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.
Four Types of Information 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:
Selling Large Data Sets
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.
Another form of information brokerage is an online information hub or market, such as an asset intelligence hub, which acts as a Facebook for IT assets by tracking all the connections between a company, its IT devices, and the manufacturers of those devices. The hub sends data about ownership to the manufacturers, who then feed it back to customers as needed through apps that provide services and predictive analytics.
Finally, there are data marketplaces that exist to provide other companies with a place and platform to monetize their own data when they don’t yet know who might want to buy it. According to Exapik (formerly Fuse Data), more than a dozen of these marketplaces have emerged since 2010 as intermediaries for buying and selling large data sets. Some offer a wide variety of data, while others are industry specific.
One of these corporate data supermarkets is Singapore-based DataStreamX, the first online marketplace specifically for real-time data. CEO Mike Davie founded it after spending time in Samsung’s Networks Division, where his responsibilities included consulting to telecommunications companies in the Middle East and Asia about monetizing their M2M and telco analytics data. DataStreamX not only provides a multi-industry, multi-vertical data marketplace, but also works with companies to package their data as products. It works with companies that have already created data products as well as those that have never bought or sold data products before.
A Southeast Asian utility company, for example, might want to analyze levels of home electricity consumption in order to understand its customers better. Although it has exact consumption metrics from each account it serves, it can’t do a true apples-to-apples comparison without knowing the size of the house associated with each account—information that’s accessible through public records in many countries, but not in the country this company serves. Meanwhile, Davie says, DataStreamX might be helping a real estate investment company in that region package information about the houses it manages, including square footage. The two companies find each other via DataStreamX. The utility company can purchase the real estate investment company’s data product, and both win: the utility gets greater visibility into its customer base, the investment company generates revenue from previously unmonetizable data. DataStreamX, of course, takes a portion of the transaction as a commission.
Challenges of the Data Business
Much of the innovation around information business models is happening in the financial services and telecommunications industries, which have long collected large amounts and types of transaction information. Retail and consumer goods organizations are actively pursuing new data-driven revenue streams, too, because social media and mobile technologies are giving them new opportunities to aggregate more information about customers.
That said, the market for monetizing data is growing more slowly than expected, not because organizations lack data to monetize, but because they’re still struggling with their approach. Creating an entirely new revenue stream unrelated to the core business is nerve-wracking, especially since data security and privacy are ongoing concerns. It’s difficult to collect, aggregate, and sell information while remaining in full compliance with the broad array of overlapping and occasionally conflicting privacy laws worldwide.
From a privacy point of view, it makes sense to create new laws to govern how organizations collect, use, and profit from the vast amounts of information they’re now collecting. Yet it may be some time before the law or best practices can catch up to new information business models as they evolve. Our expectations are already being challenged in new areas: cross-industry sharing, cross-vertical selling, and revelations we didn’t realize we could tease out of our data.
Careful analysis of smart meter readings, for example, can already pick apart energy consumption patterns at such a detailed level that a power company can determine how many television screens and laptop computers are in use at any given moment inside a house. Theoretically it could even analyze the power consumption pattern of each of those screens, correlate small spikes and dips to explosions in an action movie or pauses for commercial breaks, and conclude what TV program, movie, video game, or other application each screen is displaying.
Now, imagine the utility company selling that data to Nielsen to bolster the accuracy of its entertainment ratings. It’s almost inevitable that someone will consider that as a business model—if they haven’t already.
As companies increasingly realize that their reserves of data contain vast strategic potential not just for themselves, but for other organizations, the business of monetizing that data will expand across industries. It will definitely create new income streams and new lines of business for companies that choose to pursue it. We may even see data monetization split off to become a separate vertical industry. One thing is certain, however: the companies that actively seek out business models to generate greater value from their existing data are the ones that will be better positioned both to increase revenues and to fend off disruptive competition. D!
Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.