Businesses may want to rethink how they use TikTok as a platform because of the concerns raised by US politicians regarding the company’s data privacy practices.

Consumers have elevated privacy to a top priority across all digital channels. Many people are becoming more selective about what they share on social media and with whom to secure their data.

Engaging with and converting customers challenges social media managers, business owners, and content producers who run social media accounts. Additionally, some countries have passed laws and decrees that have an impact on marketers and necessitate action to assure compliance.

Apart from the actions businesses take on a social network, which platform they choose is also vital. Data privacy concerns about TikTok have been going on for years, and now it is a hot debate once again.

Choosing the right social media platform for data privacy

According to Statista, TikTok has more than 1 billion users, a 45% growth from its 689 million users in 2020. Businesses increasingly use well-known social networking sites to connect with eager younger customers.

“Organizations are turning to popular social media applications like TikTok for advertising purposes,” an adjunct professor in the Tulane University School of Professional Advancement, Demetrice Rogers, stated.

However, TikTok, which Beijing-based ByteDance runs, has drawn criticism from the U.S. Congress for its suspicious data gathering methods. Leaked audio from internal meetings at TikTok showed that ByteDance staff members in China frequently accessed U.S. data, despite repeated promises during hearings that TikTok does not collect and exchange U.S. user data with China.

If it turns out that ByteDance is giving the Chinese government access to customer data from the United States, the committee stated that TikTok “not only did TikTok misrepresent or provide false testimony about its data management and security practices, but it has placed the safety and privacy of millions of U.S. citizens in jeopardy.”

A privacy-driven ecosystem for a sustainable data economy

Experts warn businesses to carefully consider their social media strategy before linking to platforms that could potentially influence data privacy in light of the data privacy complaints leveled at TikTok.

A planned social media strategy is key

A corporation should consider any privacy concerns while advertising on a network like TikTok, according to Tulane University’s Rogers, and “fully understand the data use policy of TikTok” before joining a social media platform.

“While the organization could prosper by utilizing the platform for advertising, consumers could view the company negatively based on their use of tailored advertisements,” he explained.

According to Dominque Shelton Leipzig, a partner at Mayer Brown and a cybersecurity and data privacy practice member, a company’s social media use, including social media marketing, should be a component of its overall digital innovation and data initiatives. She oversees data management and privacy for ad tech and worldwide data innovation.

Rising cybersecurity risks threaten the healthcare industry

According to Shelton Leipzig, some general inquiries that corporate officers and directors could make of chief marketing officers regarding the use of social media include: “Are we communicating brand, value, and trust?” “Will our efforts demonstrate responsible data stewardship?” and “Have we vetted the types of ads that might appear next to the context we post?”

“It is critical for corporate leadership to include digital marketing in their oversight role. The types of things for corporate leaders to consider when it comes to use of platforms is how the company’s digital footprint jibes with its overall reputation for responsible data stewardship and trust,” she explained.

Understanding the privacy regulations that apply to your business, following your privacy and social media policies, being aware of underage social media users, having security systems in place, and most importantly, being open and honest with your customers is the most important thing for your business.

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 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.

When we think of data science, we rarely think beyond those people with the technical ability, knowledge, training, and qualifications necessary for the job.

But decision-makers need to be involved in data science too.

Whether you need to understand data science, know how to approach solution providers, or be better positioned to hire data scientists, having a solid foundation in data science and business strategy can be crucial to your organization.

The Tesseract Academy helps educate decision-makers on topics like what data science and AI are, how to think like a data scientist without being one, the fundamentals of hiring and managing data scientists, and building a data-centric culture. The Tesseract Academy runs a free event called the Data Science and AI clinic, to help decision-makers better understand how they can utilize data science in their companies.

“The attendees of our programs immerse themselves into the workshop and then come out of it with a clear, actionable plan,” Dr. Stylianos Kampakis, CEO and instructor at The Tesseract Academy, told me. “So, our workshops are crash courses for any non-technical professional who is thinking to use data science and doesn’t understand how. The most important part is the interactive exercises, which help drive the data strategy plan.”

Dr. Kampakis has been in data science and AI for many years and has worked with companies of all sizes, from solopreneurs to big corporates such as Vodafone. He is also a data science advisor for London Business School and works with various universities, including UCL and Cambridge University’s Judge Business School. He is also a published author.

