Many businesses are not aware of the potential benefits of big data services. Despite the hype, they either aren’t aware they have a big data issue or don’t view it that way. Big data technologies are generally advantageous for an organization when data volume, variety, and velocity suddenly grow and the firm’s current databases and applications can no longer handle the load.

Big data concerns that are not properly addressed can increase expenses and have a negative impact on productivity and competitiveness. On the other hand, a strong big data strategy can assist organizations in lowering costs and improving operational efficiency by converting labor-intensive existing workloads to big data technology and introducing new applications to take advantage of untapped potential.

Big Data-as-a-Service (BDaaS): What are big data services?

Big data as a service provides data platforms and tools by a cloud provider to assist enterprises in handling, managing, and analyzing massive data sets to produce insights that may be used to enhance business operations and achieve a competitive advantage.

Big data as a service (BDaaS) is designed to free up organizational resources by utilizing an outside provider’s data management systems and IT expertise rather than deploying on-premises systems and employing in-house staff for those functions.

Many companies generate enormous amounts of structured, unstructured, and semistructured data. Big data as a service can be offered as a contract for a managed service hosted and administered by a cloud provider or as dedicated hardware and software running in the cloud.

Remembering what big data is can help us better understand the subject.

What is big data?

Big data refers to data management issues that, as a result of the growing amount, velocity, and variety of data, cannot be resolved by conventional databases.

There are several ways to define big data, but most of them contain the idea of the so-called “three V’s” of big data:

Volume: Data volume varies between terabytes and petabytes.

Variety: Variety includes information from many different sources and formats (e.g. web logs, social media interactions, ecommerce and online transactions, financial transactions, etc)

Velocity: Businesses are becoming more demanding from when data is collected to when users receive actionable insights. As a result, data must be gathered, saved, processed, and evaluated within relatively brief time frames, ranging from daily to real-time.

Evolution of big data processing

Big data ecosystem development is progressing quickly. Today, a variety of analytical approaches serve various organizational activities.

Users can respond to the question “What happened and why?” with the aid of descriptive analytics. Traditional query and reporting setups with scorecards and dashboards are some examples.

Users can evaluate the likelihood of a specific event in the feature with the aid of predictive analytics. Examples are early warning systems, fraud detection, preventive maintenance, and forecasting.

Prescriptive analytics offer the user particular (prescriptive) suggestions. They respond to the query: What should I do if “x” occurs?

Big data as a service examples

One of the most significant developments of the digital era is the technology known as “Big Data.” Powerful analytics reveal patterns and connections hidden in enormous data sets, informing planning and decision-making in almost every business, as we see in big data benefits for SMEs.

Are you wonder about data curation definition and benefits?

Big Data usage has increased so much in the past ten years that it affects almost every element of our lifestyles, purchasing patterns, and everyday consumer decisions.

Here are a few instances of Big Data applications that impact humans daily:

Transportation

The GPS smartphone applications that most of us rely on to go from place to place in the shortest amount of time are powered by big data. Government organizations and satellite photos are two suppliers of GPS data.

For transatlantic trips, an airplane can produce 1,000 terabytes or more worth of data. All of this data is ingested by aviation analytics systems, which then analyze fuel efficiency, passenger and cargo weights, and weather patterns to maximize safety and energy use.

Big Data makes transportation easier and more efficient by:

Congestion management and traffic control: Google Maps can now provide the least congested route to any location, thanks to big data analytics.

Route planning: To plan for maximum efficiency, different routes can be compared in terms of user needs, fuel consumption, and other elements.

Traffic safety: To identify accident-prone locations, real-time processing and predictive analytics are employed.

Advertising and marketing

Advertising has always been focused on particular customer groups. In the past, marketers have used focus groups, survey results, TV and radio preferences, and other methods to attempt and predict how consumers will react to advertisements. These techniques were, at best, informed guesses.

To find out what people actually click on, search for, and “like,” advertisers purchase or collect enormous amounts of data nowadays. Utilizing precise measures like views and click-through rates, marketing initiatives are also evaluated for efficacy.

As an illustration, Amazon gathers enormous amounts of information about its millions of customers’ purchases, shipping methods, and payment preferences. The business then offers highly targeted ad placements to narrow segments and subgroups.

We have already gathered the best real-life database marketing examples for you.

Banking and financial services

Big Data and analytics are used to great effect in the financial sector for:

Fraud detection: Banks track customers’ spending habits and other activities to spot unusual behavior and anomalies that could indicate fraudulent transactions.

