibility allows teams to communicate more accurately.

Workflow automation also enables digital workflows to be tracked and redundant ones eliminated. As a result, inefficiencies and needless operations can be easily detected. Removing human error from the equation also leads to far better outcomes in general.

Customer-related workflows are also automated, just as internal procedures are. Organizations can use automation to address frequent queries, resulting in enhanced customer service and efficiency. In addition, automation solutions have strong marketing and engagement abilities that boost client engagement outcomes.

Workflow automation is a task for automation engineers. We have talked about this in one of our previous articles named “What is automation engineer?“

Cisco automated its CRM system and reduced the number of customer contacts per month by 75,000 while saving over $270 million each year in operational costs

How does workflow automation work?

Workflow automation technology automates time-consuming activities and boosts productivity. Workflow automation solutions ensure that users may quickly automate processes with a few clicks. But how does it work in practice?

It’s critical to begin by thoroughly examining the operation to figure out which team members are executing repetitive tasks. It’s also critical to comprehend the influence these workflows have on determining the significance of each process.

Before getting down to business, you should first establish objectives. This can be beneficial in determining what precisely a workflow is intended to accomplish. Getting the appropriate workflow automation software should be your priority. It should have all of the capabilities you need. You may not need the software with extra features if you are a small or medium-sized business. Your objective should be to choose something that can provide your demands with the best expenditure.

You’ve established goals for each team and have implemented the appropriate software tool. It’s time to plan a workflow using the program now. Choose the applications and services most teams use in the organization and connect them to automate the task with software.

It’s critical to ensure that the entire team is trained on the software to teach how to use it correctly. This is the most crucial step, as it may make or break the whole project. The final stage is activating the automation and saving time and resources once the team has been thoroughly educated. This step should cut down on how much time it takes to complete tasks and improve efficiency.

Lastly, it is essential to measure how workflows work and what value they add. You can achieve this by establishing procedures and tools to see an accurate picture of how well the workflow automation software is working.

How does workflow automation help different departments?

Businesses can use workflow automation in almost every aspect of their operations. Today’s business leaders should understand that workflow automation can help them operate more efficiently rather than harder, whether in marketing, human resources, or finance.

Let’s look at some of the most common use cases for workflow automation in different departments:

• Human resources can benefit from drastically reduced paperwork, ensure compliance, and provide swift onboarding and offboarding experience with workflow automation. Also, automated approvals can lead to much faster sourcing, verifying, and recruiting candidates. Improved workflow visibility is also helpful for HR since they need to assess the operation with accurate data.
• Finance departments enjoy simplified document management, automated payroll, and recurring payment processes while saving time. Finance also uses workflow automation to integrate data with other finance and business software.
• IT departments have the wolf by the ears with the hybrid work era’s dynamic new requirements. While IT specialists work to make their companies eligible for new ways of working, workflow automation helps them streamline IT ticket and escalation processes, continuously watching for shadow IT initiatives in the organization, managing digital assets, and tracking user trends.

• DevOps teams use automated workflows to organize the software development pipeline, monitor and collect data, develop testing code, and deploy tests and code.
• Sales departments utilize workflow automation to streamline list building, capture all the leads, get insightful reports and analyses on ongoing processes, and automate many aspects of their efforts.
• Marketing specialists delegate routine operations to automation systems while ensuring that all material is approved by the appropriate people and work together on workflows with teammates. Companies that have established an automated culture may automate numerous campaigns at once.

What is the difference between dynamic and static workflows?

Workflows may be either dynamic or static. The software can utilize a schema template to determine what steps should be taken during execution when an automated workflow is dynamic. Schema-agnostic workflows are practical because they can take various inputs and have less long-term upkeep. When an automated workflow is static, the steps must occur in a precise order, regardless of any variables that may come into play. When possible, agile workflows should be dynamic and interact with automation to maximize flexibility.

All workflows can be automated to some extent, whether in software development teams or sales. Automating a specific workflow procedure might require automation of a straightforward request/approval process or many workflow activities and resources.

Why not use a business rule engine instead?

An organization can deploy a workflow engine and a business rule engine (BRE) to automate tasks. These two technologies do the same thing with different approaches.

Workflow engines are programs or tools designed to aid users in automating a set of activities that make up a workflow, often within a specific period. A workflow engine, often known as a workflow software program, is the technical component of a process automation solution. The workflow engine saves time and effort for an organization by getting a procedure moving faster.

On the other hand, business rule engines are used to make autonomous decisions based on predefined rule sets. Rule engines run on a set of criteria in software to execute an app’s code if certain conditions are met. In a nutshell, these engines define criteria for how software should function in various situations. A BRE isn’t involved in orchestrating tasks; instead, it is used to automate overhead activities. On the other hand, a workflow engine is intended for workflow management.

A business rules engine (BRE) guides software to make specific decisions in light of the situation. Nontechnical users without extensive programming skills can use a business rule engine to modify their software’s behavior according to defined business requirements.

Can RPA replace workflow automation?

Robotic process automation (RPA) is, in some ways, similar to workflow automation. The distinction between RPA and workflow automation is that RPA is used to automate separate, discrete activities. In contrast, workflow automation is used to automate a series of interconnected operations. While both workflow automation and RPA rely on technology to automate processes, workflow automation places a higher priority on communication between various parts of the process.

Best workflow automation software for 2022

Users can use workflow automation systems to build and modify workflow automation schemes through visual/drag-and-drop interfaces found on many of these software. Shapes and connections represent people and tasks that will be carried out for any request, allowing an automation scheme to be developed based on real-world activities. It enables rapid prototyping of workflows in an easily understood structure. Administrators may use workflow analysis tools like reporting, dashboards, and KPIs to ensure that processes are correctly functioning after they have been implemented.

Workflow automation software should be simple to use, cost-effective, and capable of assisting the company in achieving its business objectives

If a business is attempting to use workflow automation, it must be careful about its software selection. Workflow automation software should be simple to use, cost-effective, and capable of assisting the company in achieving its business objectives. The majority of workflow automation software is offered as a SaaS solution. The following are the top workflow automation software in 2022…

Flokzu

Flokzu is ideal for small teams who wish to optimize their time management and task processes. Users will get pending tasks in their inbox, making it an excellent project management tool, which is one of the tool’s main benefits. It would be beneficial for any team.

Zapier

Zapier is an excellent solution for freelancers and small-to-medium organizations that use various tools. You can connect applications using Zapier. For instance, you might link MailChimp and Typeform with Zapier to send emails and gather leads. Especially useful for marketing and customer support processes.

Integrify

Integrify is a fantastic tool for small- and medium-sized companies wanting to automate routine activities. Its drag-and-drop process builder is quick and straightforward to go from start to conclusion. This tool is perfect for administrative teams.

Kissflow

Kissflow is developed for small businesses that are starting to experiment with workflow automation. The simplicity and user-friendly design of the tool will make it simpler to get started automating procedures. Kissflow provides practical abilities for human resources and accounting teams.

Nintex

ProcessMaker is the only open source and web-based workflow automation tool on our list. It’s a simple-to-use software utilized in healthcare, communications, manufacturing, and education.

Hubspot

HubSpot aims at small and medium-sized enterprises that have yet to use workflow automation and large businesses with established procedures. Businesses can upgrade to many different subscription packages as they need more functionalities.

What should be considered when choosing workflow automation software?

Regardless of which product you lean towards, you want workflow automation software that is quick, adaptable, and suited to your specific demands. Automation is intended to make things easier. Therefore, the product itself should be simple to use too. Examine for a user-friendly interface, no-code options, and drag-and-drop designers.

Using cloud-based workflow automation tools is usually simpler to maintain and operate than on-premises versions. They also provide simple access and security, as well as data scaling. Many cloud-based programs are interoperable with one another. Check if your workflow automation software is compatible with APIs and other tools like Zapier that could help you automate processes.

Many on-prem solution suppliers charge astronomic prices for workflow automation systems. On the other hand, SaaS solutions charge a predictable price and prevent unpleasant fee shocks.

Every business is different, and so are its procedures. Ensure the workflow automation software can handle complicated situations such as conditional steps and multiple branches. You can’t improve a process if you can’t review it. In-built reporting is available in the best workflow automation software solutions to inspect delays, track activities, and adjust.

Workflow automation software must be able to work from anywhere and on any device. Make sure the tool is compatible with the needs of mobile and hybrid workers.

If you are into automation, you can also read our article: The age of hyperautomation: Automate everything possible.

Delving into ERP vs CRM, one needs to make a detailed comparison to decide as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) are the most common software tools businesses use to automate core business activities. ERP connects a company’s financial and operational systems to a central database, allowing it to run successful operations. CRM is used to handle how consumers interact with their companies.

ERP vs CRM

Both ERP and CRM are data storage platforms that touch on several business departments. While they may be developed on the same platform, the software is frequently purchased separately and integrated where necessary.

What is ERP?

Enterprise Resource Planning (ERP) emerged from Material Requirements Planning (MRP), a method for manufacturers to understand and manage all of their resources necessary to run a successful company. ERP is a centralized database that all parts of an organization use. This entails finance, including the general ledger (GL), accounts payable, accounts receivable, payroll, and financial reporting at its most basic level.

ERP also includes product management, order management, procurement and distribution, supply chain management, and data concerning service businesses. ERP touches on procurement, production, distribution, and fulfillment as well. ERP systems may also provide Human Resources Management Systems (HRMS), Customer Relationship Management (CRM), and e-commerce.

What is CRM?

The goal of CRM is to create a single, central location for all customer information so that it may be tracked throughout the customer journey. Businesses can use data and analytics to make more informed decisions on which customers to pursue greater income, how sales teams are doing, how to service clients properly and appropriately, etc. Customer service can use a centralized CRM system to identify whether clients have outstanding customer support issues, allowing them to react appropriately. Alternatively, customer care may rapidly determine whether a caller is a high-value client or a potentially high-value one and direct them to the appropriate service level.

What is the difference between an ERP and a CRM?

While the entire company will rely on ERP and CRM systems, the fundamental distinction between these two technologies is that ERP focuses on financial data. In contrast, sales and customer service departments use CRM to handle customer information. CRM is often referred to as the front office, while ERP is known as the back office. Some ERP systems include a CRM component, while some do not. However, CRM software systems do not have ERP elements. Salesforce.com, for example, is not an ERP system because it does not manage transaction-based data. It may access order history or invoices, but they are imported via an integration with the ERP system.

Both ERP and CRM are business software that uses a relational database to store and analyze data. Both are supplied via a traditional on-premises approach or software as a service (SaaS). The vendor maintains the tool in its own data center, and customers access it through the cloud. CRM systems were quicker to migrate to the cloud since they were easier to build. Companies were initially wary of putting financial data in the cloud.

Why do organizations need both CRM and ERP?

Most businesses, from small and midsize firms (SMBs) to large enterprises, will eventually require both an ERP and a CRM system – or a single platform for both. When businesses run their financials on entry-level accounting software like QuickBooks or even spreadsheets, they frequently seek an ERP system when those systems prove to be restraining their growth, inefficient, or just too basic. This is also true for firms that use individual sales agents’ email clients, spreadsheets, or contact management systems to manage their customer interactions.

The business model of a firm will influence whether it invests in CRM or ERP first. A firm with a small number of high-value clients and complicated financials may be more likely to invest in an ERP system first, whereas one with simple financials and a huge client base that needs regular contact might go for a CRM system. However, both systems are required by most businesses.

How does ERP support CRM?

A technical connection, rather than having two sets of data, is required to transmit B2B and sales data between ERP and CRM systems. When performing an upsell or cross-sell campaign, a sales representative, for example, might wish to look up a customer’s order history, credit status, and outstanding payments. To calculate sales commissions, for example, the finance department might need access to the CRM system if they’re processing payroll or bulk order discounts. An ERP-based CRM system also has advantages for company executives who want to evaluate pricing structures and manage KPIs like client acquisition costs and customer lifetime value (CLV) using a consolidated approach. The CLV can be estimated using a customer lifetime value calculator.

The configuration, pricing, and quotation (CPQ) procedure is a typical integration between CRM and ERP that many organizations execute. CPQ applications need data from both the CRM and ERP systems to work effectively. Most CRM and ERP solutions include prebuilt connections between them, which are provided by a third-party partner. These integrations, on the other hand, may be pricey and time-consuming to maintain when the CRM or ERP system is upgraded.

Is Salesforce an ERP or CRM?

Salesforce is a CRM platform, not an ERP system. Salesforce CRM offers several important sales and service capabilities, but it does not include ERP tools such as inventory, manufacturing, supply chain, or financial management.

Is Microsoft Dynamics an ERP?

Microsoft Dynamics AX is a multi-site, multi-country ERP system that helps medium and large businesses run efficiently across the globe. With a robust and adaptable industry infrastructure in Microsoft Dynamics AX, you may standardize procedures, give visibility throughout your organization, and simplify compliance. Microsoft Dynamics AX has been rebranded as Microsoft Dynamics 365 for Finance and Operations. Dynamics 365 is a cloud-based business applications platform that combines components of Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP), as well as productivity apps and artificial intelligence technologies.

Artificial intelligence (AI) and data analytics are rapidly growing trends in the tech world. With increasing potential for innovation, it is paramount that we stay up to date with all the latest developments in this field. According to MarketsandMarkets, the worldwide artificial intelligence (AI) market will increase from USD 58.3 billion in 2021 to USD 309.6 billion by 2026, at a compound annual growth rate (CAGR) of 39.7 percent over the projected period. It seems that every company wants a piece of this growing pie. By 2022 it is expected that 90% of companies will be using some form of artificial intelligence for data analytics purposes.

COVID-19 is a pandemic that has swept the globe. Based on seroprevalence investigations, the United States is currently expected to have more than 6 million cases, with many more people thought to be exposed and asymptomatic. With the various COVID-19-related datasets that have been collected, AI is assisting us in fighting this virus through applications such as early detection and diagnosis, contact tracing, case and death projections, medication and vaccine research, and so on.

What are AI and Data Analytics?

Artificial intelligence (AI) is a broad term that defines machines capable of human-like functions. Specifically, it refers to the ability of an application to be able to solve problems on its own. In other words, AI allows computers to learn and perform tasks without direct instructions from users. Perceptual computing is one of the most important fields in artificial intelligence research. It deals with computer vision, which is commonly defined as the ability of a computer. Artificial intelligence is the theory and development of computer systems capable of performing activities normally requiring human intelligence.