For CEOs, founders, managers, entrepreneurs, and product managers, taking a data science workshop from a strategic and business perspective could give their businesses a competitive edge. After all, 2021 is looking to be a pivotal year in staying ahead of the game with data science.

“I know that executives are busy people,” Kampakis said. “That’s why I wanted to create something which can give them results as fast as possible. It’s a win-win because even I’ve seen people and companies grow due to my teachings, and they will always come back to me for further coaching later down the line. There is nothing more rewarding than seeing a client get ahead of the competition, as a result of the methods and tools I teach.”
Anyone interested can visit The Tesseract Academy’s website for further details and register for the event.

Big data allows marketing and production strategists to see where their efforts are succeeding and where they need some work. With big data analytics, every move you make for your company can be backed by data and analytics. While every business venture involves some level of risk, with big data, that risk gets infinitesimally small, thanks to information and insights on market trends, customer behaviour, and more.

Unfortunately, however, many CEOs seem to think that big data is available to all of their employees as soon as it’s available to them. In one survey, nearly half of all CEOs polled thought that this information was disseminated quickly and that all of their employees had the information they needed to do their jobs. In the same survey, just a little over a quarter of employees responded in agreement.

Great Leadership Drives Big Data

In entirely too many cases, CEOs look at big data as something that spreads in real-time and that will just magically get to everyone who needs it in their companies. That’s not the case, though. Not all employees have access to the same data collection and analytics tools, and without the right data analysis and data science, all of that data does little to help anyone anyway.

In the same study that we mentioned above, of businesses with high-performing data-driven marketing strategies, 63% had initiatives launched by their own corporate leaders. Plus, over 40% of those companies also had centralized departments for data and analytics. The corporate leadership in these businesses understood that simply introducing a new tool to their companies’ marketing teams wouldn’t do much for them. They also needed to implement the leadership and structure necessary to make those tools effective.

Great leaders see big data for what it is – a tool. If they do not already have a digital strategy – including digital marketing and production teams, as well as a full team for data collection, analytics, data science, and information distribution – then they make the moves to put the right people in the right places with the best tools for the job.

Vision, Data-Driven Strategy, and Leadership Must Fit Together

CEOs should see vision, data-driven strategy, and leadership as a three-legged chair. Without any one of the legs, the chair falls down. Thus, to succeed a company needs a strong corporate vision. The corporate leadership must have this vision in mind at all times when making changes to strategy, implementing new tools and technology, and approaching big data analytics.

At the same time, marketing and production strategies must be data-driven, and that means that the employees who create and apply these strategies must have full access to all of the findings of the data collection and analysis team. They must be able to make their strategic decisions based directly on collected data on the market, customer behaviour, and other factors.

To do all this, leadership has to be in place to organize all of strategic initiatives and to ensure that all employees have everything they need to do their jobs and move new strategies forward.

This post originally appeared on RonaldvanLoon.com

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Many industry prognosticators (and not a few vendors) are pushing Internet of Things (IoT) technology but are somewhat vague as to how financial results for actual businesses will be materially improved. Instead, they tend to focus on concepts like “digital transformation,” which sound promising but is difficult to quantify. This places many businesses in a quandary—they are aware of the fact that IoT holds great promise but they can’t really move forward without being able to identify a tangible ROI. And make no mistake, unlike consumer IoT, and even government-sponsored IoT initiatives (think smart cities), without tangible and quantifiable improvements in financial outcomes (i.e. an ROI), businesses will be hard pressed to move forward with the vigor that industry prognosticators think they should.

Unfortunately, this lack of focus on tangible business uses cases has the potential to stall growth in IoT and deprive businesses of many of the benefits they expect to realize from it (and growth in M2M should not be interpreted as growth in IoT; M2M is simply connectivity while IoT typically involves multifaceted systems incorporating machine learning, data analytics, complex rule generation, and automated orchestration of actions).
Fortunately, there is an approach to IoT deployments that can, depending on the nature of the business, demonstrate fast and meaningful payback. This approach focuses almost exclusively on actual business-oriented IoT use cases but it differs from the “platform-first” strategies being pushed by many vendors and analysts.

The platform-first approach

Proponents of the platform-first school argue that organizations of all types should first make a corporate-wide decision on an IoT platform. Although the definition of IoT platform is somewhat broad, in general it is the system upon which applications can be built to take advantage of data being collected from myriad dissimilar devices. After standardizing on an IoT platform, organizations then need to purchase or develop applications, potentially integrate other enterprise systems and ingest device data. Only after they’ve done all of that can they begin to focus on IoT use cases that actually benefit the business and presumably, generate an ROI.