Risk management: Banks can track and report on operational procedures, KPIs, and personnel activities thanks to big data analytics.

Customer relationship optimization: In order to better understand how to turn prospects into customers and encourage higher use of different financial products, financial institutions study data from website usage and transactional data.

Personalized marketing: Banks build detailed profiles of each customer’s lifestyle, tastes, and goals using big data, which are then applied to micro-targeted marketing campaigns.

Government

Government organizations gather enormous amounts of data, but many of them, particularly at the local level, don’t use cutting-edge data mining and analytics tools to get the most out of it.

The Social Security Administration and the IRS are examples of organizations that utilize data analysis to spot false disability claims and tax evasion. The FBI and SEC monitor markets using big data techniques to find illegal business practices. The Federal Housing Authority has been predicting mortgage default and repayment rates using big data analytics for years.

Media and entertainment

The entertainment sector uses Big Data to analyze consumer feedback, forecast audience interests and preferences, manage programming schedules, and target advertising efforts.

The two most notable examples are Spotify and Amazon Prime, which both use big data analytics to provide subscribers with customized programming recommendations.

Meteorology

Globally distributed weather sensors and satellites gather much data to monitor the environment.

Big Data is used by meteorologists to:

• Analyze the trends in disasters.
• Make weather predictions.
• Recognize the effects of global warming.
• Determine the locations of the world where drinking water will be available.
• Provide early notice of imminent emergencies like storms and tsunamis.

Healthcare

Big Data is steadily but significantly changing the enormous healthcare sector. Patients’ electronic health records are updated in real-time using wearable technology and sensors data.

Do you know using data brings down healthcare costs?

Big Data is currently being used by providers and practice organizations for a variety of purposes, such as the following:

• Predicting the onset of epidemics
• Early symptom recognition to avert diseases that can be prevented
• Digital health records
• Real-time notification
• Increasing patient involvement
• Prediction and averting the development of major medical disorders
• Plan strategically
• Research speed up
• Telemedicine
• Improved medical image analysis

Cybersecurity

Big Data may increase the danger of cyberattacks for enterprises, but machine learning and analytics can use the same datastores to deter and combat online crime. Analysis of historical data can produce intelligence to build more effective threat controls.

Additionally, machine learning can alert companies when patterns and sequences deviate from the norm, so effective countermeasures may be performed against risks like ransomware assaults, harmful insider programs, and attempts at unauthorized access.

After an intrusion or data theft has occurred at a corporation, post-attack analysis can reveal the techniques utilized. Machine learning can then be used to create defenses that will thwart such attempts in the future.

We have already gathered cybersecurity best practices in 2022.

Education

Big Data is being embraced by administrators, academics, and other stakeholders to assist them enhance their courses, entice top talent, and enhance the student experience.

Examples comprise:

Customizing curricula: Big Data makes it possible to customize academic programs to the needs of specific students, frequently combining online learning with conventional on-site classes and independent study.

Reducing dropout rates: Predictive analytics provides educational institutions with information on student performance, feedback on suggested courses of study, and advice on how graduates perform in the labor market.

Improving student outcomes: It is possible to better understand students’ learning preferences and habits by examining their individual “data trails,” which may then be applied to design an environment that fosters learning.

Targeted international recruiting: Institutions can more correctly anticipate applicants’ chances of success thanks to big data analysis. On the other hand, it helps overseas students identify the universities most likely to accept them and best meet their academic objectives.

5 best big data services company

In the modern world, gathering data allows you to identify the causes of failure, update risk profiles, and other issues. Faster decision-making and cost reduction are further benefits.

Cloud-based analytics and Hadoop technologies enable businesses to examine information or data, instantly accelerating decision-making. But which companies are the best?

IBM

American corporation International Business Machine (IBM) has its main office in New York. As of May 2017, IBM was ranked number 43 on the Forbes list with a market capitalization of $162.4 billion. With about 414,400 people, the firm is the largest employer and operates in 170 countries.

IBM made a profit of $11.9 billion on sales of about $79.9 billion. For 24 years running, IBM has the most patents produced by the industry as of 2017.

The largest supplier of goods and services for big data is IBM. IBM Big Data solutions offer functions like data management, data analysis, and data storage.

Oracle

Oracle provides fully integrated cloud applications and platform services with more than 420,000 clients and 136,000 employees working in 145 countries. According to Forbes’ ranking, it has a market valuation of $182.2 billion and annual sales of $37.4 B.