Data analytics is a process by which large amounts of data are analyzed to reveal patterns, trends, and associations, especially relating to human behavior and interactions. In increasingly automated work environments, artificial intelligence has become a crucial tool for companies looking to streamline their processes and cut costs. Data analytics is the ability to find insights from data and make informed decisions based on that. AI technology is used in data analytics to help us understand these insights faster and make smarter decisions.

The COVID-19 Pandemic: How It Will Reinforce The Need for AI and Data in 2022

In 2022, Artificial intelligence and big data will be the driving force in the growth of humanity and the economy. One of the major factors that will accelerate this is the emergence of artificial intelligence and big data. Data analytics is not just a trending topic. In fact, it is one of the most important trends in the future. Artificial intelligence will be a major part of the future and will be used to control and improve every industry.

While advanced AI applications in healthcare hold great promise, we currently lack the large datasets and accuracy of data required to go beyond fairly simple algorithms and truly improve outcomes. AI, at its most basic level, is the process of teaching machines to behave like humans, automating tasks like coding claims and scheduling appointments. AI is most commonly used in healthcare today to automate tasks like call center routing and appointment scheduling.

There are at least two reasons why we don’t have the necessary data sets to fulfill AI’s promise in healthcare. For starters, much of our healthcare data is siloed between providers’ offices, health insurers’ offices, laboratories, and other locations. Each location collects patient data, but the data sets do not communicate with one another. Second, much of what influences health occurs outside of healthcare settings, in places where patients live, work, and play.

The good news is that activity in this field is brisk. COVID-19 compelled us to digitize healthcare interactions, and federal regulations require that datasets adhere to standards that enable integration. These trends point to exponential growth in the size and granularity of our datasets, allowing healthcare data scientists to begin training the models required to fully realize AI’s potential to impact clinical outcomes.

The Primary Contributions of AI-based Data Analytics in COVID

Artificial intelligence and other cutting-edge technologies used to combat the pandemic aided in early detection and diagnosis, trend analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control. Some contributions of AI-based data-driven analytics in COVID are:

• AI impacted the COVID-19 era in six distinct ways, including epidemic containment strategies (ECS), epidemic data life cycle (EDLC), epidemic handling with heterogeneous source data (EHHSD), healthcare-specific AI (HCSAI) services, general epidemic AI services (GEAIS), and drug design and repurposing (DDAR) against COVID-19, which have not been covered in the recent literature
• The difficulties in applying AI to available epidemic data that is not in desirable form at the moment due to a variety of issues (e.g., diverse formats, legislation, heterogeneous sources, and privacy concerns, among others).
• It elaborates on the privacy issues that arise as a result of the ongoing pandemic’s movement of person-specific data in cyberspace.
• It provides a concise overview of the most recent technologies, other than AI, that have contributed to the fight against the recent pandemic through innovative features.
• It discusses numerous cutting-edge studies that have used AI techniques for good in the ongoing COVID-19 pandemic.

Conclusion – Why You Can’t Afford To Ignore The Power of AI and Data Analytics

COVID-19 has many complex clinical implications for people who have underlying diseases like diabetes, pneumonia, and heart disease, to name a few. As a result, we may need to use AI and data analytics to deal with the clinical implications of COVID-19’s prognosis. To summarize, Data Analytics and AI are the business of the future. As a result, if you want to help your business, you should consider adopting these cutting-edge technologies. Now, whether you are a business owner or a service provider, you must be well-versed in technologies before implementing them.

This article was originally published on Hackernoon and is reproduced with permission.

The problems in front of you in business will often be relatively straightforward. But increasingly, business issues aren’t easy or black and white. They’re filled with a high number of constantly moving variables that make any sort of predictability difficult to achieve. Solving these problems that are seemingly impossible to analyze is achievable, but it takes a decidedly different approach than what many entrepreneurs and leaders are used to.

What’s Created So Many Difficult Problems?

One cornerstone in the foundation of these problems is that environments and ways of thinking are shifting. The world is dramatically different (e.g., climate change, growing calls for equality), and what might not have been considered or prioritized in the past now often cannot be left out of the picture. As these shifts occur, people have created new options and connections between systems or points of living that didn’t exist before. 

As frontrunners of innovation in a space where new precedents are constantly being established, digital natives have a head-start navigating the unpredictable terrain in which many traditional businesses are increasingly finding themselves. The lessons digital companies have learned from encountering – and solving – VACA (Volatile, Uncertain, Complex, Ambiguous) problems regularly can provide a solid foundation upon which any business can build a winning strategy.

Behind all of this is an explosion in digital. Thanks to technology, the power that once rested squarely with companies – particularly huge corporations – has shifted to the consumer. Instead of businesses telling buyers what to consume through advertisements and knowing what reach and sales likely will be, people can directly share what they think, hold companies accountable, and conduct their transactions from virtually anywhere. This new relationship is much more two-way than in the past.

Because of all the technology and new links that exist, and because customers now are co-drivers with behavior that is so quick and fickle, there’s no real way for leaders to have clear certainty about what to do. The old approach where businesses relied on data and best practices, which still has significant sway and inertia in the corporate space, is impractical and directly contradictory to developing the desired degree of innovation necessary to stay competitive. Companies have to make decisions even when they have no precedent or standard from others.

Don’t Analyze and Model to Death, Just Go Do It

One key difference between traditional businesses and digital natives lies in the fundamental approach each takes when dealing with change. In the past, more conventional operation models rested on the idea of long-process modeling. For instance, if you wanted to sell something, you’d likely do some interviews, make a prototype, test it, send it to focus groups, get feedback, tweak it, and only bring it to market when your data suggested it was what customers wanted. However, when you have an uncertain problem, the best approach is just to do something about it. Instead of predicting how people will respond through all this extensive modeling, just go ahead and put the product in front of the consumer and see what happens, then escalate based on your results.

Digital means that you can do this sort of real-world, experiment-based probing much more easily. You don’t necessarily have to rely on physical infrastructures or tools to figure out what people think or get items in play. You can reduce your ideas to really small things so that you can move through many rapid iterations.

By keeping shifts and tests tiny and conducting them in the actual consumer environment, you can decide early on what to drop and what to keep and minimize your financial losses. You gain a significant degree of agility and granularity even as your risk decreases, and you don’t waste months or years on projects nobody wants. Those elements look extremely attractive to most investors, and customers end up feeling like you’re listening to them as individuals as they move through their unique journey.

Examples of companies that use this problem-solving approach include Google and Amazon. They push new products or functionalities just about every week to about one percent of their base. This way, they don’t disrupt the majority of their customers as they explore what to scale. But because they have so many customers, one percent still translates to a sample group that’s adequately sized.

In the Long-Term, Humility Yields Success

Many leaders of the champion companies of the 20th century still believe that they are paid to know the best way to surmount any obstacle. They struggle against not providing or getting concrete answers to today’s complex problems. So the biggest hurdle to taking the “just do” strategy for complex problem solving is simply being humble enough to admit that not knowing is okay. You must be willing to accept that being the smartest in the room isn’t as valuable as it used to be.

While this is not easy, it’s also liberating. It allows you to see what works for you instead of relying on copying anyone else, and it frees up your organization’s creativity and energy. It empowers teams to take action and trust themselves and each other, even if they don’t know precisely how they’ll get to the finish line. All of that delivers a laudable amount of yield with bankable value. It also provides a sense that the group is adaptable and capable of staying in the game and winning it.

So if you want to innovate, quit looking for blueprints where there aren’t going to be any. Use data where it makes sense, but be willing to get your products out into the real world so you can see what users actually will do. You’ll work faster and more economically, and the relationships you build through direct interaction with your customers will be priceless.

For decades, managing data essentially meant collecting, storing, and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information that can be pulled from the massive amounts of data being generated, accessed, and stored in myriad locations, from corporate data centers to the cloud and the edge. Given that, data analytics – helped by such modern technologies as artificial intelligence (AI) and machine learning – has become a must-have capability, and in 2022, the importance will be amplified.

Enterprises need to rapidly parse through data – much of it unstructured – to find the information that will drive business decisions. They also need to create a modern data environment in which to make that happen.

Below are a few trends in data management that will come to the fore in 2022.

Data lakes get more organized, but the unstructured data gap still exists

There are two approaches to enterprise data analytics. The first is taking data from business applications such as CRM and ERP and importing it into a data warehouse to feed BI tools. Now those data warehouses are moving to the cloud, with technologies like Snowflake. This approach is well understood, as the data has a consistent schema.

The second approach is to take any raw data and import it directly into a data lake without requiring any pre-processing. This is appealing because any type of data can be funneled into a data lake, and this is why Amazon S3 has become a massive data lake. The trouble is, some data is easier to process than others. For instance, log files, genomics data, audio, video, image files, and the like don’t fit neatly into data warehouses because they lack a consistent structure, which means it’s hard to search across the data. Because of this, data lakes end up becoming data swamps: it is too hard to search, extract and analyze what you need.

The big trend now and a continuing data trend for 2022 is the emergence of data lake houses, made popular by DataBricks, to create data lakes with semi-structured data that does have some semantic consistency. For example, an Excel file is like a database even though it isn’t one, so data lake houses leverage the consistent schema of semi-structured data. While this works for .csv files, Parquet files, and other semi-structured data, it still does not address the problem of unstructured data since this data has no obvious common structure. You need some way of indexing and inferring a common structure for unstructured data, so it can be optimized for data analytics. This optimization of unstructured data for analytics is a big area for innovation, especially since at least 80% of the world’s data today is unstructured.

Citizen science will be an influential, related 2022 trend

In an effort to democratize data science, cloud providers will be developing and releasing more machine learning applications and other building block tools such as domain-specific machine learning workflows. This is a seminal trend, because, over time, the level of what individuals will need to code is going to decrease. This will open up machine learning to many more job roles: some of these citizen scientists will be within central IT, and some will live within lines of business. Amazon Sagemaker Canvas is just one example of the low-code/no-code tools that we’re going to see more of in 2022. Citizen science is quite nascent, but it’s definitely where the market is heading and an upcoming data trend for 2022. Data platforms and data management solutions that provide consumer-like simplicity for users to search, extract and use data will gain prominence.

‘Right data’ analytics will surpass Big Data analytics as a key 2022 trend

Big Data is almost too big and is creating data swamps that are hard to leverage. Precisely finding the right data in place no matter where it was created and ingesting it for data analytics is a game-changer because it will save ample time and manual effort while delivering more relevant analysis. So, instead of Big Data, a new trend will be the development of so-called “right data analytics”.

Data analytics ‘in place’ will dominate

Some prognosticators say that the cloud data lake will be the ultimate place where data will be collected and processed for different research activities. While cloud data lakes will assuredly gain traction, data is piling up everywhere: on the edge, in the cloud, and in on-premises storage. This calls for the need to, in some cases, process and analyzes data where it is, versus moving it into a central location because it’s faster and cheaper to do so. How can you not only search for data at the edge, but also process a lot of it locally, before even sending it to the cloud? You might use cloud-based data analytics tools for larger, more complex projects. We will see more “edge clouds,” where the compute comes to the edge of the data center instead of the data going to the cloud.

Storage-agnostic data management will become a critical component of the modern data fabric

A data fabric is an architecture that provides visibility of data and the ability to move, replicate and access data across hybrid storage and cloud resources. Through near real-time analytics, it puts data owners in control of where their data lives across clouds and storage so that data can reside in the right place at the right time. IT and storage managers will choose data fabric architectures to unlock data from storage and enable data-centric vs. storage-centric management. For example, instead of storing all medical images on the same NAS, storage pros can use analytics and user feedback to segment these files, such as by copying medical images for access by machine learning in a clinical study or moving critical data to immutable cloud storage to defend against ransomware.

Multicloud will evolve with different data strategies

Many organizations today have a hybrid cloud environment in which the bulk of data is stored and backed up in private data centers across multiple vendor systems. As unstructured (file) data has grown exponentially, the cloud is being used as a secondary or tertiary storage tier. It can be difficult to see across the silos to manage costs, ensure performance and manage risk. As a result, IT leaders realize that extracting value from data across clouds and on-premises environments is a formidable challenge. Multicloud strategies work best when organizations use different clouds for different use cases and data sets. However, this brings about another issue: moving data is very expensive when and if you need to later move data from one cloud to another. A newer concept is to pull compute toward data that lives in one place. That central place could be a colocation center with direct links to cloud providers. Multicloud will evolve with different strategies: sometimes compute comes to your data, sometimes the data resides in multiple clouds.

Enterprises continue to come under increasing pressure to adopt data management strategies that will enable them to derive useful information from the data tsunami to drive critical business decisions. Data analytics will be central to this effort, as well as creating open and standards-based data fabrics that enable organizations to bring all this data under control for analysis and action.

This article on data analytics was originally published in VentureBeat and is reproduced with permission.

For decades, managing data essentially meant collecting, storing, and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information they can pull from the massive amounts of data generated, accessed, and stored in myriad locations, from corporate data centers to the cloud and the edge.

Given that, data analytics – helped by such modern technologies as artificial intelligence (AI) and machine learning – has become a must-have capability and in 2022, the importance will be amplified. Enterprises need to rapidly parse through data – much of it unstructured – to find the information that will drive business decisions. They also need to create a modern data environment in which to make that happen.

Below are a few trends in data management that will come to the fore in 2022:

Data managers will broaden their focus from structured data to unstructured data analytics

Traditionally, a lot of data science was focused on feeding structured data to data warehouses. But with 90 percent of the world’s data becoming unstructured and with the rise of machine learning, which relies on unstructured data, data scientists should broaden their skills to incorporate unstructured data analytics. They need to learn to glean value from data with no specific structure or schema and ranges across video files, genomics files, seismic images, IoT data, audio recording,s and user data such as emails. Developing these skills, which involves staying current and experimenting with new unstructured data analytics capabilities in data lakes as well as learning unstructured data management techniques, will be paramount in 2022.

‘Right data’ analytics will surpass Big Data analytics as a key trend 

Big Data is almost too big and is creating data swamps that are hard to leverage. Precisely finding the right data in place no matter where it was created and ingesting it for data analytics is a game-changer because it will save ample time and manual effort while delivering more relevant analysis. So, instead of Big Data, a new trend will be the development of so-called “right data” analytics. 

Storage-agnostic data management will become a critical component of the modern data fabric

A data fabric is an architecture that provides visibility of data and the ability to move, replicate and access data across hybrid storage and cloud resources. Through near real-time analytics, it puts data owners in control of where their data lives across clouds and storage so that data can reside in the right place at the right time. IT and storage managers will choose data fabric architectures to unlock data from storage and enable data-centric vs. storage-centric management. For example, instead of storing all medical images on the same NAS, storage pros can use analytics and user feedback to segment these files, such as copying medical images for access by machine learning in a clinical study or moving critical data to immutable cloud storage to defend against ransomware. 