The challenges associated with this approach should be obvious. In most cases businesses are being asked to make significant financial investments with no clear view as to what, if any, payback will result. They are also assuming substantially more risk than would otherwise be required since they are being forced to make enterprise-wide decisions on what is still, in many cases, evolving technology. Finally, platforms are by definition incomplete systems; they need applications to be developed on them before they can be deployed in production. This aspect carries the downside that time-to-benefit is lengthened.

The use-case-first approach

The reality is that, as has been the case with most major technologies adopted by businesses over the last quarter century, initial production deployments of IoT technology will almost always be related to individual business initiatives. These initiatives are typically not viewed, nor should they be viewed, as IoT initiatives. Instead, they are focused on driving specific business outcomes—for example, improving asset uptime, reducing service and warranty costs, improving food safety, complying with government regulations, adding new revenue generating services, etc. It is only as organizations evaluate technologies that can help them meet these business objectives that they frequently discover data generated by various distributed devices can be harnessed, analyzed, and used to automatically drive business processes. In other words, these ROI-producing business initiatives begin to take on the aspects of IoT initiatives.

The important point here is that successful—and successful means that they (a) work, and (b) provide a financial return—IoT initiatives are those for which the main goal is a quantifiable business outcome and in which IoT only plays a supporting role, albeit a critical one.

How did we get here?

It is possible that the chief reason we see a bifurcation in approaches to industrial IoT is that different businesses may choose different departments to spearhead these efforts. For organizations that look to IT to lead the charge, the platform-first approach will be more attractive as it potentially represents a uniform architecture that can be deployed across the enterprise. However, it also represents an “IoT for IoT’s sake” approach that may fail to deliver a payback and leave the business somewhat frustrated with the results.

Use-case-first initiatives, on the other hand will almost always be sponsored by OT (operations technology; really any revenue generating line of business). As such, there is by definition an ROI-producing business objective driving things and IoT, while critical, is incidental to the initiative.

Is there a downside?

Some will argue that the use-case-first approach could result in dissimilar systems being deployed in different parts of the organization, long the bane of IT’s existence. While this could be the case, the potential downside is mitigated by the fact that—unlike the early days of computing (Mac versus Windows) and local area networking (Ethernet versus Token Ring)—IoT systems use well-established protocols and standards. This allows dissimilar systems not only to coexist but even to exchange information and leverage multiple data sources.

While this battle is likely to continue for some time it is also likely that OT will prevail, at least in the near term. The reason is simply that ROI drives everything for businesses (again, consumers and governments tend not to care whether their investments are well thought out) and IoT platforms, by themselves, are hard-pressed to generate meaningful financial returns.

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The rise of data science in the last decade has been driven by the ease of access to deep data and significant reductions in the costs associated with processing it. These days anyone with a credit card can now setup a cloud-based data warehouse and tracking system within minutes, but achieving a return on this investment is not so straightforward. This is not to say that effective use of data science can’t be very profitable, just that it is not always guaranteed.

There are three key reasons why data science projects can potentially fail:

1.Solving the wrong problem

Most data science applications are about optimization, i.e. let’s take a product and make it better, faster and easier using data. Ideally you could take the product as a whole and optimize for revenue, but this is not always possible, as it requires taking into account all the elements that can influence revenue, and their relationships with each other, thereby factorially increasing the number of permutations that would need to be tested. This is why optimization problems are usually smaller scale i.e. increase consumption via better recommendations, increase conversions with re-targeting, etc. However, this simpler view can lead to large resource dedicated to solving a problem that may have little impact on the overall revenue.

Taking an example from games, product managers obsess over getting non-spenders to convert. There is a good reason to obsess over this; most F2P games only get around 2% of players to ever spend. However, the clearest route to improving this number, offering a large ‘first payment’ discount, is not a good solution to improving revenue as it naturally deflates the value of in-game content and may put off repeat spends. There are analogs to this in retail CRM systems which have established a ‘race to the bottom’ for pricing to the point where consumers now will only spend if they think they are getting a substantial discount.