The largest player in the big data space is Oracle, which is also well-known for its leading database. Oracle makes use of big data’s advantages in the cloud. It aids firms in defining their big data and cloud technologies data strategy and approach.

It offers a business solution that uses big data applications, infrastructure, and analytics to offer insight into logistics, fraud, etc. Oracle also offers industry-specific solutions that guarantee your business can benefit from big data potential.

Amazon

In 1994, Amazon.com was established, with its headquarters in Washington.

Amazon’s cloud-based platform is well known. Elastic MapReduce, which is built on Hadoop, is its flagship product. It also provides Big Data products. Redshift, NoSQL, and DynamoDB Big Data databases are examples of data warehouses that utilize Amazon Web Services.

Microsoft

Microsoft is a US-based software and programming company with Washington as its corporate headquarters. According to Forbes, it has $85.27 billion in sales and a market capitalization of $507.5 billion. Around 114,000 people are currently employed by it worldwide.

Microsoft has a broad and expanding big data strategy. A collaboration with the Big Data firm Hortonworks is part of this plan. Through this cooperation, Hortonworks’ data platform will have access to the HDInsight tool for analyzing both structured and unstructured data (HDP)

Google

Google was founded in 1998, and California is headquartered. It has a $101.8 billion market capitalization and $80.5 billion of sales as of May 2017. Around 61,000 employees are currently working with Google across the globe.

Google provides integrated, end-to-end Big Data solutions based on innovation at Google and helps different organizations capture, process, analyze and transfer data in a single platform. Google is expanding its Big Data Analytics; BigQuery is a cloud-based analytics platform that analyzes a huge set of data quickly.

Best big data solutions

There are very successful big data solutions according to various needs.

Here are some of them:

Big data services in AWS

The most significant big data implementation support provided by AWS is in the form of analytics tools. You can utilize the provider’s wide range of services to automate data analysis, manipulate datasets, and gain insights.

Amazon Kinesis

With the help of the Kinesis service, you may gather and examine real-time data streams. Website clickstreams, application logs, and Internet of Things (IoT) telemetry data are a few examples of supported streams. Kinesis supports data export to Redshift, Lambda, Elastic MapReduce (Amazon EMR), and S3 storage, among other AWS services. Using the Kinesis Client Library, you may leverage Kinesis to create unique streaming data applications (KCL). Real-time dashboards, alert production, and dynamic content are all supported by this library.

Amazon EMR

You can analyze and store data using the EMR distributed computing framework. It is built using clustered EC2 instances and Apache Hadoop. A well-known platform for processing and analyzing massive data is Hadoop.

By managing and maintaining your Hadoop infrastructure when you deploy EMR, you are free to concentrate on analytics. The most popular Hadoop tools, such as Spark, Pig, and Hive, are supported by EMR.

Amazon Glue

You can process data and carry out extract, transform, and load (ETL) activities using the service Glue. It can be used to transport data between your data storage as well as clean, enrich, and catalog data. Being a serverless service, Glue frees you from the hassle of establishing infrastructure and only charges you for the resources you use.

Amazon Machine Learning (Amazon ML)

Without ML knowledge, Amazon ML is a service that supports creating machine learning models. It has wizards, visualization tools, and pre-built models to get you started.

The service can help you evaluate training data, optimize your trained model to suit business requirements, and more. Once finished, you can access your model’s output through batch exports or an API.

Amazon Redshift

You can use Redshift, a fully-managed data warehouse service for business intelligence analyses. It is designed for big SQL queries on structured and semi-structured data. SageMaker, Athena, and EMR are just a few analytics services that can access the S3 data lake storage where query results are stored after processing.

You can query data on S3 using Redshift’s Spectrum capability, which allows you to avoid using ETL procedures. This function analyses your data storage and query requirements, then optimizes the procedure to reduce the quantity of S3 data that needs to be read. This cuts down on expenses and expedites query processing.

Amazon QuickSight

You can create visualizations and analyze ad hoc data with the business analytics application QuickSight. It supports ingesting data from a wide range of sources, including on-premises databases, exported Excel or CSV files, and AWS services like S3, RDS, and Redshift.

A “super-fast, parallel, in-memory calculating engine” is used by QuickSight (SPICE). This engine employs machine code generation to create interactive searches based on columnar storage. To ensure that the following inquiries are as quick as possible, the engine maintains the data after a query has been executed until the user manually erases it.