Data fabrics will be a strategic enterprise IT trend in 2022 

Data fabric is still a vision. It recognizes that your data exists in many places, and a fabric can bridge the silos and deliver greater portability, visibility, and governance. Data fabric research has typically focused on semi-structured and structured data. But 90 percent of the world’s data is unstructured (think videos, X-rays, genomics files, log files, and sensor data) and has no defined schema. Data lakes and analytics applications cannot readily access this dark data locked in files. Data fabric technologies need to bridge the unstructured data storage (file storage and object storage) and data analytics platforms (including data lakes, machine learning, natural language processors, and image analytics). Analyzing unstructured data is becoming pivotal because machine learning relies on unstructured data. Data fabric technologies need to be open and standards-based and look across environments. In 2022, the data fabric should move from a vision to a set of architectural data management principles. Given its rising relevance and sheer magnitude, technology vendors need to incorporate unstructured data into their data fabric architectures.

Multi-cloud will evolve with different data strategies

Today, many organizations have a hybrid cloud environment in which the bulk of data is stored and backed up in private data centers across multiple vendor systems. As unstructured (file) data has grown exponentially, the cloud is used as a secondary or tertiary storage tier. It can be difficult to see across the silos to manage costs, ensure performance and manage risk. As a result, IT leaders realize that extracting value from data across clouds and on-premises environments is a formidable challenge. Multi-cloud strategies work best when organizations use different clouds for different use cases and data sets. However, this brings about another issue: moving data is very expensive when and if you need to later move data from one cloud to another. A newer concept is to pull compute toward data that lives in one place. That central place could be a colocation center with direct links to cloud providers. Multi-cloud will evolve with different strategies: sometimes compute comes to your data, sometimes the data resides in multiple clouds.

Synthetic data + unstructured data will be needed to manage data growth

Data security and privacy are becoming more pressing, and synthetic data is an excellent solution to prevent user data collection. Synthetic data is also more portable since you do not have many privacy laws to consider. While synthetic data reduces the footprint of customer data, it is still a tiny fraction of the total unstructured data. The bulk of data is application-generated, not user data, so synthetic data coupled with unstructured data management is needed to manage data growth.

Enterprises continue to come under increasing pressure to adopt data management strategies that will enable them to derive useful information from the data tsunami to drive critical business decisions. Analytics will be central to this effort, as will creating open and standards-based data fabrics that enable organizations to bring all this data under control for analysis and action.

Market trend analysis is an indispensable tool for companies these days. Social media gives analysts access to data that might otherwise be tough to collect. Rapidly changing business conditions require deep insight, and a market trend analysis report is a critical tool. Aside from future-proofing businesses, trend analysis reports also help companies tune into current dynamics and create better products or services.

There are many tools and data sources trend analysts use to prepare a market analysis report. However, social media data offers the most fertile ground. Today there are over 4.5 billion social media users worldwide. That’s over half the world’s population accessing social media and interacting with content.

Social media data is even more valuable because of the high costs of generating original research from scratch. In essence, social media platforms offer all the data companies need, and cost-effectively. Here are three major insights that market trend analysts can derive from social media data.

Consumer Preferences

Every business lives and dies with its customers, and assessing consumer preferences is a tough task. While existing customers often make their intentions clear with their purchase patterns over time, market trends often shift and push potential future customers away from a brand’s messaging.

“Usually, the first signs of a shift (in market trends) show themselves through social media or engagement metrics,” writes SimilarWeb’s Daniel Schneider in a recent post on market trend analysis. “This crucial rise or fall in traffic, engagement, or variation in demographics is what reveals your competitive advantage.” In this context, competitive advantage refers to a company or brand’s position in the market and its appeal to consumers, relative to how its competitors are perceived in “the conversation.”

Social media engagement data offers a wealth of insight in this regard. For instance, high-level data such as the number of comments or likes, and engagement per hashtag, provide companies insight into which topics niche consumers are interested in. Monitoring the trends in these metrics also reveals broader market shifts.

A company’s engagement rate trend and conversion ratios offer insight into marketing effectiveness over time. In the same way that a decreasing sales conversion rate over time points to a possible disconnect with consumers, so too does a falling follower or subscriber count.

Thanks to rising social awareness, companies are expected to take stands on important issues these days. Monitoring the usage of hashtags related to these issues, keeping an eye on trending topics, and tracking engagement metrics on content that addresses these issues helps companies easily tune into the current market climate.

When compared to conducting surveys or polls, there’s no doubt that social media data removes biases and presents user opinions in a useful manner.

Seasonal Trends

Many industries are subject to seasonal trends, and market analysts need to figure them out. The consequences of predicting an incorrect trend can be catastrophic, thanks to production and procurement schedules tied to seasonal demand. 

A market trend analysis that mines social media demographic data will uncover seasonal trends at multiple levels. At a high level, trend analysts can figure out who their customers are and what their tendencies look like. Platforms such as Facebook’s Ad Manager provide a wealth of information, right down to the type of devices the user prefers and even their political leanings.

Analysts can dig deeper into these data and uncover specific data points that help them segment their customer audience. For instance, customers older than 50 might prefer a product during fall, but a younger audience might prefer it during spring. By providing demographic data, trend analysts can help their companies meet demand intelligently.

Market trend reports informed by such data help companies anticipate trends that might develop in the future. As strategic business advisor Bernard Marr points out, “By practicing market analysis, you can stay on top of which trends are having the most influence and which direction your market is headed — before any major changes take place — leaving you well placed to surpass your competition.”

Social media data provides companies an easy way to access data that points to major trend changes. Demographic data allows companies to isolate audiences who might form a future customer base and figure out their preferences in advance. In turn, this helps them create production schedules that match that audience’s seasonal preferences.

Market Dynamics

The market a business operates in is subject to a variety of forces. Chief among these is competitor activity. Disruptive products introduced by competitors can seriously harm a company’s earning ability. A famous example of this is Apple eliminating the likes of Palm and Blackberry within a few years after the release of the iPhone.

Monitoring a brand’s social share of voice and comparing that to its competition helps trend analysts figure out who’s occupying the top of users’ minds in the market. Analysts can also correlate these trends to sales volumes and connect product improvements, marketing strategies, and discover broad market trends. These data also help companies build lasting relationships with their customers.

Given the fast pace with which consumer preferences change these days, traditional data-gathering techniques will leave companies playing catch-up. “Because so much of the world is sharing its opinions on every subject at all hours of the day, trends and markets can shift quickly,” says Meltwater’s Mike Simpson. “It is not just the customer of next year or next month that organizations need to consider — but the customer of the next day.”

Whether it’s trends in engagement, demographics, or competitor data, social media data helps analysts gain perspective on how the market is headed.

A Full Picture

Social media platforms offer a treasure trove of user data. Market trend analysts can mine these data continuously to connect business performance and consumer behavior. Social media gives companies a real-time, cost-effective look into their customers’ minds compared to traditional data-gathering methods.

The world has come to rely on data. Data-driven analytics fuel marketing strategies, supply chain operations, and more, and often to impressive results. However, without careful attention to data accuracy, these analytics can steer businesses in the wrong direction.

Just as data analytics can be detrimental if not executed properly, so too can the misapplication of data analysis lead to unintended consequences. This is especially true when it comes to understanding accuracy in data.

What Is Data Accuracy?

Data accuracy is, as its sounds, whether or not given values are correct and consistent. The two most important characteristics of this are form and content, and a data set must be correct in both fields to be accurate.

For example, imagine a database containing information on employees’ birthdays, and one worker’s birthday is January 5th, 1996. U.S. formats would record that as 1/5/1996, but if this employee is European, they may record it as 5/1/1996. This difference could cause the database to incorrectly state that the worker’s birthday is May 1, 1996.

In this example, while the data’s content was correct, its form wasn’t, so it wasn’t accurate in the end. If information is of any use to a company, it must be accurate in both form and content.

Why Is Data Accuracy Important?

While the birthday example may not have significant ramifications, data accuracy can have widespread ripple effects. Consider how some hospitals use AI to predict the best treatment course for cancer patients. If the data this AI analyzes isn’t accurate, it won’t produce reliable predictions, potentially leading to minimally effective or even harmful treatments.

Studies have shown that bad data costs businesses 30% or more of their revenue on average. If companies are making course-changing decisions based on data analytics, their databases must be accurate. As the world comes to rely more heavily on data, this becomes a more pressing concern.

How to Improve Data Accuracy

Before using data to train an algorithm or fuel business decisions, data scientists must ensure accuracy. Thankfully, organizations can take several steps to improve their data accuracy. Here are five of the most important actions.

1. Gather Data From the Right Sources

One of the best ways to improve data accuracy is to start with higher-quality information. Companies should review their internal and external data sources to ensure what they’re gathering is true to life. That includes making sure sensors are working correctly, collecting large enough datasets, and vetting third-party sources.

Some third-party data sources track and publish reported errors, which serves as a useful vetting tool. When getting data from these external sources, businesses should always check these reports to gauge their reliability. Similarly, internal error reports can reveal if one data-gathering process may need adjustment.

2. Ease Data Entry Workloads

Some data is accurate from the source but becomes inaccurate in the data entry process. Errors in entry and organization can taint good information, so organizations must work to eliminate these mistakes. One of the most significant fixes to this issue is easing the manual data entry workload.

If data entry workers have too much on their plate, they can become stressed or tired, leading to mistakes. Delegating the workload more evenly across teams, extending deadlines, or automating some processes can help prevent this stress. Mistakes will drop as a result.

3. Regulate Data Accessibility

Another common cause of data inaccuracy is inconsistencies between departments. If people across multiple teams have access to the same datasets, there will likely be discrepancies in their inputs. Differences in formats and standards between departments could result in duplication or inconsistencies.

Organizations can prevent these errors by regulating who has access to databases. Minimizing database accessibility makes it easier to standardize data entry methods and reduces the likelihood of duplication. This will also make it easier to trace mistakes to their source and improve security.

4. Review and Clean Data

After compiling information into a database, teams must cleanse it before using it in any analytics process. This will remove any errors that earlier steps didn’t prevent. Generally speaking, the data cleansing workflow should follow four basic steps: inspection, cleaning, verifying, and reporting.

In short, that means looking for errors, fixing or removing them (including standardizing formats), double-checking to verify the accuracy, and recording any changes made. That final step is easy to overlook but crucial, as it can reveal any error trends that emerge between data sets.

5. Start Small

While applying these fixes across an entire organization simultaneously may be tempting, that’s not feasible. Instead, teams should work on the accuracy of one database or operation at a time, starting with the most mission-critical data.

As teams slowly refine their databases, they’ll learn which fixes have the most significant impact and how to implement them efficiently. This gradual approach will maximize these improvements’ efficacy and minimize disruptions.

Data Accuracy Is Essential for Effective Analytics

Poor-quality data will lead to unreliable and possibly harmful outcomes. Data teams must pay attention to data accuracy if they hope to produce any meaningful results for their company.

These five steps provide an outline for improving any data operation’s accuracy. With these fixes, teams can ensure they’re working with the highest-quality data, leading to the most effective analytics.

Data is an indispensable resource for retailers. Today, most retail businesses understand that they must capitalize on digital data, but fewer know how to make the most of it. If these companies hope to reach their full potential, they must improve their data management.

Data management is often a struggling point for businesses. In a 2017 study, only 3% of companies’ data met basic quality standards, and 47% of newly created records contained at least one critical error. Retailers are rushing to take advantage of data, but poor management is holding back their results.

Improved data management can help retailers see better returns from these operations. Here are seven opportunities for these businesses to benefit from better data management.

1. Increasing Marketing Personalization

Personalized marketing is one of the most common uses of data in retail. Retailers could significantly improve these efforts by putting more emphasis on the management side of data. For example, enriching their first-party information with data from third-party sources can help target consumers on a more specific level.

Data enrichment can reveal more about the users retailers are trying to target with personalized marketing. They can then reach out to them through multiple channels, emphasizing those they use the most. This will make these efforts more effective, leading to higher conversion rates.

2. Preventing Mistargeting

Similarly, better data management can improve personalized marketing by minimizing errors. Mistargeting is a shockingly common issue, with 96% of surveyed consumers receiving mistargeted information or promotions. Retailers can avoid these mistakes by using more thorough data cleansing and enrichment.

Before using data to customize marketing campaigns, retailers should check it against other sources to verify its accuracy. Removing questionable or unverifiable information will prevent mismarketing, create more relevant messages, and keep consumers engaged.

3. Minimizing Supply Chain Costs

Another area of untapped potential for data in retail is supply chain management. Just as retailers must cleanse and verify consumer data in marketing, they must ensure supply chain data is accurate before acting on it. Better accuracy and organization in these data sets can lead to significant savings.

For example, fuel is often one of the highest fleet expenses, and fuel savings rely on many factors. Poor or misleading data about regional fuel costs, engine efficiency, or route travel time can lead to higher fuel consumption. Spending more time and effort ensuring this data is accurate will help minimize these expenses.

4. Improving the Accuracy of e-Commerce Listings

Better data management can also improve the functionality of retailers’ e-commerce sites. Poor data management can lead to errors like incomplete information on product listings or inaccurate inventory figures. These errors can then impact shoppers, making them less likely to return after a negative site experience.

Before listing new items on their online store, retailers should look over the data to ensure it’s complete. Similarly, they should clean and organize inventory data to ensure it updates in a timely manner, reflecting actual stock numbers. Even small changes like this can have a considerable impact.

5. Avoiding Stock Shortages

Keeping accurate inventory records is essential for more than just maintaining functional e-commerce sites, too. Retailers who improve their inventory data management can prevent shortages by gaining a better understanding of their needs.

Data cleansing and organization will provide more transparency over retailers’ supply of various items. Retailers can then enrich these records with sales data to predict what customers want before these trends shift. They can then adjust inventory levels accordingly to meet changing seasonal demand, minimizing waste.

6. Appealing to Socially- and Eco-Conscious Consumers

Better data management improves visibility across retailers’ processes by compiling and structuring otherwise vast, unnavigable data sets. When retailers improve their internal transparency, they can then become more transparent with consumers. This will help appeal to eco-conscious or socially-minded customers.