2.Mismatch of problem, technology and personnel

While the pool of qualified data scientists and engineers has swollen over recent years, the diverse and ever growing range of technology solutions means that any single data scientist or team may only have experience with a small range of vendors. This is a problem as each source of technology is very much designed to work with a particular class of data. For instance, Hadoop is a great solution for batch-processing, but is not well suited to data that is drip-fed in real-time. Similarly, NoSQL databases are great for cases when the data structure needs to be flexible, but will not perform as well on large static structured data as a relational database. Additionally, while normalized data schema may be appropriate for traditional database tech and low dimension problems, flat wide structures will give a performance boost in modern column oriented databases like PostgreSQL and AWS redshift.

Mismatches of technology and/or personnel to the core business problems are common across the games industry and typical for small companies that simple cannot invest in a large diverse data team. In these cases the impact on data productivity can be devastating, with latency and data complexity issues causing all but the simplest business use cases to be abandoned.

3.Data integrity

Ultimately the best laid plans of data scientists are undone by the simplest of errors. Erroneous data feeds are one of the most common issues in data science projects. These are usually caused by a lack of communication with product developer and/or a lack of understanding of how the product operates. In recent times the proliferation of third party cloud services, and the need to combine data from them, has vastly increased the opportunity for data bugs to spawn and propagate.

With time and diligence problem data feeds can be corrected, but this process will introduce costly delays that can reduce confidence in data usage across a business.

It is generally the mismatch between simplified business goals and the way they are defined as analysis projects that causes failures or problems with data science projects. Often, commercial management doesn’t fully understand the process required to conduct an analysis project and this is compounded as they invariably have a requirement to obtain broad answers quickly. Analysts need to stand strong and become better communicators and negotiators; as it is they who ultimately have to take responsibility for the scope of the projects they agree to lead. Research topics need to be broken-down into their constituent parts, with gap analysis undertaken on the data, tools and human resources available, so that realistic expectations and timescales are set.

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Whether you run a small business with just a few employees or you are in charge of a multinational corporation, you can benefit from an effective big data strategy. Thanks to website analytics, geo-location services, data mining, and the constant stream of data flowing to and from us through everyday devices and products, there is more market data available today than at any other time in history.

Not only that, but the amount of data we’re producing is growing at an incredibly rapid rate. In less than five years from today, experts predict that our annual data creation will reach nearly 45 trillion gigabytes. With mind-boggling amounts of data like that available to individuals, corporations, and governments, there is no question that your business needs a big data strategy.

Why? Even the most powerful computers aren’t going to sift aimlessly through multiple trillion gigabytes of information in a timeframe that will work for gathering information on your market and your target audience. Thus, you need a focused, data-driven strategy that will not just aim to collect information but to use that information in the most effective ways possible to help your business overcome obstacles and improve your bottom line.

So, before you dive into big data analytics, you should first ask yourself four questions. These will help guide you as you create an effective strategy that will show significant and tangible improvements for your business.

1. What Is the Biggest Challenge Facing My Business Right Now?

Are you having difficulty standing out from the competition? Is production a problem? Are your branding efforts falling on deaf ears? Are your customers not fully satisfied? Don’t you know your customer well enough? Understanding the biggest challenges facing your company will let you know where to focus your data collection and analysis efforts. If you’re launching a start-up and are trying to identify your ideal customers, you’re going to be looking at different data sets than if you have an established business and you’re launching a new product.

2. What Can I Do to Get to Know My Customers?

Data collection and analysis can do a lot to show you who your customers are, where they live, what kinds of patterns they follow when using your site or purchasing things from you, etc. Consider all of the ways you can get to know your customers to improve efficiency in your company’s website, engage your customers more, and give them an all-around better customer experience.

3. Will Better Insights and Data Analysis Help Me Handle My Business’ Biggest Challenges?

This is almost a trick question because the answer is almost always, “Yes.” If you are trying to increase the average shopping cart value in your ecommerce store, understanding your customers’ shopping patterns and making targeted recommendations can help. If you are trying to gain more exposure through social media, your insights and data analysis of visitor engagement can help a great deal, as well.

4. How Can I Apply Big Data Analytics to Create the Best Possible Strategy?

Look at your goals for your business and how they relate to what you do and don’t know about your customers. With so much data streaming online today, there’s no reason that you should ever make a strategic move that isn’t data-driven. Consider your customers’ browsing, buying, and spending patterns. Look at trending online and social media activity for your target audience. Find out how you can tweak your strategy to help focus it more and make it more customer- and data-driven.

This post originally appeared on RonaldvanLoon.com

image credit: Rajiv Patel

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