Every Company is a Data Company

A few years ago, even leaders of successful businesses might have raised an eyebrow at that statement. But in 2021, it rings true. Every company has data. In fact, 85% of companies say that improving use of data insights in business decisions is a moderate, high or critical priority. What’s more, an increasing number of companies—as many as 51% according to Forrester—sell or share their data as a source of revenue, up from 32% in 2016.2 This concept has many names, including data productization, Data as a Platform (DaaP), and Big Data 2.0.

The burgeoning trend creates a domino effect: it provides an incentive for companies who are not already harnessing their own data efficiently to do so—not just to create or augment revenue streams, but simply to remain competitive by increasing internal efficiency and understanding their end users.

Those who are already using data effectively for internal purposes should consider identifying new ways to monetize it. Of course, first you must have a clear understanding of the data you have, which must take into account metadata definitions, data quality, privacy, and more. To have a healthy data governance infrastructure, everyone within your organization must share responsibility to ensure data lives inside—not beside—your business.

Keep in mind that a commitment to data productization also creates more responsibilities for Chief Data Officers, who must now look beyond their own walls: instead of simply using data to drive improvements within their own organization, they must find creative ways to take data to market.

Data Monetization Strategy Maturity


There are myriad ways to monetize data, including leveraging it to enhance an existing product and/or optimize the customer experience (smart companies do both). A good example of this is Amazon’s recommendation engine, which aims to increase revenue by selling more of the company’s own products to customers already on their site.

Companies can also monetize data by leveraging existing information to create products for new customers. For example, FICO (initially Fair, Isaac and Company) has evolved in how they generate revenue from the data they use to power their main offering—credit scoring.

FICO started in 1956 as a company that developed algorithms to show consumers’ past behavior, then employed predictive analytics to determine their level of credit risk and sell that information to lenders. By the early 1990s, they used artificial intelligence (AI) for credit card fraud detection. At present, their technologies drive 65% of all credit card decisions, with large financial institutions relying heavily on this information.

In 2016, FICO unveiled the Analytic Cloud and the Decision Management Suite (DMS), delivering predictive analytics at a lower cost. It also released a comprehensive suite of new B2B software tools for customer credit, marketing, fraud, cybersecurity, and compliance based on the data collected to generate credit scores. Since launching the Analytic Cloud and the DMS FICO has experienced impressive sales growth year over year, growing earnings from $881 million in 2016 to over $1.1 billion in 2019. While their FICO score business is also still wildly lucrative, DMS has been the most rapidly growing segment.

Of course, FICO is dependent on data—it’s intrinsic to their business. But what about companies where data productization opportunities aren’t so obvious?

For example, when you think of those most likely to be an innovative data company, an automotive parts manufacturer is probably not the first idea that comes to mind. However, one company in this industry created a new revenue stream by turning their buyer data into a data and analytics solution that helps their distributor partners sell more inventory. Distributors subscribe to the service, which provides direct revenue to the parent company, and the insight it provides helps them sell more of the company’s primary products.

Analytic Cloud and the DMS FICO has experienced impressive sales growth year over year

CapTech has worked on data productization efforts like this with clients as well. We recently worked with a sports industry leader, developing standardized, repeatable, and reusable APIs that deliver sports statistics to a wide range of customers, including those in media, betting, and technology. These API-driven data feeds provide sports data to customers—which includes but is not limited to sports technology firms, broadcast partners, and other sports websites—within a tiered pricing and access model. While historical data might be offered at a lower price point, any data from real-time sporting events would be higher.

In addition, CapTech developed a streaming solution that allowed the near instantaneous delivery of data. In this instance, the sports company’s customer was a gaming syndicator whose goal was to enable real-time sports gambling. Now the sports organization is able to process, enrich, transform, and deliver data within one second from the time it is recorded. Every bit of the latency in the streaming process is measured: from devices to the on-site mobile data center, mobile data center to cloud service, cloud service to database, database to betting service.


These two examples show the endless possibilities for data productization. While the tire distributor offered a new product that adds a completely different type of value to its customers, the sports company used its data to create more content for its fans, partners, and communities worldwide. Both efforts brought entirely new revenue streams into existence. Data productization doesn’t work unless the business plans are in place that allow organizations to learn and improve processes based on initial results. In the case of the sports industry leader, CapTech developed the client’s data and suite of interactive dashboards to allow the company to do just that.

But turning data into a service or product doesn’t happen overnight. Companies should make infrastructure engineering investments upfront before any new revenue streams based on the data can be launched. As noted at the outset of this article, there are also important data governance issues to consider as you prepare to embark on this path.

So whether you’re an organization lagging behind in your data and analytics efforts, or even if you’re already leveraging it for the betterment of your business, it’s important to view data through a new lens. Because the power of data isn’t having it. It’s in activating it. And with the right data strategy and partner, you have the potential to generate new business for your organization.


1. “Chief Data Officers: Invest In Your Data Sharing Programs Now With B2B Data Sharing, Benefits Are Greater Than The Sum Of Its Parts,” March 11, 2021, Forrester. Retrieved from forrester.com.

2. “Data Commercialization: A CIO's Guide To Taking Data To Market - Advanced Level: Strategy Practices For Insights- Driven Businesses,” March 27, 2020. Forrester. Retrieved from forrester.com

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Ben Harden


Ben leads our Data & Analytics practice and specializes in delivering enterprise-scale data warehousing solutions using the Agile Scrum methodology. He has been consulting with Fortune 500 clients on data and analytics solutions for over 18 years.

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Dan Magestro

Managing Director, Data & Analytics, PE Portfolio Solutions

Dan is a managing director in CapTech’s Chicago office, where he leads CapTech’s national Private Equity Portfolio Solutions practice. Dan has extensive experience working with private equity firms in technical due diligence, post-acquisition strategic planning, and data modernization, primarily in healthcare and software portfolio companies.

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Arjun Baradwaj

Arjun Baradwaj

Manager, Data & Analytics

Arjun is the Data & Analytics practice lead in CapTech’s DC office. Arjun has worked with several data clients across industries, with extensive experience in the sports space.

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