Data management makes it easier to track where parts and products come from or how much waste a company generates, for example. Retailers can then communicate this information to consumers to demonstrate their social or environmental governance. This transparency will build trust and can improve sales.

7. Complying With Data Privacy Regulations

As retailers collect more data, data privacy regulations become a more relevant concern. At least 21 states have proposed privacy legislation that retailers may have to comply with, requiring more insight into their data operations. This compliance will be far easier with better data management.

Deduplicating, cleansing, and organizing data will make it easier to provide any necessary documentation to authorities. Similarly, it can offer more insight into retailers’ data operations, showing whether and how they need to adjust to remain compliant. As these regulations become more common and strict, this will become a crucial consideration.

Better Data Management Is Key to Retail Success

Data can be a retailer’s most valuable resource if they can use it properly. Better data management will unlock data’s full potential, improving retail operations across multiple fronts.

These seven areas aren’t the only opportunities retailers have to benefit from better data management, but they are among the most impactful. By addressing data management in these areas, retailers can experience considerable improvements.

If there’s one topic that has captured the public’s attention consistently over the decades, it is this: have aliens visited Earth, and have we caught them in the act on camera? Unidentified Flying Objects (UFOs) and Unidentified Aerial Phenomena (UAPs) tick all the boxes regarding our love of conspiracy theories, explaining the unexplainable, and after-hours conversation starters.

As with many things in life, data may have the answer. From Peter Sturrock’s survey of professional astronomers that found nearly half of the respondents thought UFOs were worthy of scientific study, to the SETI@Home initiative, which used millions of home computers to process radio signal data in an attempt to find alien communications, UFOs and UAPs continue to fascinate the world.

However, the scientific community seems to have a dim view of studying these phenomena. A search of over 90,000 grants awarded by the National Science Foundation finds none addressing UFOs, UAPs, or related topics.

But the tide may be turning.

A US Intelligence report released in June 2021 (on UAPs specifically – the US military is keen to rebrand UFOs to avoid the “alien” stigma associated with the UFO acronym) has rekindled interest within a broad audience.

Among other findings, the report noted that 80 of the 144 reported sightings were caught by multiple sensors. However, it also stated that of those 144 sightings, the task force was “able to identify one reported UAP with high confidence. In that case, we identified the object as a large, deflating balloon. The others remain unexplained.”

UAP data requires new ways of working. The ability to fuse, analyze, and act on inherently spatial and temporal data in real-time requires new computing architectures beyond the first generation of big data. 

Vectorization and the quest to identify UFOs/UAPs

Enter “vectorization.” A next-generation technique, it allows for the analysis of data that tracks objects across space and time. Vectorization can be 100 times faster than prior generation computing frameworks. And it has the attention of significant players, such as Intel and NVIDIA, which are both pointing towards vectorization as the next big thing in accelerating computing.

NORAD and USNORTHCOM’s Pathfinder initiative aims to better track and assess objects through the air, sea, and land through a multitude of fused sensor readings. As part of the program, it will be ‘vectorizing’ targets. One company helping to make sense of this is Kinetica, a vectorization technology startup, which provides real-time analysis and visualization of the massive amounts of data the Pathfinder initiative monitors.

“After a year-long prototyping effort with the Defense Innovation Unit, Kinetica was selected to support the North American Aerospace Defense Command and Northern Command Pathfinder program to deliver a real-time, scalable database to analyze entities across space and time,” Amit Vij, president and cofounder at Kinetica, told me. “The ability to fuse, analyze, and act across many different massive data streams in real-time has helped NORAD and USNORTHCOM enhance situational awareness and model possible outcomes while accessing risks.”

The platform allows data scientists and other stakeholders to reduce the technology footprint and consolidate information to increase operational efficiency.

“Military operators can deepen their data analysis capabilities and increase their situational awareness across North America by combining functions currently performed by multiple isolated systems into a unified cloud database producing intelligence for leadership to act on in real-time,” Vij said. “Kinetica quickly ingests and correlates sensor data from airborne objects, builds feature-rich entities, and deepens the analysis capabilities of military operators. Teams of data scientists can then bring in their machine learning models for entity classification and anomaly detection.”

Parallel (data) universe

Vectorization technology is relatively new in data science and analysis and shows promise for specific applications. Vectorization is different from other data processing methodologies.

“Vectorization, or data-level parallelism, accelerates analytics exponentially by performing the same operation on different sets of data at once, for maximum performance and efficiency,” Nima Negahban, CEO and cofounder at Kinetica, told me. “Previous generation task-level parallelism can’t keep pace with the intense speed requirements to process IoT and machine data because it is limited to performing multiple tasks at one time.” 

The way we have dealt with these problems is unsustainable from a cost standpoint and other factors such as energy use.

“Prior generation big data analytics platforms seek to overcome these inefficiencies by throwing more cloud hardware at the problem, which still comes up short on performance and at a much higher cost,” Negahban said. “In an almost industry-agnostic revelation, companies can implement this style anywhere their data requires the same simple operation to be performed on multiple elements in a data set.”

How does that apply to the Pathfinder program and its objectives?

“For the Pathfinder program, vectorization enables better analysis and tracking of objects throughout the air, sea, and land through a multitude of fused sensor readings much faster and with less processor power,” Negahban said. “The technology’s speed and ability to identify the rate of change/direction attributes algorithms that can disguise planes, missiles and potentially help the government better understand what these UAPs or UFOs really are. This means that NORAD can understand what they see in the sky much faster than before, and with much less cost to the taxpayer!”

Vectorization technology is known for its high-speed results, and recent investments in the supporting infrastructure from some of the world’s most significant hardware manufacturers have helped advance the field.

“Every five to 10 years, an engineering breakthrough emerges that disrupts database software for the better,” Negahban said. “The last few years have seen the rise of new technologies like CUDA from Nvidia and advanced vector extensions from Intel that have dramatically shifted our ability to apply vectorization to data operations.”

Negahban likens the process, and the resulting speed vectorization achieves, to a symphony. 

“You can think of vector processing like an orchestra,” Negahban said. “The control unit is the conductor, and the instructions are a musical score. The processors are the violins and cellos. Each vector has only one control unit plus dozens of small processors. Each small processor receives the same instruction from the control unit. Each processor operates on a different section of memory. Hence, every processor has its own vector pointer. Vector instructions include mathematics, comparisons, data conversions, and bit functions. In this way, vector processing exploits the relational database model of rows and columns. This also means columnar tables fit well into vector processing.”

Data has the answer

We can’t have an article about UFOs and UAPs without talking about the sizeable grey lifeform in the room. I’ve been fascinated by the subject of flying objects and aliens since I was a child, but if I were an X-Files character, I’d be the ever-cynical Scully. So here’s one of my many hypotheses.

Throughout the 1980s and into the 90s, newspapers regularly featured “martian invaders” and other alien visitors, with front-page blurry photos and tabloid headlines. Caught mainly on 35mm cameras and basic video cameras, the images of cigar and saucer-shaped objects in the sky would always be blurry and debunked a few weeks later.

There are 3.6 billion smartphone users today. The majority of these devices have incredibly high-quality cameras. Not only that, but taking photos, capturing Instagram Stories, and recording TikTok videos is now so ubiquitous, the smartphone has become an extension of our arms.

Yet, we do not see countless videos or photos of UFOs and UAPs anymore. Sightings are rare compared to when there were significantly fewer cameras in use at any given time and when we used them with specific intention instead of part of our daily lives. So just how likely is it that any of these sightings are alien in origin versus human-made objects and natural phenomena? I couldn’t resist posing this to Kinetica.

“What we know from government-issued statements is that no conclusions have been drawn at this time,” Vij said. “The June 25th preliminary assessment of UAPs by Director of National Intelligence calls for an effort to ‘standardize the reporting, consolidate the data, and deepen the analysis that will allow for a more sophisticated analysis of UAP that is likely to deepen our understanding.'” 

If we are going to find an answer, it will be data-driven and not opinion-based, that’s for sure. 

“What’s interesting is that much of the data from radar, satellites, and military footage has been around for decades, but it was previously an intractable problem to fuse and analyze that volume and type of data until recently,” Vij said. “The answer to this question now feels within reach.”  

Vectorization technology certainly offers the performance and flexibility needed to help find the answers we all seek. How can the data science community take advantage?

“What has recently changed is that the vectorized hardware is now available in the cloud, making it more of a commodity,” Negahban said. This has allowed us to offer Kinetica as-a-service, reducing the traditional friction associated with what was traditionally viewed as exotic hardware, requiring specialized and scarce resources to utilize. Our goal is to take vectorization from extreme to mainstream, so we’ll continue to make it easier for developers to take advantage of this new paradigm.”

The truth is out there, and it’s being processed in parallel.

The bulk of investments made in data management platforms thus far has not been money well spent, according to a Data Value Scorecard published today by data lake platform Dremio.

The scorecard finds only 22% of the data leaders surveyed said they have fully realized a return on investment (ROI) in data management in the past two years.

Potentially more troubling, over half of respondents (56%) admitted that when it comes to data management they have no real way of consistently measuring ROI. The scorecard is based on a survey of 500 data and analytics leaders at enterprise IT organizations in the U.S., U.K., Germany, Denmark, Sweden, Norway, Australia, Hong Kong, and Singapore.

Data access

The survey, conducted in collaboration with Wakefield Research, notes that more than three-quarters of respondents (76%) are currently locked into at least one closed system. Those proprietary platforms also make it difficult for analysts to access all the potentially relevant data they need in a timely manner, Dremio CEO Billy Bosworth noted.

Eighty-four percent of data leaders surveyed said it’s normal for data analysts at their company to work with a partial dataset. Only 16% said they expect the data management platform they employ to make fresh data available in a matter of hours or minutes. More than half (51%) said it takes their organization weeks to update data stored in their current platform. This issue is especially problematic because most digital business processes need to occur in near real time, Bosworth added.

A total of 79% of respondents also noted they have concerns about the level of scale that can be achieved using their current platforms.

Data management issues

Finally, the survey makes it clear organizations are struggling with data management. Survey respondents on average said they make 12 copies of their data to ensure it is available for all users. A total of 60% report their company has more than 10 copies of such data. A full 82% said their end users have used inconsistent versions of the same dataset at the same time due to cumbersome extract transform and load (ETL) processes that are required to move data into a data management platform.

Overall, the scorecard suggests only about 20% of organizations are successfully managing their data, with 28% of respondents claiming it is “very easy” for end users to access data and develop insights. Only 20% said timelines for ETL projects are “rarely or never” underestimated, while an equal percentage said their company has “little to no” restrictions on data access for governance.

The fact that many organizations manage data poorly is one of the dirty little secrets enterprise IT leaders don’t like to acknowledge. Most data is created within the context of an application used in a line of business. The data created by each of those applications is often conflicting and inconsistent. That issue is now coming to a head because digital business transformation initiatives that rely on analytics and artificial intelligence (AI) need access to reliable data to accurately automate a process.

Cleaning up that mess creates an opportunity for centralized IT teams to become more relevant. No line of business unit is able to aggregate all the data required to drive a digital process on their own, Bosworth noted. “Most organizations have come to that realization,” he said.

Data storage

Dremio is making a case for replacing data warehouses running on-premises or in the cloud with a data lake that leverages inexpensive cloud storage to make petabytes of data available via SQL queries. Bosworth argues that as more data is stored in the cloud, IT organizations need to manage data completely independent of both the applications employed to create it and the infrastructure used to store it.

Achieving that goal becomes easier when data is stored, for example, in an open cloud storage service that enables IT organizations to take advantage of a centrally managed data lake platform to identify and manage data in a more consistent manner, Bosworth said.

Employing data lakes as an alternative to data warehouses is not a new idea. Many organizations have attempted to build data lakes based on open-source Hadoop platforms. But those efforts have often resulted in the creation of data swamps, simply because organizations lacked the tools and processes to effectively manage terabytes of data. As a result, many organizations today are often reluctant to launch another data lake initiative.

In terms of overall data management maturity, no two enterprise IT organizations are alike. However, it’s becoming increasingly apparent that the ability of any organization to compete in a world dependent on digital processes will come down to how well they manage the data that drives those processes.

This article was originally published at VentureBeat and is reproduced with permission.

No industry is immune from the impact of technological, social, or economic disruption. Today, Chief Data Officers are playing a crucial role in navigating the complexities of the ever-changing world and engineering response to the changing demands of markets. 

So, what’s next for the Chief Data Officer role? How can CDOs navigate disruption, bring business value to their organizations, and, ultimately, get an invitation to the board of directors? 

In this article, you will learn:

1 – A brief overview of the Chief Data Officer role

2 – The current state of play & challenges for Chief Data Officers

3 – How to extract the true value of data & use cases

4 – What the future holds for CDOs

What are Chief Data Officers?

Chief Data Officer is in charge of developing and implementing how the organization acquires, manages, analyzes, and governs data. Chief Data Officers are responsible for putting data on the business agenda instead of treating data as a by-product of running a business.

Until the 1980s, the role of the data manager was far from being a senior position. The Chief Data Officer first appeared in the early 2000s. One of the first appointed Chief Data Officers was Cathryne Clay Doss of Capital One in 2002. Five years later, Usama Fayyad took the CDO role at Yahoo!. Today, Chief Data Officers are the driving force that leverages data to drive business outcomes.

Even though businesses recognize the importance of data leadership, data value is still a vague concept for many. Therefore, there’s a lack of meaningful metrics to measure the effectiveness of the Chief Data Officer role. 

Not only are CDOs expected to provide a 360-degree view of the company data that is usually scattered across multiple silos, but they are also expected to use data for the transformation of business models and, ultimately, increase revenues. 

When the inevitable business disruption occurs, CDOs are expected to drive the transformation, put in place the right tools and strategies for innovation and execution.

The two opposing trends

Last year, The World Economic Forum found that 84% of business leaders are currently “accelerating the digitization of work processes” and automating tasks. For many Chief Data Officers, the pandemic is the first significant disruption and a growth catalyst that is yet to be embraced and leveraged. 

Martin Guther (SAP’s VP, Platform&Technologies CoE MEE) says that the pandemic acted as an accelerator that enforces current trends rather than a disruptor by itself. He sees the current trends for CDOs coming from two very different perspectives – legislation and innovation. 

“CDOs need to look into how the data is taken care of. This is actually something that has been mandated by law for quite some time, and now it’s enforced – companies need to put a lot of work into how they treat the data that customers give them,” says Guther. 

On July 13th, we will be discussing the future of the CDO role together with the experts from SAP, Lufthansa Industry Solutions, HelloFresh & idealo. Apply to join our free CDO Club webinar.

