Data has never been more valuable. A recent McKinsey report indicates that data moving across international borders raised the global GDP by US$2.8 trillion in 2014 alone. In fact, McKinsey claims that these international data flows now have a greater impact on GDP growth than the movement of goods and services.
Now that almost every transaction has a data component, data is woven inextricably into even the smallest company’s daily operations. Any business can use digital platforms to reach a potential worldwide customer base. And with the rise of the Internet of Things and other connected devices, every business can now collect and strategically leverage more information than it could ever have dreamed of just a few years ago.
But it’s not just that data is necessary to support other transactions. Data, which has always been important as an input to business strategy, is now becoming business strategy. According to Forrester, 30% of enterprises tried to commercialize their data in 2015, a 200% increase over 2014, when only 10% of enterprises took their data to market. Although Forrester predicts that many of these attempts to derive value and revenue from data will “sputter,” IDC is more optimistic, predicting that by 2020 data monetization efforts will allow companies to generate an additional $430 billion in revenues.
In short, today’s businesses are sitting on a potential treasure trove of information that is ripe to be transformed into insight, either alone or in combination with data from other sources, and from insight into revenue. The problem is that many organizations don’t have easy access to their data or aren’t sure how to take advantage of it. They may not even be aware of how valuable their data could be. That leaves them vulnerable to disruption—and leaves money on the table.
However, the rise of Big Data and the technologies necessary to gather and parse it have finally given companies a chance to identify and claw back some value from the contents of their data warehouses. As they increasingly compete less on product or service attributes than on strategic insight and business models, they’re finally learning how to turn data that’s already on hand into additional revenue streams and entirely new business models.
When companies decide to begin monetizing their data, they generally start by asking themselves two questions: Do they look at the data they’ve been giving away (or the data sitting unused in databases) and look for opportunities to monetize it? Or do they start by identifying a customer need and then try to figure out how to monetize their data to solve it? The answer is both. Your data scientists have to work from one direction, looking for patterns in the data that suggest possibilities for insights other organizations would be willing to purchase. At the same time, your sales and customer service teams have to approach it from the opposite direction, asking customers what their problems are. Then you need to see how they’re currently trying to address those problems so you can determine whether you have data that might be relevant and useful.
As more companies tackle the challenge of matching their data to problems it can solve, four broad types of information business models have emerged:
The sale of business data is not, in and of itself, a new business model. Retailers and other organizations have been selling their mailing lists and other data sets for decades. However, today’s data sets are larger and more complex, by orders of magnitude, than anything previously available, and business analytics tools are more sophisticated and better able to squeeze more information out of them. Organizations that have these vast data sets and can afford the tools necessary to analyze them are creating revenue streams based on selling the raw data and/or the results of their own analysis.