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.
1. 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.
GM, for example, gathers vast amounts of information about its customers’ driving habits through its OnStar onboard computers, then anonymizes the data sets and sells them to insurance companies. The insurers then analyze the data for driving patterns that allow them to assess risk more intelligently. In the end, consumers also benefit, because the insurers can develop new pricing bundles that more accurately reflect actual risk.
This business model also allows companies to leverage data that was, until recently, no more than a by-product. For example, 60%–80% of the world’s text messages pass through a single hub. (Disclosure: The hub is owned and operated by SAP.) Previously, the hundreds of communications providers that transmit these text messages used only the details about where they originate and terminate for billing purposes; but by working with the hub’s operator, they can now monetize the information by aggregating it in a vast, anonymized database. Other companies can buy access to query the database about the physical movements of mobile phones. For example, the database reveals not just how many people were in a particular location at a specific time, but how many people who live within 10 miles of a given address are likely to pass that address at 1 p.m. on Thursdays. You can imagine how valuable that is for retailers assessing the success of a store or planning their inventory. Indeed, London-based sandwich chain Pret A Manger uses these analytics to predict where to open new shops—and how many of which types of sandwiches to make each afternoon in each of its 350 current locations.
Owns the Information You Want to Sell?
Global interest in privacy is rising, and with it, interest in personal information platforms—tools that allow individuals to decide on a case-by-case basis whether an organization can collect their data, how much of that data it can collect, and how much of that data it can then share with advertisers for a profit.
Handshake, for example, is a United Kingdom-based web site and app, currently in the closed beta stage, that will enable users to create a profile indicating what data they’re willing to share, then negotiate the price of their data directly with firms that want to purchase it. Theoretically, individuals will be able to use this secure marketplace for personal information to negotiate deals such as: “I will let you know A, B, and C about me, and you can share A and C, but not B, with these specific advertisers. In exchange, you will pay me X fraction of a percent of the advertising revenues (or $10, or a gigabit of high-speed mobile data, or a movie pass).”
With the European Union implementing strict new privacy regulations, these initiatives that allow individuals to sell the rights to their own data may have a profound impact on organizations’ ability to monetize data, even if the data is aggregated and anonymized.
2. Slicing and Dicing Data
Just as Big Data technology makes it possible to gather and analyze larger amounts of data than ever, it also allows companies to break that data down into smaller, more conveniently consumed, lower-friction offerings.
For example, think about travel companies that sell digital maps and guidebooks, not just by region or country, but by city or even neighborhood. Tourists might balk at spending $15 for a guide to an entire country when they’re only visiting three cities, but they might happily drop a dollar each on guides to each of those cities, and a little more for detailed walking tours of the neighborhoods where they’re staying.
Another approach is to meter data based on the amount purchased or the time spent searching for it. Research databases that charge by the article and genealogy web sites that charge by the document are characteristic of this model. So are paywalls and freemium apps like Strava, a fitness app that collects and aggregates data about physical activity through a variety of devices and sensors. The free version of the app delivers basic information, but to access deeper analysis, users have to purchase the premium version.
This business model also includes micropayments, which spread out the cost of accessing or subscribing to data in such small dollar amounts that users have a hard time resisting the purchase. For example, insurers have often required individuals who enjoy high-risk activities such as skiing to carry an umbrella personal injury policy, essentially forcing them to pay for an entire year of coverage for an activity that they participate in only occasionally. Today, though, more granular insight into customer behavior lets insurers offer insurance for a high-risk activity at the point of purchase—for example, adding it to the cost of the lift ticket, much as travel agencies offer flight cancellation insurance when customers book their vacations.
3. Selling Customers’ Data Back to Them
If companies create an infrastructure that allows them to collect data about how their customers use their products and services, they can analyze that data to generate useful feedback, not just for themselves but for their customers as well. This is becoming especially common in the sporting goods and athletic apparel industries, where companies are snapping up fitness apps like MyFitnessPal and RunKeeper and building social communities around them. In the short term, this helps companies create products and services that allow customers to track and improve their performance, use their purchases more effectively or enjoyably, and make better choices about future purchases. In the long run, it builds customer trust and loyalty so that, over time, they participate more in the community, share even more data, and attract their peers (and their peers’ data) as well.
This is as true in the B2B world as it is in the B2C market. A company can analyze its business customers’ use of its product and sell that analysis back to customers to improve usage or boost adoption. Automotive Resources International, for example, is the world’s largest fleet management company, specializing in rescue trucks, police cars, ambulances, and other vehicles that require customization and special accessories. It currently differentiates itself from the competition by sharing mileage, fuel consumption, and other performance data with customers as part of its basic package.
B2B companies can also expand on this idea by offering aggregated information about their entire customer base to individual customers. This allows customers to compare their own use of a product or service to the average, to companies like their own, or to groups of other companies with particular characteristics, and to then adjust their own use of the product accordingly. For example, if your database contains data about thousands of organizations’ procurement functions, you can create benchmarks that allow each of them to make better procurement decisions.
4. Brokering Information
We saved this business model for last, because it’s the most obvious one: data monetization not as an additional revenue stream, but as a core business. This is the business model of Bloomberg L.P., which aggregates market data and financial news and provides a tool that enables users to access and act on it, and of Google, which delivers free search to users in return for targeting eyeballs for advertisers.
Companies with the information brokerage model may be hiding in plain sight. TripIt, for example, lets users forward their travel bookings to a service that collates them into an organized itinerary that they can check with a mobile app (Disclosure: TripIt is owned by SAP). The app provides basic alerts like check-in times and gate changes at no charge, and it generates some revenue by charging for an upgrade to TripIt Pro, which offers extra features (an example of business model #2). However, TripIt’s primary purpose is to collect travel and expense data about corporate travelers and provide detailed analytics about that data to their employers.
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.