The other trend is innovation – newer techniques to learn from mass data are booming. The usage of breakthrough technologies like AI and machine learning opens up the possibilities to extract insights from previously unavailable data.

The clash of legislative restriction and the increased technical capability for innovation challenges Chief Data Officers to keep a balance between compliance and drive innovation while reinforcing customer trust. In a data-driven world, a growing amount of business data contains personal or sensitive information. If applications use this data for statistical analysis, it must be protected to ensure privacy.

Extracting the value of data

We’ve all heard the saying circulating in the business world for the last decade – “data is the new oil.” But is it possible to measure the value of data to back up this statement? And if it is, then why do Chief Data Officers are struggling to measure their success?

According to SAP’s Martin Guther, data is an intangible asset that is often not valued with accounting standards. 

“Many companies know more about the value of office furniture than about the value of their data. Data is not represented anywhere in the company’s balance sheet – that presents a big challenge for CDOs as they need to find ways to prove the value of their work.”

So how can CDOs measure the value of data? 

“There are three quantifiable elements that drive the data value – incremental revenue, cost reduction, and risk mitigation,” says Guther. Simply collecting more data does not necessarily create more value. 

To extract the data value, CDOs need to look at a combination of three factors: data volume, data quality & data use. Chief Data Officers must actively manage each aspect. 

“All three drivers need to come together as they are multiplied by each other. If one element is out of the equation, the value won’t be extracted.” For example, the data volume and quality are top on the technical side, but the findings are not applied across departments on the organizational side. Ensuring the formula is used correctly on both technical and organizational levels is probably the most complex challenge for a CDO. 

How data helped Saturday Night Live show gain more viewership

For example, the media and entertainment industry quickly realized that extracting the data value from their content is key to long-term growth. In 2015, Michael Martin (SVP Product, Technology and Operations at NBC Entertainment Digital) encountered a challenge – even though much of the SNL show’s library was online, the audience still wasn’t able to access the content they liked. As a result, only recent shows got the most viewership. 

To let fans discover SNL’s content in full, Martin’s team realized they need to address data to drive the viewer’s experience. SNL library wasn’t getting enough visibility because of a mismatch between the content and the metadata. The most reliable data consisted of dates, titles, and characters. The problem was that this data didn’t consider that titles were often vague to conceal a joke, fans didn’t know when the shows were aired, and character names were not always known.

Martin’s team used a mixture of metadata and semantics to model data and capture every character, cast member, season, impersonation, sketch, and each item’s characteristics. This reformulated approach to data mapped the library of the SNL show in much more detail and allowed the fans to discover and access their favorite content. 

Why letting data guide the way is critical

As for recent examples, both the freight and aviation industries experienced accelerated disruption due to the pandemic. With the worldwide approvals of the Covid-19 vaccine, the global distribution of vaccines in extremely demanding circumstances was a challenge for Lufthansa Industry Solutions. 

Susan Wegner, VP Artificial Intelligence & Data Analytics at Lufthansa Industry Solutions, is convinced that following the principle of “letting data guide the way” was crucial for adapting to novel and demanding circumstances. 

The overall network was severely affected by the pandemic, with borders closing and planes being grounded, since a considerable share of the cargo volume is usually transported in the bellies of passenger airplanes. 

“Besides transforming some of the passenger airplanes into cargo freighters, we have algorithms,  which optimize the production planning. With our freight forecast AI we had a clear competitive advantage and the ability to adapt quickly, because we knew we had the algorithms on our side”, says Wegner. 

AI algorithms Lufthansa Industry Solutions deploys were initially designed to learn and retrain themselves continuously. This was particularly beneficial since an unusual event like a pandemic does not paralyze the AI algorithms as they are not solely based on historical data. Thus, within a very short time of taking the pandemic into account, these algorithms adjusted their forecasts and calculations accordingly. 

Another critical element for successful adaptation was the way data and AI leaders managed their teams. AI and data teams worked closely together with the process and business departments. 

“With these cross-functional and diverse teams, we make sure that decisions in the process and business departments are based on the optimal amalgamation of data and experience. This proved a valid approach as we all knew quite early that not many of us have a lot of experience with pandemics, and data never lies”, says Wegner. 

While some departments analyzed data for decision-making purposes on demand, others utilized AI to automate processes and tasks, not only in terms of speed but process efficiency and costs, since the latter one is also quite important.

“From a leadership position, Covid changed a lot,” says Wegner. She took the role at the beginning of Covid-19 and could not meet the whole team in person. It was a huge challenge to build up relationships and trust, encourage and motivate the team virtually. 

“I was very lucky since the team is basically digital native and had great ideas on how to connect virtually. But personal meetings count, and therefore I tried to arrange additional one-to-ones always when it was safely possible.” 

From Decentralized Data Teams to Data Evangelism

Mina Saidze, Data Evangelist at idealo (a German price comparison service), shared how her company approached generating the most value from data. “The advantage of having a decentralized data team structure is that each unit can specialize in a certain domain. However, the pitfall is that silos can evolve over time and, hence, the lack of communication and collaboration hinders innovation”, says Saidze.

The company decided to combine the best of two worlds – a hybrid approach between decentralized domain specialization and inspiring an understanding of how important data is for an organization as a whole. 

The Data Leadership Team and the CTO approved the centralized Centre of Excellence and the Data Evangelist role to help strengthen the collaboration among tech and business units within the organization. 

Mina’s role is developing best practices, identifying relevant use cases, and building up an Analytics Community to tackle the current challenges.

What’s next for the CDO role? 

Even though organizations have woken up to the fact that data deserves a place on the board, the CDO role is not yet defined. Gartner estimates that by 2025, 90% of large organizations will have a CDO. So what does the future hold for the Chief Data Officer role?

“If a CDO wants to go into the direction of being a senior business leader, being a part of the board of directors, the data assets that she or he is managing and the value must be clear,” says Martin Guther.

Chief Data Officer is one of the most critical roles right now as it brings together innovation, compliance, technical & business perspectives. There’s a lot of risk yet a tremendous opportunity if you get it right.

According to Mina Saidze, a Chief Data Officer role should not be seen as encroachment by CIOs and CTOs. In fact, a CDO is a person that underpins the company strategy with data and ensures the data quality and generates value out of this asset.

“A future trend I observe is that CDOs do not treat data as a liability but as an opportunity. Until recently, many CDOs were focused on limiting the downsides of data, such as GDPR policies or ensuring data quality. Soon, we will need CDOs who have a vision and the know-how to generate new revenue streams for the company by developing data-driven products, services, and processes. An entrepreneurial mindset, paired with a data background can help the CDO of the future to succeed in this role”, said Saidze.

As Martin Guther states, “CDOs should ask themselves what they truly want to achieve. Do they want to be innovative thinkers and risk their ideas being too progressive to be applied in the organization? Do they want to be a technical master working behind the scenes? Or do they want to become a transformational leader acting across all these domains of expertise? I think that there’s a great opportunity to grow and take their place at the board of directors.”

You can meet the experts featured in this article on July 13th at 6 PM CET at the SAP & Data Natives CDO Club event. Apply to participate by filling out the form below:

Like oil or money, data is a valuable resource that holds great potential. But too many organizations set their sights on amassing data, or worse, using it only in isolated processes or departments when they should be focused on applying data throughout their everyday operations.  

Technology is, of course, part of the puzzle, but in an authentic data culture, execs have the ability to spark innovation and identify new business opportunities via more efficient processes. Lasting success relies on smarter analytics-infused processes and the merging of culture and people. It’s a bold move that requires commitment from the top – fueling strategic decision-making by all stakeholders, no matter their title or department. 

A new perspective 

Organizations that take full advantage of data and analytics hold this strategic mindset. They see, use, and perfect data to gain a competitive edge. In a December 2020 Harvard Business Review (HBR) survey, close to 90 percent of respondents felt analyzed data was critical to their company’s business innovation strategy. Yet, while data analytics was cited as enhancing the customer experience (CX) and operational efficiency, participants said it was not routinely applied to fuel innovation or new business opportunities. So, what’s the holdup? It is far easier to acquire data than it is to convert it into usable insights. In addition, moving to a data-led business culture is challenging. It can seem risky, for example, if those in charge lack confidence that the right policies, ethics, and governance are in place for ideal enterprise-wide sharing of data. But leaders in a healthy data culture know better and accept the risk, doing what is necessary to make the data useful. 

Start at the top 

The infusion of analytics throughout an organization requires strategic planning, thoughtful execution, and unwavering commitment from executive leadership for real and lasting impact – a big task for any company. 

Leading a data-centric organization doesn’t necessarily require data science degrees across the C-suite, although it is imperative that those in charge have a working knowledge of standard data principles. Armed with an understanding of the insights desired, an appreciation for clean data, and the ability to identify data gaps, leaders are better equipped to revamp the decision-making process. Leading by example, executives have an opportunity to educate their workforce on the importance of data literacy ahead of infusing analytics into existing workflows. 

Within the comfort zone  

Respondents in the HBR survey voiced concerns around poor training and lack of employee skills as impediments to broad use of data. They highlighted a lack of quality data as well. But in a data-driven culture, routine applications, workflows, and processes are infused with analytics, making training unnecessary or minimal at best. Multiple steps can more easily be automated for a frictionless user experience. Analytics capabilities are woven into actively-used tools, putting insight and actionable intelligence within reach – it’s there when and where it’s needed, enabling informed, real-time decisions in context.  

Consider that devices such as smartphones, ‘fitness’ watches, and immersive applications and websites have us craving instant gratification and personalization. It is no different in the workplace. Employees want relevant, up-to-the-minute insights delivered when and where they need them, in context, and without having to learn how to use new software. Alerts to changing conditions or issues are a welcome bonus that extend the utility of infused analytics.  

Weaving analytics into current technology 

It’s important to note that an organization’s technology choices can reduce data visibility. In contrast, analytics infusion presents data and actionable intelligence to the people who need it, when they need it, in the workflows, they are accustomed to.  

How data will be used simply must influence technology choices. This presents an ongoing opportunity to reinforce the adoption of data strategies throughout the organization – and even outside the organization, with partners, suppliers, and customers. With consensus at the C-level, goals are defined, assessed, and modified as needed. While democratizing data is not without risk, it defines leadership that recognizes the intrinsic value of a data-centric organization. 

The analytics-infused enterprise 

Data can be a blessing and a curse, especially without the infrastructure and processes in place to tap into that data’s inherent value – compounded by the constant stream of data being gathered and added to your existing arsenal. Data that is dirty, inaccessible across systems, or shared with only a handful of stakeholders only exacerbates the matter. Fortunately for most organizations, leveraging data to its full extent can be accomplished with an adjustment in company mindset. Enabling data access – throughout systems, processes, and people – provides the foundation to an analytics-infused environment. It’s a new landscape where smart decision-making happens at the point of need. For company leaders, it is a bold step toward fostering a data-driven culture, empowered and motivated for success across the organization. 

Real-time data is immediately passed along for analysis rather than collected and stored. Some such systems offer on-demand real-time analytics. In those cases, the platform does not send data until a person requests it. It’s immediately available once they do. 

Other real-time data tools have continuous analytics capabilities. Then, the system provides an ongoing information stream, dictated by the parameters a user sets.

Here are six ways it can help companies in ways other methods can’t. 

1. Tightened Cybersecurity

Cyberattacks can be debilitating for the targeted victims, and the amount of information stolen in these events is typically vast. In an April 2021 attack on the Washington, D.C., police department, hackers got a whopping 250 gigabytes of data and threatened to leak it if they did not receive their ransom demands. That was also the 26th government cyberattack in 2021 so far. 

Applying real-time data to cybersecurity makes teams more aware of cyberattacks before they happen. That knowledge allows them to respond to prevent incoming attacks rather than reacting after the fact. 

An Israeli company called Deep Instinct has a real-time data solution that shows what’s possible. It can spot anomalies in 10 milliseconds after learning to detect them. 

2. Enhanced Medical Care

Medical practitioners often refer to data from a point in time, such as after taking a patient’s blood pressure or looking at the results of recent lab tests. However, there’s an ongoing trend of referring to real-time data, too. 

Some hospitals use it to identify patient deterioration, enabling care teams to act before the worst happens. Real-time data gives medical professionals a continuous picture of a patient’s status, allowing them to make better decisions about treatments, discharge, and end-of-life care.

Dozee is a real-time patient monitoring brand from Indian health tech startup Turtle Shell Technologies. A sensor placed under a patient’s mattress gives statistics about heart rate, breathing, and other vital characteristics. In one hospital project involving more than 4,000 patients, Dozee gave data that allowed for hundreds of transfers from intensive care units to other departments or recommended changes in oxygen therapy. The system provides an accuracy rate of more than 98%. Dozee’s business model also allows patients to rent sensors for home use. They can then automatically transfer the statistics to physicians. 

3. Improved Fleet Management

Many people don’t immediately realize how crucial the logistics sector is for facilitating daily needs. For example, truck drivers play a vital role in ensuring grocery stores have shelves full of fresh milk first thing in the morning. 

Also, the global e-commerce market totaled $4.28 trillion in 2020, with more growth projected in future years. Excellent fleet management ensures all those deliveries reach their destinations on time. Incoming information allows managers to make changes as new situations arise. 

Business owners and fleet managers often use telematics software to get real-time updates about driving behavior, truck location, and vehicle performance. Some solutions allow users to receive updates that meet specific parameters. That option means they get the most relevant data without unnecessary or unwanted details. 

4. Personalized Shopping Experiences

Successful retailers increasingly aim to provide universally seamless shopping for consumers, regardless of whether they buy things online or in person. Real-time data helps retail personnel personalize the shopping experience. 

For example, a sales floor worker might have a tablet that gives real-time data to show that a person they’re helping looked for a particular item online before coming into the store. That knowledge helps them make better recommendations, identify which items the shopper may respond to best, and promote upsell opportunities. 

5. Reduced Manufacturing Challenges

An unplanned manufacturing disruption can cost tens of thousands of dollars and lead to dozens of upset clients. That’s why company leaders often rely on real-time data to minimize such incidents and become more informed about all aspects of factory operations. 

For example, real-time data can show which machines use the most energy or identify process bottlenecks. One solution used in additive manufacturing lets users monitor machine performance remotely, helping them make production decisions. Another perk is that it gives better inventory control. Managers can see how many supplies they have, prompting reordering decisions. 

