“Data is a critical asset to business success.” That’s probably the closest thing to a self-evident truth you’re likely to find in today’s ultra-competitive business landscape. We all know how important data is. Those who have more of it, and know how to wield AI and advanced analytics upon it, have a substantial advantage. And yet, businesses face headwinds when trying to monetize their data, particularly outside their company. That’s why, when it comes to data monetization, we are still in the early stages of the game.
Doug Laney has dedicated a portion of his career to finding methods to break down the value behind data. His data valuation journey started after the Twin Towers came tumbling down on 9/11. The loss of life was tragic, but companies also lost enormous amounts of data. Insurance companies claimed that data had no value, hence Laney’s study of “Infonomics” was born.
Twenty years later, after a stint as a Gartner analyst Laney continues to study the nature of data at the business consulting firm West Monroe. He helps clients come up with strategies for managing data like the real asset that it is, and finding ways to use it for competitive advantage.
“The economic differences of data versus other kinds of assets is tremendous,” Laney says. “People call data the new oil, but that’s not at all accurate. You can only use a drop of oil one way at a time. Once you use it, it’s gone. And using oil doesn’t create more oil. But data is what economists would call a non-rivalrous, non-depleting, regenerative asset. Companies that understand the economic differences of data are really well positioned to capitalize on it.”
The biggest companies in the world certainly have figured that out. Amazon, Google, and Facebook ostensibly are considered technology giants, but Laney doesn’t like that description at all. “They’re data companies,” he says. “They’re making their value out of data, not technology. To continue to call these companies technology companies, is not only wrong, it’s distracting from who they actually are.”
Data isn’t visible to the naked eye, so it can be difficult to see data companies in the real world. Take airlines, for example. You might think that United Airlines and American Airlines are valuable in part because of the large airplanes they own.
But when United and American went to get COVID-19 relief loans to help them through the pandemic, the banks weren’t interested at all in using big jets as collateral. They wanted their data, in the form of MileagePlus and AAdvantage.
“They’re much more data companies than anyone realizes,” Laney says. “We think that they own planes, and partially own airports, or whatever. They own gate rights and they own customer data. That’s it.”
Recognizing that data has value, and actually realizing that value, are two separate things. It’s important for business leaders to take a hard look at the data assets they have, and try to discover what that value is. Laney has created models, based on longstanding accounting methods (i.e. the cost approach, the market approach, and the income approach) that can help companies put an actual dollar value to their data.
But coming up with a dollar figure is the easy part. It’s arguably much more difficult to find a business model that allows a company to easily extract that value from the data. That’s due in part to the nature of that value, or what somebody else is willing to pay for the data. Creating these networks that allow data monetization to happen is not easy, and new data privacy regulations make it harder.
“A decade ago, there was more of a focus on using any given data asset for some operational purpose, like we’re going to use our customer data to help us sell more, and then maybe some analytics purpose, like we’re going to report on it, so create some pretty pie charts, some bouncy bar charts, or dashy dashboards,” Laney says.