Big data services in Oracle

The expanding need for many industries, including banking, healthcare, communications, public sector, retail, etc., is met by Oracle’s big data industry solutions. There are many different technological options, including system integration, cloud computing, and application development.

Oracle Big Data Preparation Cloud Services

With the help of the managed Platform as a Service (PaaS) cloud-based Oracle Big Data Preparation Cloud Service, you can quickly ingest, correct, enrich, and publish huge data sets in a collaborative setting. For downstream analysis, you can combine your data with other Oracle Cloud Services, such as Oracle Business Intelligence Cloud Service.

Oracle Big Data Appliance

Running a variety of workloads on Hadoop and NoSQL systems requires a high-performance, secure platform, such as the Oracle Big Data Appliance. You can use Oracle SQL to query data on these platforms once Oracle Big Data SQL is installed. Oracle Big Data Appliance is protected using Apache Sentry, Kerberos, network encryption, and data at rest encryption.

Oracle Big Data Discovery Cloud Service

Oracle Big Data Discovery Cloud Service is a collection of end-to-end visual analytics tools in the cloud that use Hadoop’s processing power to turn raw data into business insight in a matter of minutes without the need to master complicated software or rely solely on highly qualified personnel.

Data Visualization Cloud Service

Oracle Data Visualization Cloud Service (DVCS) enables seamless analysis across all environments with on-premises and cloud deployment options. It is a component of Oracle’s full analytics platform. The graphical display of abstract information is known as data visualization.

Big data services in IBM

Popular database solutions from IBM that allow big data analytics include DB2, Informix, and InfoSphere. Additionally, IBM offers well-known analytics programs like Cognos and SPSS.

Below are IBM’s Big Data Solutions:

Hadoop System

Data that is both structured and unstructured is stored on this platform. It is made to process a lot of data to find business insights.

Stream Computing

Thanks to stream computing, organizations can use in-motion analytics, such as the Internet of Things, real-time data processing, and analytics.

Federated discovery and Navigation

Software for federated discovery and navigation aids businesses in the analysis and access of data throughout the enterprise. The Big Data products from IBM described below can be used to gather, examine, and manage both structured and unstructured data.

IBM® BigInsights™ for Apache™ Hadoop®

It lets businesses easily and quickly evaluates massive amounts of data.

IBM BigInsights on Cloud

It offers Hadoop as a service via the IBM SoftLayer cloud computing platform.

IBM Streams

Organizations may gather and analyze data in motion for essential Internet of Things applications.

Best big data consulting services

Bigdata Analytics Consulting Companies offer expert consultants who share their extensive industry and domain knowledge with various organizations in big data technology, big data analytics, process, and methodologies, leveraging their real-world experience, industry best practices, and technology best practices, enabling the clients to succeed in big data projects.

These are some of the best big data consulting services:

IBM Analytics Consulting

IBM Bigdata Analytics provides access to IBM’s 9000 strategy, analytics, and technology experts and consultants from around the globe, who can assess the business and identify the specific areas in which analytics can bring the most value to the business.

HP Bigdata Services

In order to transform big data into useful information, HP Big Data Services assist in reshaping IT infrastructure. The Big Data solutions include compliance, protection, strategy, design, and implementation.

Dell Big Data Business Intelligence Consulting

Dell Big Data Business Intelligence Consulting helps businesses succeed and create new revenue streams with big data solutions. The big data business intelligence consulting services include assessments, proof of concept projects, and managed services.

Oracle Consulting

Enterprise performance management (EPM) and business intelligence (BI) solutions can be rapidly and successfully deployed with the help of architectural, upgrade, and implementation services from Oracle Consulting.

Conclusion

Big data is a term that is widely used in the business and technological worlds. In a nutshell, it is the process of obtaining extremely huge quantities of complicated data from various sources and analyzing it to uncover patterns, trends, issues, and presents chances to get useful insights.

Big data as a service (BDaaS) is designed to free up organizational resources by utilizing an outside provider’s data management systems and IT expertise rather than deploying on-premises systems and employing in-house staff. Many companies generate enormous amounts of structured, unstructured, and semistructured data.

Big Data projects that are currently most fascinating and gratifying offer insights based on what is occurring right now, not just what was happening last week, allowing for immediate action rather than merely learning from the past.

Despite the fact that certain consulting organizations may be able to assist you, only you can decide which big data solutions are appropriate for your business. So, what are you waiting for? Choose your solution and join the data-driven revolution!

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.