6. Increased Accuracy for Car Insurance Rates

Most people have experienced the frustrating situation of paying high rates for car insurance despite being exceptionally safe drivers and having few or no claims. The traditional method of setting premium rates takes driving history into account. However, it also relies on other characteristics, such as a person’s age, gender, and sometimes even marital status. 

That system often makes many drivers pay more than they should. Relying on real-time data about motorists’ behavior is a newer option that insurance companies could use instead. It results in usage-based insurance (UBI). A study found that real-time data made people generally safer drivers, too. In one area, they reduced their hard braking events by 21% after six months. 

Real-Time Data Facilitates Responsiveness

The modern world is full of data, but not all of it gets collected in real-time. These six examples highlight why people in more industries realize real-time data is the way forward. 

Information that’s five minutes old can become largely useless, particularly in emergencies. Even in less-urgent situations, decision-makers benefit from having the most up-to-date content available. That allows them to make the smartest choices based on what circumstances demand.

Collaboration is a key driver of success in business and one of the hardest things to achieve in today’s work climate. Businesses are juggling remote workers, hybrid workers, and everything in between since the pandemic’s onset. Business intelligence can be crucial in creating collaboration.

According to the U.S. Census and Bureau of Labor Statistics, remote working between 2005 and 2017 went up by 159%; however, despite this, FlexJobs noted that only 3.4% of the workforce worked from home. Of course, this was all before COVID-19 hit businesses hard, forcing most non-essential workers to work remotely. And perhaps to everyone’s advantage, this trend doesn’t seem to be going anywhere too fast. A recent survey demonstrated that 82% of respondents enjoy working from home, and 66% felt they were more productive working remotely. It seems that the future of work has changed dramatically this year, forcing businesses to put various solutions in place to support the changes that COVID-19 brought about.

According to Zapier CEO Wade Foster, who has hundreds of employees working remotely,  “companies who don’t have effective systems in place are winging it in a lot of areas right now. They’re going to have a hard time with this sudden transition. They are being thrust into an environment where they have no structure.” He told Computerworld that the “wrong type of management, misaligned culture, and lack of essential tools” could create a negative remote working environment.

Business intelligence can be the catalyst

One of the “essential tools” to create a successful, collaborative work environment is having an automated centralized Business Intelligence platform, cloud-based, that is utilized across the organization’s BI landscape. Today, businesses are dependent on data to make important decisions. Gone are the days where only business analysts were accessing data; today, the entire organization or anyone who needs to make a decision – whether big or small – needs to have access to the same data to collaborate with team members immediately. The data exists in silos and needs one centralized solution that will allow visibility to the entire landscape. A cloud-based, centralized Business Intelligence platform provides the internal framework where everyone has access to the same semantically defined, usable and trustworthy data right off the bat.

This has been particularly useful when collaboration is required to solve a business problem for a large organization. So much time was wasted in the past, and projects were stalled by the “black hole” of manually discovering, organizing, preparing, and cleaning data for analysis. Sometimes this manual work would take months. The automation of metadata lets people get on with collaboration by accessing the same data across the entire company. A person working in California will have the same data that a remote worker in Australia will have, and if they are on the same team, they are looking at the same information and able to collaborate on how to proceed to the next step.

It is now more crucial than ever to have a centralized, automated business intelligence platform. As workers become more scattered across the world, data is becoming more centralized, creating a collaborative environment within the remote workspace. The ability to trace the origin of metadata with a view of the entire data lineage, and with data discovery trace data immediately, will significantly impact the speed in how decisions can be made by providing the relevant information to make such decisions immediately, creating the internal framework of a collaborative workforce.

Who knew that COVID-19 would change the way businesses operate forever – making us more remote yet cohesive and collaborative since the nuts and bolts of the company are being forced to centralize? Companies worldwide are unifying through centralized data; having the right information, no matter where you are.

Augmented analytics: the combination of AI and analytics is the latest innovation in data analytics. For organizations, data analysis has evolved from hiring “unicorn” data scientists – to having smart applications that provide actionable insights for decision-making in just a few clicks, thanks to AI. 

Augmenting by definition means making something greater in strength or value. Augmented analytics, also known as AI-driven analytics, helps in identifying hidden patterns in large data sets and uncovers trends and actionable insights. It leverages technologies such as Analytics, Machine Learning, and Natural Language Generation to automate data management processes and assist with the hard parts of analytics. 

According to Gartner, by the end of 2024, 75% of enterprises will operationalize AI, driving a 5x increase in streaming data and analytics infrastructures. The capabilities of AI are poised to augment analytics activities and enable companies to internalize data-driven decision-making while enabling everyone in the organization to easily deal with data. This means AI helps in democratizing data across the enterprise and saves data analysts, data scientists, engineers, and other data professionals from spending time on repetitive manual processes.

How does AI improve Analytics?

The latest advances in Artificial Intelligence play a significant role in making business processes more efficient and powerful with the help of automation. Analytics, too, is becoming more accessible and automated because of AI. Here are a few ways in which AI is contributing to analytics:

• With the help of machine learning algorithms, AI systems can automatically analyze data and uncover hidden trends, patterns, and insights that can be used by employees to make better-informed decisions. 
• AI automates report generation and makes data easy-to-understand by using Natural Language Generation.
• Using Natural Language Query (NLQ), AI enables everyone in the organization to intuitively find answers and extract insights from data, thereby improving data literacy and freeing time for data scientists.
• AI helps in streamlining BI by automating data analytics and delivering insights and value faster.

So, how does it work?

While traditional BI used rule-based programs to deliver static analytics reports from data, augmented analytics leverages AI techniques such as Machine Learning and Natural Language Generation to automate data analysis and visualization. 

• Machine Learning learns from data and identifies trends, patterns, and relationships between data points. It can use past instances and experiences to adapt to changes and improvise on the data. 
• Natural Language Generation uses language to convert the findings from machine learning data into easy-to-decipher insights. Machine Learning derives all the insights, and NLG converts those insights into a human-readable format.

Augmented analytics can also take in queries from users and generate answers in the form of visuals and text. This entire process is of generating insights from data is automated and makes it easy for non-technical users to easily interpret data and identify insights.

Augmented Analytics for Enterprises

Business Intelligence can help in making improved business decisions and driving better ROI by gathering and processing data. A good BI tool collects important data from internal and external sources and provides actionable insights out of it. Augmented analytics simply improves business intelligence and helps enterprises in the following ways:

1. Accelerates data preparation

Data analysts usually spend most of their time in extracting and cleaning their data. Augmented analytics takes away all the painstaking processes that data analysts need to do by automating the ETL (extract, transform and load) data process and providing valuable data that can be useful for analysis.

2. Automates insight generation

Once the data is prepared and ready for processing, augmented analytics uses it to automatically derive insights. It uses machine learning algorithms to automate analyses and quickly generate insights, which would take days and months if done by data scientists and analysts. 

3. Allows querying of data

Augmented analytics makes it easy for users to ask questions and interact with data. With the help of NLQ and NLG, it takes in queries in the form of natural language, translates it into machine language, and then produces meaningful results and insights in the form of easy-to-understand language. This makes data analytics a two-way conversation wherein businesses can ask questions to their data and get answers in real-time.

4. Empowers everyone to use analytics products

The feature of querying data makes it possible for professionals to delve deeper into their data and also enables everyone in the organization to use analytics products. Enterprises no longer require data scientists or professionals with technical expertise to use BI tools to analyze data. This has led to an increase in the user base of BI and analytics tools.

5. Automates report generation and dissemination

With augmented analytics, insights can be generated from data at the speed of thought. These insights can further be used to automate report writing, saving a lot of manual efforts in report generation. 

Augmented Analytics in Action

Augmented Analytics can be used to solve various business problems. Some use cases and applications of it include demand forecasting, fraud, and anomaly detection, deriving customer and market insights, performance tracking, and so on. Here are a few examples:

• Banking and financial institutions use augmented analytics to generate personalized portfolio analysis reports.
• Retail and FMCG companies use intelligence powered by augmented analytics to track market insights and make informed decisions.
• Companies in the financial services sector use recommendations and insights mined by augmented analytics to detect and prevent fraud or anomalies.
• Media and entertainment companies use insights generated from augmented analytics to provide tailored content to their users.
• Marketing and sales functions across businesses use augmented analytics to extract data from external and internal sources and gain insights into sales, customer trends, and product performance.

 Wrapping Up

The complexity and scale of data being produced and used by businesses across sectors are more than humans alone can handle. Enterprises have started adopting the new AI wave in analytics to tackle data and improve their processes. Augmented analytics is the disruptor, and leveraging it with BI platforms can help businesses to analyze data faster, optimize their operations and make data teams more productive.

Companies are collecting more data than ever these days. Business intelligence processes, in turn, have become a necessity in every business function, including the supply chain. 

BI tools can get complicated in a hurry. Given the sea of data that supply chain companies find themselves swimming in, choosing the right solution can become tricky. It makes sense to focus on a few BI functions that cut to the heart of supply chain analytics. 

“It’s worth noting that not every business intelligence software on the market will integrate with specific databases,” writes digital business icon Neil Patel in his recent guide to BI. Different platforms have different specialties, and not all of them can handle the IoT signal firehose that supply chain monitor networks are known to emit. 

Assuming that your software and network infrastructure are optimized for supply chain use cases, here are a few key ways that you can use BI to derive insights that make a difference.

ROLE-BASED MODELING

Supply chains are complicated to deal with. A lot of this complexity arises from the different roles that are involved in the process. 

From a transportation perspective, roles such as the VP of logistics or transportation compliance might be involved. Outside of transportation, roles such as operations planning executives, sales executives, and health and safety executives also exist. All of these roles have very different objectives, and as a result, smart data modeling is critical to empowering them with the information they need.

The VP role may focus on adherence to the transportation budget, requiring a high-level view of processes. A transportation executive needs granular information on route options and scenario modeling based on a variety of factors. The roles outside transportation need dashboards derived from a combination of data sets from across the organization.

Savvy BI deployment offers companies the ability to model scenarios across all of these roles and project data as needed. Scenario modeling based on roles helps companies reduce risk across the supply chain and plan their processes more efficiently.

DATA SOURCE INTEGRATION

Supply chains involve many different processes and, as a result, disparate data sources. For example, an operations analyst might be more concerned with machine sensor data than with the condition monitoring data of their shipments. 

Today’s enterprise-grade BI platforms can integrate all of these data into a single environment and make them available for use in reports. Integration is a huge deal in supply chain analytics since supply chain data affects so many aspects of organization-level planning. It’s something that supply chain solution providers have been focusing on.

“The basic use of integrating our service to customer ERP (enterprise resource planning) systems is quite exciting already, in my opinion,” Janne Juhala, co-founder and CEO of condition monitoring startup Logmore, said in one recent interview. “In its simplicity, it makes reporting and analysis so much more efficient for companies that have strong processes established within their own systems.”

By integrating disparate data sources, processes from all parts of the organization come together. By combining different data points, executives can derive insight into cross-dependent processes and build efficiency in their organization.

ROOT CAUSE ANALYTICS

Supply chain managers deal with a staggering array of issues daily, which have different implications depending on the manager’s purview. 

For example, a transportation department manager might be dealing with a significant number of load tenders being rejected by carriers. A sales and marketing manager might be analyzing data to figure out why production can’t keep pace with a recently announced sales promotion.

Drilling down to the root causes of these issues is critical for business success. BI software allows executives to create visually rich dashboards that expose the root causes of failure. The transportation manager can create a rate curve analysis to figure out why tenders are being rejected. The sales and marketing executive can access production analytics to better plan sales campaigns.

Drilling into root causes also exposes the costs associated with each transportation option. This helps companies reduce costs and increase their bottom line.

EMBEDDED ANALYTICS

Supply chain companies often face challenges concerning the adoption of new behavior. Employees might remain rooted in outmoded ways of executing their tasks and will find it difficult to switch to a standalone analytics package. 

A key step to achieving greater analytics adoption is to embed them into existing applications. Thanks to embedding analytics, employees don’t need to change their workflows and can access insights within their existing applications. Even giving your team the advantage of minimizing the friction involved with unnecessarily switching between app interfaces can make a big difference. 

By giving her last-mile logistics account reps the ability to receive alerts regarding data anomalies in Slack, Deliveroo executive Marion De Najar says that “The team can focus their attention on what really matters, rather than being overwhelmed with a lot of data points and not really knowing where to look.” This is the power of embedded BI services. “They’ve gained a lot of efficiencies because they know where to focus their efforts,” she adds.

The applications for supply chain professionals are virtually limitless. For example, a shipping manager can query a summary of carrier performance before placing an order with a given vendor. As far as the manager is concerned, the summary is a part of their system. The BI package has crunched data in the background and has delivered insight in an easy to understand rating format.

LANDED COST AND PROCUREMENT ANALYSIS

Companies need insight into profitability by customer and product. Logistics components are an important factor to take into account when calculating cost. The problem is that these costs often include unplanned accessorial costs. 

Companies that don’t use BI systems overcome this issue by using costs projected on their tenders. However, these numbers are far from accurate. A good BI system can pull numbers from finalized freight audit data and therefore incorporates accessorial costs. 

Supplier relationships are a huge part of reducing costs, and procurement analytics help companies in this regard. Vendor performance can be measured accurately, and companies might discover that certain vendors perform better when tasked with sourcing or dispatching specific products.

POWERFUL BI FOR BETTER INSIGHTS

Thanks to the wealth of data that companies generate, they can gain insight into every aspect of their supply chains, reduce costs, increase efficiency, and make strategic changes to operations. Smart supply chains need smart, data-driven solutions.

Talent recruitment without data is drought with challenges. Hiring managers may find themselves at a disadvantage when determining how much they need to pay their employees. Offer them too little, and they’re unlikely to work for you. Pay them too much, and you’re not utilizing your resources effectively.

Compensation management and salary benchmarking are, therefore, critical parts of the hiring and employee retention process.

Paying close attention to your payroll analytics helps to solve any issues that might arise from these processes. It’s also possible to automate many of the decisions that managers routinely make. By having the right tech stack in place – including solutions for payroll management, data collection, and analytics – and running reports to surface the information you need when you need it, businesses run smoothly and make the right compensation-related decisions.

Here are three benefits that executives can see by including payroll considerations in their data-driven management scope.

BETTER WORKFORCE PLANNING

You can’t investigate every hiring decision manually and determine the exact figure to satisfy a new hire. Analytics packages allow you to personalize payroll decisions to your employees. As Sean Manning, founder of Payroll Vault, says, “Few aspects of business are more personal than when you pay your employees the money they have earned and by which they live, so it makes sense to have personal care on the payroll management side as well.”

Offering your employees optimal salaries needs to balance against your company’s budget. With business environments constantly changing, companies need to keep an eye on their costs. Salaries are among the most significant expenses that companies incur, accounting for anywhere from 40 to 80 percent of revenues.

Payroll analytics solutions allow you to not only benchmark salaries. They also help you model the impact that the new hire will have on your organization’s costs on multiple levels. “Because of the sensitive nature of payroll, even small mistakes can cost money in employee turnover and deteriorate trust among the workers who remain,” notes Alex Margolin of Papaya Global.

“At the same time, having access to accurate and precise BI data can help you spot areas of your business that need improvements, such as excessive overtime pay in one department when an additional worker would ultimately cost less, or a budget shortfalls in another department.”

If you’re dealing with a global workforce, it’s likewise essential to remember that you pay your employees a lot more than just their salaries, depending on where they’re based. The structure of local benefits packages plays a vital role as well. Locations that require high insurance benefit contributions might be better served by hiring contingent workers instead of full-time employees.

Alternatively, you can compare the cost of training new employees against the cost of losing confidential business data when an employee moves on. Your data might indicate that investing in a full-time team might make more sense, despite more generous benefit contributions.

Payroll data can also help you retain key employees by understanding what their values are. While satisfactory compensation leads to better work performance, the amount of money you pay an employee isn’t the only factor motivating them. For example, younger workforces might value flexible work hours more than excellent health insurance packages. An older workforce might appreciate remote work possibilities more than an open office environment.

Analytics helps you tailor compensation to your employees in better ways and build loyalty amongst your workforce.

BETTER COMPLIANCE FOR GROWING BUSINESSES

As businesses grow and retain team members overseas, compliance knowledge and internal audits become more critical, highlighting the value of a centralized database for all talent contracts and salaries. For example, in some countries, it’s illegal to email employees after work hours.

Companies based in the United States tend to have it easier than their global counterparts when it comes to labor regulations. They can terminate employment contracts for any reason other than discriminatory ones. In some countries, worker councils, labor laws, and work contracts make this situation more complicated.

For example, in The Netherlands, the period of notice for an employee is one calendar month. Still, it isn’t as simple as that – if you give notice to an employee that they’ve been terminated on any day other than the first of the month, the notice is legally considered to have been issued on the first of the following month.

Local labor laws can change quickly, as well. Because of all the global trade taking place in Saudi Arabia, authorities have instituted a complex system of “Nitaqat” laws, requiring foreign companies to employ Saudi nationals in specified percentages of their workforces. What categories of companies are bound to which Nitaqat quotas are somewhat dynamic, and the laws continue to evolve, making it harder for growing companies to plan headcounts and related expenses.

These days, payroll analytics need to go beyond merely providing great visualization tools and the ability to create ad-hoc reports. You need to use your payroll data as a single source of truth that you can reconcile with updates on regulatory considerations.

BETTER CASH MANAGEMENT AND PROJECTION

What was your global workforce spending last month, and why was it higher? What are your employer cost differences over time and by category in Spain and Italy? What does your net pay waterfall look like? Which contracts are expiring at the end of the month, and how will they impact your cash flow? How can you offset the business loss with new hires?

These are some of the critical questions that hiring managers deal with daily. Astonishingly, many companies choose to answer these questions using complicated spreadsheets backed by gut instinct on the manager’s part. With employment practices changing, trusting your instincts isn’t enough.

For example, the gig economy is growing every year, and many companies find themselves increasingly relying on freelancers. Unlike salaried employees, freelancers generally get paid on an ad-hoc basis. Even if they’re paid in advance, your company needs to project work demands in advance to ensure you’re deriving maximum benefit. Data analytics can help stakeholders identify seasonal cycles and other trends that they might overlook independently.

“Slow payments and poor communication about payment processes are one of the biggest headaches for gig-economy workers,” asserts Robert McGuire, publisher of Nation1099. “In many companies, line managers handle these relationships much the way they handle purchasing of software and supplies.”

Inaccurate cash projection is often behind these snafus, and culturally, management needs to approach gig worker commissions differently from other vendors. With the help of the right analytics workflows, you cannot only evaluate upcoming cash outflows but also spot trends that traditionally drain cash and plan to mitigate them ahead of time.

Global organizations need to take a wide variety of factors into account before hiring new employees. Aside from cultural fit, evaluating the risks that the business faces on a local basis is critical. Analytics can help you view payroll costs at multiple levels, from a business unit to a branch and even on a regional level. You can plan your workforce better and make better cash management decisions.

BETTER ANALYTICS EQUALS HAPPIER WORKFORCES

Payroll analytics can help yield insight into not only your cash flow but also what drives your employees. By staying compliant and anticipating trends through the power of data analysis, you’ll create a culture of satisfaction amongst your employees. This boosts your bottom line and allows you to separate your company from the competition.

Data comes in many shapes and forms, but two of its core structures are stacks and queues. TechTarget’s definition states the following; “In programming, a stack is a data area or buffer used for storing requests that need to be handled.”

And what’s inside that data stack? It’s not just a data warehouse. Data stacks are composed of tools that perform four essential functions; collect, store, model and report. But the stack itself and the data warehouse are the two we’ll focus on in this article since they have a high importance level.

To get the lowdown on why it is essential to focus on your data stack and warehouse, we talked with Archit Goyal, Solutions Architecture Lead at Snowplow Analytics to understand more.

What are the opportunities and challenges that arise when choosing and developing a data stack?

“Choosing a data stack will depend on multiple factors: the company’s core use cases, the size and capabilities of their data team, their budget, their data maturity, and so on,” Goyal said. “One of the key choices is choosing between packaged analytics solutions (think GA or Adobe) versus more modular components that combine to make up a data stack. The main advantage of packaged products is that they have a lot of analytics tooling ready to go right out of the box. However, the main drawback is that you sacrifice control and flexibility over your data management in favor of simplicity and ease-of-use. Picking and setting up multiple best-in-breed tools to make up the analytics stack is harder work, but will give you greater control over your data asset in the long term.”

So what is a data warehouse, and why do companies need it? For example, what’s the difference between a data warehouse and a MySQL database?

“A Data Warehouse is a centralized data repository which can be queried for business benefit,” Goyal said. “They can contain data from heterogeneous sources, such as SQL, CSV files, text files, and more. Comparatively, data warehouses are columnar databases, and MySQL is a relational database. This means that warehouses are optimized for historical analysis of data as it is easy to aggregate values across rows (e.g., count sessions over time), whereas MySQL databases are good for storing and retrieving individual entries as a transactional store in an app.”

What are some excellent examples of data warehouses? 

“The big three (currently on the scene) are Google’s BigQuery, Amazon’s Redshift, and Snowflake,” Goyal said. “These are typically used to store a company’s data in a columnar format to allow for easy analysis and reporting. When used as the source of truth for a company to answer business questions, particularly about its users, it can be extremely powerful.”

So that covers warehouses, but what is our definition of a data stack, and what should be inside a good data stack?

“At Snowplow, we think about the data stack in four different stages,” Goyal said. “First, we collect. Data quality matters. With high-quality and complete data, attribution models are accurate, it’s easy to track and understand user behavior, and customer experiences can be optimized. That’s why our customers choose Snowplow, as we provide flexibility to collect data from multiple platforms and channels, as well as delivering clean and structured data.”

“Then, we store. Snowflake, BigQuery, Redshift, and S3 are all examples of tools for storing data that is collected.”

“The third stage is to model. Data modeling can help teams democratize their data – At Snowplow, our customers use tools like Snowplow SQL Runner, dbt, and Dataform to model their data.”

“Finally, we report. At this stage, data teams want to enable self-service of analytics within their organization. This includes the use of tools such as Looker, Redash, PowerBI, and Amplitude.”

“There is no one size fits all approach,” Goyal said. “Many teams opt for the out of the box solutions mentioned earlier while, increasingly, sophisticated data teams are combining the modular components outlined above to build a robust data stack which they can control from the get-go.”

What is an excellent data stack use case?

“Snowplow customers and recruitment marketing specialists VONQ wanted to use data to attract talent and advertise jobs on behalf of their customers,” Goyal said. “To make better recommendations and provide actionable insights for recruiters, VONQ invested in a data warehouse and data model that fit their business needs. For their use case, VONQ chose to implement a Snowflake data warehouse, citing the pricing model, user management, and performance as some of the key drivers behind their decision.”

“In addition to implementing Snowflake, VONQ needed a way to serve their data as well as near real-time responses for their customers. They decided to take a small amount of their data from their data warehouse and put it in the database Postgres where they could configure indexes, for example. For this data movement, they implemented Airflow because of its functionality with batch ETLs. Once their data was in Postgres, it allowed the data team to build an Analytics Service to serve actionable data to the wider team.”

“Natalia, Data Engineer at VONQ, shared this data journey with us in a recent webinar – you can watch it on-demand here.”

Which data models are out there, and how should you navigate them to make the best choice for better business insights?

“Data modeling is an essential step in socializing event-level data around your organization and performing data analysis,” Goyal said. “In its most basic form, data modeling is a way of giving structure to raw, event-level data. This structure is essentially your business logic applied to the data you bring into your data warehouse – making it easier to query and use for your specific use cases.”

“There are many ways to model your data to make it easier to query and use, and at the end of the day, the way you’ll model it will depend on your business logic and analysis use cases. If you’re modeling your data for visualization in a BI tool, you’d want to follow the logic required by the BI tool, or do the modeling within the BI tool itself (e.g., using Looker’s LookML product).”

“For most retailers and ecommerce companies, Google and Adobe’s data model will suit their use case. These giants have built their platforms and logic for retailers — conversion and goal tracking, funnel analysis, etc. is optimized for a traditional ecommerce customer journey. That said, many businesses struggle to make Google and Adobe work for them, e.g., if you’re a two-sided marketplace with two distinct groups of buyers and sellers or a (mobile) subscription business that wants to understand retention.”

“Say you’re a recruitment marketplace, and you have job seekers and recruiters interacting with your platform (two distinct user groups with different behavior). When a job seeker is looking for a job, one search on the site might result in five job applications. This means that the traditional funnel or conversion rate would make no sense.”

“Here are some examples of data models that we see with our customers: Modeling macro events from micro-events (e.g., video views); Modeling workflows (e.g., sign-up funnels); Modeling sessions; and Modeling users.”

“Check out our guide to data modeling to learn more about each example and tips on how to turn your raw data into easy-to-consume data sets.”

What should data professionals pay attention to when developing a data stack and data warehouse?

“This question has a long answer full of ‘it depends,'” Goyal said. “However, it’s important to consider two things: data quality and transparency. Having high quality – complete and accurate – data in a granular format is often key to setting data science teams up for success. Transparency into how data is processed upstream of a data science model is important to be able to justify the output.”

Archit Goyal will be speaking at DN Unlimited Conference on November 18-20, 2020 – meet him at the Data Science track during his talk “Building a strategic data capability.”

• The DN Unlimited Conference will take place online for the first time this year
• More than 100 speakers from the fields of AI, machine learning, data science, and technology for social impact, including from The New York Times, IBM, Bayer, and Alibaba Cloud
• Fully remote networking opportunities via a virtual hub

The DN Unlimited Conference will take place online for the first time this year.

The Data Natives Conference, Europe’s biggest data science gathering, will take place virtually and invite data scientists, entrepreneurs, corporates, academia, and business innovation leaders to connect on November 18-20, 2020.

The conference’s mission is to connect data experts, inspire them, and let people become part of the equation again. With its digital networking platform, DN Unlimited expects to reach a new record high with 5000+ participants. Visitors can expect keynotes and panels from the industry experts and a unique opportunity to start on new collaborations during networking and matchmaking sessions.

In 2019, the sold-out Data Natives conference gathered over 3000 data, technology professionals and decision-makers from over 30 countries, including 29 sponsors, 45 community and media partners, and 176 speakers.The narrative of DN Unlimited Conference 2020 focuses on assisting the digital transformation of businesses, governments, and communities by offering a fresh perspective on data technologies – from empowering organizations to revamp their business models to shedding light on social inequalities and challenges like Climate Change and Healthcare accessibility.

Data science, new business models and the future of our society

In spring 2020, the Data Natives community of 80.000 data scientists mobilised to tackle the challenges brought by the pandemic – from the shortage of medical equipment to remote care – in a series of Hackcorona and EUvsVirus hackathons. Through the collaboration of governments such as the Greek Ministry for Digital Governance, institutions such as the Charité and experts from all over Europe, over 80 data-driven solutions have been developed. DN Unlimited conference will continue to facilitate similar cooperation.

The current crisis demonstrates that only through collaboration, businesses can thrive. While social isolation may be limiting traditional networking opportunities, we are more equipped than ever before to make connections online.

The ability to connect to people and information instantly is so common now. It’s just the beginning of an era of even more profound transformation. We’re living in a time of monumental change. And as the cloud becomes ambiguous, it’s literally rewriting entire industries

Gretchen O’Hara, Microsoft VP; DN Unlimited & HumanAIze Open Forum speaker.

The crisis has called for a digital realignment from both companies and institutions. Elena Poughia, the Founder of Data Natives, perceives the transformation as follows:

It’s not about deploying new spaces via data or technology – it’s about amplifying human strengths. That’s why we need to continue to connect with each other to pivot and co-create the solutions to the challenges we’re facing. These connections will help us move forward

Elena Poughia, the Founder of Data Natives

The DN Unlimited Conference will bring together data & technology leaders from across the globe – Christopher Wiggins (Chief Data Scientist, The New York Times), Lubomila Jordanova (CEO & Founder, Plan A), Angeli Moeller (Bayer AG, Head Global Data Assets), Jessica Graves (Founder & Chief Data Officer, Sefleuria) and many more will take on the virtual stages to talk about the growing urge for global data literacy, resources for improving social inequality and building a data culture for agile business development. 

On stage among others:

• Erika Cheung, Executive Director, Ethics in Entrepreneurship
• Cory Doctorow, Science Fiction Author, Activist, and Journalist.
• Whurley (William Hurley), Eisenhower Fellow, a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE)
• Alistair Croll, Founder, Solve for Interesting
• Clare Jones, Chief Commercial Officer, what3words
• Mark Turrell, Orcasci, CEO
• und many more …

BI can help you start making sense of your data, but it still expects you to do the heavy lifting when it comes to finding insights. Building predictive models that can cut down your decision time and offer better insights is a must, but achieving them sounds impossible. 

So, why are we still hung up on BI? It’s time to embrace a paradigm that empowers us to make smarter, better predictions using real data. With machine learning leading the way, data science is quickly making BI obsolete. 

This whitepaper covers: 

• How data science is helping analytics take a leap forward
• The importance of focusing on the models you use to analyze your data
• Use cases that you can implement in your organization

As the world contends with the Covid-19 pandemic and its profound impact across regions and industries, companies are not only looking for ways to safeguard their people and customers but also their business’ long-term future – read how data & analytics can help your business deal with the crisis. 

The ability to make good, timely decisions will be critical to how well businesses survive this crisis. It will also impact how well they can position themselves on a trajectory of future profitable growth when things begin to return to a ‘new normal’.

Cloud-based data platforms and data & analytics have a large role to play in this—from stabilizing the business to laying the foundations of new processes and predicting what’s next. Therefore, it is critical that you have short, medium and long-term data-driven plans in place as quickly as possible to help make informed decisions. 

Here are the three key data-driven pathways—resilience, realignment and recovery—to maximize your chances of emerging with prosperity from the Covid-19 crisis. 

The power of data on the pathways to prosperity 

Decision-making becomes more challenging during periods of stress, especially where there is uncertainty about the future. To remain successful, data must underlie every aspect of the business, providing critical input to readjust plans and predictions, as well as guide and automate decision-making.

Data has already played a significant role in the response to the crisis. Healthcare providers are leveraging data from countries that were affected earlier by the pandemic to forecast needs for hospital beds, masks and ventilators. Grocery retailers are utilizing point-of-sale (POS) data to help distributors identify and ship the items most important to their customers. And telcos are using network traffic data to monitor and manage network capacity, build predictive capacity models, identify bottlenecks, and prioritize and plan network expansion decisions. 

It’s no wonder that 75% of business intelligence, data & analytics professionals are working harder and longer hours now than before the pandemic, according to Forrester. 

Applying data & analytics to every phase of your crisis strategy 

It’s positive that now more than ever, many organizations are striving to make data & analytics a centerpiece of their culture and way of doing business. Here are some top tips on how you can develop a data-driven response to a crisis.

• Phase #1: resilience—fine-tune your data platform and leverage the cloud

During the immediate response, data & analytics professionals will need to revisit their resilience plans for their data platform to ensure it stands up in the current operating environment. Ensuring that performance doesn’t suffer is paramount, especially where workloads and data volumes have increased. And maintenance is essential to ensure data is robust—automating key tasks such as database tuning, automated indexes, data distribution and compression.

The ability to rapidly onboard new data and provide access to real-time insights will also grow in importance. As will the ability to maintain the security and integrity of data during periods of remote working. This too is where the flexibility of cloud will play its part if you’re looking to scale-up your data infrastructure.

A cloud is a powerful tool when it comes to allowing large numbers of people to access large volumes of data in real-time. Not only does it improve the ease of access and shareability of data it also increases agility and flexibility. This means you can not only turn data into value faster than ever before but also quickly adapt as the market and wider economic landscape evolve.

• Phase #2: realignment—optimize and modernize your data and infrastructure 

The use of data & analytics during this phase is critically important if you are to navigate the rocky waters of a recession. Data & analytics enable companies to objectively evaluate multiple business situations, such as how to manage uncertain supply and demand, assess and mitigate supplier risk, adjust to disruptions in operations and supply chains, and adapt to sharp changes in consumer confidence and priorities.

Understanding the changing patterns of consumers is top of mind for many of the industries most impacted by the pandemic as multiple changes in behavior are already evident. For example, Amazon has dramatically ramped up its fulfillment capacity while online grocery marketplaces have had to adapt to huge increases in deliveries of fresh vegetables to meet demand. 

Optimising existing data & analytics processes—such as developing a 360-degree view of the customer or product or consolidating data on to a consistent platform or into a set of processes—can be effective to streamline decision-making.

• Phase #3: recovery—invest in tools that focus on speed, performance, scale, and future growth

When businesses shift toward recovery, more companies will need to start to ramp up their deployment of AI-enabled solutions to boost revenue and renew top-line growth. AI and ML provide the ability to analyze data from vast numbers of sources that can discover emerging trends and anticipate potential future shocks.

Analyzing immense volumes of data to learn underlying patterns, enables you to make complex decisions and predict human behavior, among many other things. AI-enabled systems also continuously learn and adapt, which means they can optimize the insights and predictions they produce over time.

As we spend more and more of our daily lives online, both as workers and consumers, we expect ML and advanced data & analytics to be put to use to detect new consumption patterns and deliver hyper-personalized products and services. This should be part of your organization’s long-term approach, ensuring you can analyze data at scale to unlock the true value of it and operate in an agile manner with the right supporting IT infrastructure—most likely cloud-based—as well as open up data and eliminate any silos. 

Get the most out of your data & analytics

Embracing the flexibility of the cloud is a must. Uniting and analyzing data from various sources into a centralized cloud-based data platform will allow the numbers to do the talking. This foundation enables businesses to solve complex challenges and run smarter—driving change with actionable insights at fast speeds. 

This speed to value is critical in such a fast-moving uncertain economic outlook to harness prosperity. Now is the time to push the boundaries of what’s possible with data at every step of your Covid-19 crisis strategy to ensure you emerge positioned for future profitable growth.

Data scientists are constantly challenged with improving their ML models. But when a new algorithm won’t improve your AUC there’s only one place to look: DATA. This guide walks you through six easy steps for data acquisition, a complete checklist for data provider due diligence, and data provider tests to uplift your model’s accuracy. 

Editor’s note: This free guide walks you through six easy steps for data acquisition, a complete checklist for data provider due diligence, and data provider tests to uplift your model’s accuracy.

When trying to improve a model’s accuracy and performance data improvement (generating, testing, and integrating new features from various internal and/or external sources) is time-consuming, difficult, but it could be a major discovery and move the needle much more.

The process of data acquisition can be broken down into six steps:

Hypothesizing – use your domain knowledge, creativity, and familiarity with the problem to try and scope the types of data that could be relevant to your model.

Generating a list of potential data providers – create a shortlist of sources (data partners, open data websites, commercial entities) that actually provide the type of data you hypothesized would be relevant.

Data provider due diligence – an absolute must. The list of parameters below will help you disqualify irrelevant data providers before you even get into the time-consuming and labor-intensive process of checking the actual data.

Data provider tests – set up a test with each provider that will allow you to measure the data in an objective way.

Calculate ROI – once you have a quantified number for the model’s improvement, ROI can be calculated very easily.

Integration and production – The last step in acquiring a new data source for your model is to actually integrate the data provider into your production pipeline.

Get the full guide for free here.

Technology companies are under huge pressure to deliver faster and better experiences, so it’s not surprising that focus is centered on the front end of systems. With slick, friction-free services vital to outshine competitors in an ever-evolving market, backstage mechanics are often seen as a lower priority.

The trouble is what companies get out of their tools is based on what they put in. If the back-end systems running the show aren’t operating effectively, the overall performance will fall flat. Just look at the drop in iPhone sales plaguing Apple following its IOS 13 update.  

To stay ahead of the digital transformation curve and keep up with consumer expectations, businesses must recognise the risks of overlooking functionality fundamentals and work to reset the balance — beginning with a deeper understanding of where the biggest issues lie. 

The high cost of a limited focus 

Most tech organisations underpin their offerings with a support network of tools and talent, and this means back-end systems depend on contracts with various suppliers, platforms, and people. Consequently, the majority of firms also have procedures in place for coordinating these agreements and keeping operations moving, but few are doing so effectively. With the bulk of attention and budgets devoted to service features, contracts are frequently left to manual management — and the result is a multi-million-pound black hole in resources. 

According to recent Censuswide research, issues caused by manual contract coordination are costing tech companies an average of £17 million per year, with more than a third (40%) pegging annual losses at over £1 million. And the damages aren’t just financial: time absorbed by labour-intensive contract cycles typically reaches 84 hours, and for some, it runs as high as 200 hours; exceeding the equivalent of a full working month. 

Where are the key problems?

In part, these substantial losses spring from the increased likelihood of error that heavy human reliance can bring. More than half (59%) of tech organisations featured in the Censuswide study have faced difficulties due to project time delays, while a further 58% point to incorrect document disposal. 

But challenges also arise from the fact that manual processes rarely operate well at scale. Manual management has its limits and when striving to juggle multiple contracts at once, it’s almost inevitable that some inefficiency gaps will go unnoticed and critical details or actions will slip through the net. Two fifths (42%) of tech companies admit to having accidentally allowed contracts to expire, and for many, such oversight has driven serious consequences; including a drop in revenue (52%), legal issues (33%), as well as lost staff (38%) and business (36%). Taking a broader view, this trend appears to be near-universal; 96% of companies using manual aspect processes have experienced at least some issues. 

Stuck in the middle 

Given the clear link between manual contract management and negative outcomes, tech players might be expected to show an eager interest in updating their methods. But this is only half true. Analysis reveals three in five (59%) appreciate that digitising processes will be very important in the next few years. However, the shocking reality is only 9% of tech companies have moved to fully automated agreements.

While it might seem that the best solution is simply investing in new tools, addressing the problem will be a more complex task. In sharp contrast to the tech sector’s general ethos of constant innovation and development, the current inertia is rooted in multiple obstacles, which largely boil down to a reluctance to change. 

When asked to list the main barriers to automation, 34% of tech companies named company culture and 33% identified a company preference for hard documents. Possibly most telling of all, 30% stated that the digitisation stalemate is due to lack of stakeholder buy-in.  

Overcoming cultural barriers 

Tech companies are far from alone in this predicament. Change anxiety is a widespread issue and one of the core blockers for companies trying to achieve digital transformation,But that doesn’t make overcoming this hurdle any less critical. Failure rates for digital transitions are consistently high — tipping 70% globally — and only 16% of transformational efforts have successfully embedded long-lasting change. If businesses want to retain their competitive position and reduce inefficiencies, they must take a holistic approach to change by creating strategies that put equal emphasis on inspiring people and up-scaling tools.

Maximising transformation success

The good news is that a high number of tech companies have already made the first step towards improving their agreement processes: acknowledging the need for development. Now, they must make the shift from enthusiasm to action and implement transitional programmes built around two essential pillars:

1. Empowering staff

Before organisations can reap the rewards of more advanced contract processes, they must secure internal acceptance of the change. People are still the key agents of change, and this means winning hearts and minds is critical. In addition to clearly communicating the plans for new processing methods and how they will impact every employee, businesses must ensure individuals are able to master smart tools. Through tailored training, companies can help workers to use sophisticated tech with confidence: equipping them to harness tools as a means of easing daily pressure, saving time, and boosting productivity. 

2. Embracing automated efficacy 

It goes without saying that companies need to make the jump from manual to digital, but they must also choose their transformational path with care. To maximise efficiency and limit risks, an entirely digitised and secure system is vital. The best solutions not only automate the lifecycle of an agreement from the point it is created to final approval and storage, but also offer businesses total control. By giving the contract-holder final say over who sees, accesses and signs a document, these tools ensure there is minimal scope for error while keeping a single, unified version in one place for easy accessibility. Once the right tech is in place, it is then up to businesses to guide their people in best practice application to ensure investment delivers strong returns and automation stays for the long-run.

It may be hidden from view, but the back-end of any system is the critical engine room of the business; providing essential the fuel behind the customer-facing front-end. To prevent malfunctions and inefficiency from sending their services off course, tech companies must recognise the necessity of keeping their underlying processes in good order. One component of this is the system they use: bringing in the technology for effective end-to-end automation and moving away from manual practices. But people are also paramount. To embed sustainable digital transformation, companies need to select their contract systems wisely and enable their employees to realise their fully-automated potential.

The global COVID-19 pandemic has generated a wide variety of responses from citizens, governments, charities, organizations, and the startup community worldwide. At the time of writing, the number of confirmed cases has now exceeded 1,000,000, affecting 204 countries and territories.

From mandated lockdowns to applauding health workers from balconies, a significant number of people are taking this as an opportunity to step up and help in any way they see fit. And this is true of the various tech ecosystems too.

And while some are repurposing their existing startups and businesses to assist with the pandemic response, others are joining an ever-expanding number of hackathons across the globe to come up with fresh ideas and feasible solutions.

One such hackathon, #HackCorona, gathered over 1,700 people, and during the course of the 48-hour long online event, 300 people delivered 23 digital solutions to help the world fight the outbreak. Organized by Data Natives and Hacking Health Berlin, the event was created in record time, a hallmark of people’s response to the situation. There really is no time to waste.

Attracting hackers from 41 countries, the teams worked tirelessly to produce solutions that were useful, viable, and immediately available to help in a multitude of areas affected by the spread of the novel coronavirus. Mentors and jurors from Bayer Pharmaceuticals, Flixbus, MotionLab.Berlin, T-Systems International, Fraunhofer, and more both assisted the teams with their applications, and decided which would win a number of useful prizes.

“We are happy to have created a new community of inspired, talented, and creative people from so many different backgrounds and countries eager to change the course of this critical situation,”  CEO at Data Natives, Elena Poughia, said. “This is exactly the reason why we, at Data Natives, are building and nurturing data and tech communities.” 

Distrik5, born from members of the CODE University of Applied Sciences in Berlin, developed a digital currency that is earned when one of its users provides assistance to the elderly, those that are at the highest risk of dying from COVID-19 and its associated complications. The team won a fast track to join the current incubator cohort at Vision Health Pioneers.

Homenauts created a participatory directory of resources to help maintain strong mental health while isolating. Polypoly.eu developed Covid Encounters, a mobile app to track exposure and alert citizens without compromising privacy. HacKIT_19 created a solution that uses data to help you make better decisions with self-reported symptoms. 

In total, eight teams created winning solutions that are viable and instantly applicable to the crisis. And #HackCorona is just one of many such examples around the world.

“The solutions created were a good mixture of ‘citizen first’ solutions with the aim to assist people with limited technology,” Poughia said. “However, what really stood out to me was that we need more data scientists working closely with epidemiologists to predict and understand the current outbreak.”

Poughia warns that we mustn’t slow down now, or become complacent.

“I think it is admirable to see institutions, academic universities, incubators, and accelerators joining in to support the projects,” Poughia said.

“What we need is immediate action and immediate support to keep the momentum going. Volunteers should continue to come together to help but we also need the support of governments, companies, startups, and corporations, so that we can accelerate and find immediate solutions.”