Clive Humby famously clued us in on the importance data would assume in our lives nearly 20 years ago with his claim that it was “the new oil.” Since then, “data-driven” has become a phrase that every business uses and aspires to, suggesting that most organizations have adopted a data culture and every employee feels empowered by data and responsible for protecting and nurturing a data-first environment.

You can forgive data professionals who gathered in New York City recently at the inaugural Data Universe event, however, if they feel this promise has not yet been fully delivered upon. Many have had a different experience, where data teams are isolated from the rest of their organization and feel the burden of establishing a data-first culture falls disproportionately on their shoulders.

But if establishing and maintaining a culture that elevates data and AI to their proper place isn’t the sole responsibility of the data team, who’s problem is it?

Accountability

As it turns out, it’s everyone’s problem. But, according to Rob Thomas, senior vice president of software and chief commercial officer at IBM, getting everyone in an organization to understand and take ownership in a truly data-driven business isn’t common.

In his Data Universe session, Thomas, lamenting that an IBM study found only 40 percent of businesses are currently in production with AI solutions, touched on what he considers the main problem.

“The reality is, is nobody feels accountable, so things just kind of drift along,” he said. “You really need to stop experimenting and start getting stuff into production. But leadership has to step off the ledge and say, ‘we're going to be an AI-first company, because if we're not, we're going to be left behind’.”

So, one of the most important functions of corporate leadership over the next few years will be establishing a culture and communication that emphasizes AI and data ownership throughout an organization, not just from the data team.

Is Data an Asset?

Perhaps one reason why a data-driven culture takes longer to establish than, for example, a sales-driven culture, is because data has no value. At least to accountants and insurers. And when huge swaths of the global financial system can’t even agree on that, it becomes apparent that the way we think about data outside of data teams still need to evolve.

In a keynote address at Data Universe, Doug Laney, an author, professor and influencer who has written pioneering books on data and AI, expanded on Humby’s data-is-the-new-oil metaphor by responding, “no, it’s more valuable than that.”

Laney noted three characteristics about data that make it that way. Unlike oil, data is a non-depleting asset—you can use a single piece of it over and over. You can also use the same piece of information in multiple ways simultaneously. And, when you use data, instead of energy that is used and gone forever (and pollutants), it generates even more data.

“No matter what definition you use, data clearly meets the criteria of an asset,” Laney said to the Data Universe audience. “It's something that's owned and controlled, exchangeable for cash, and generates probable future economic benefits.”

And yet, since shortly after 9/11—when many companies affected were trying to value data lost in the terror attacks (before the days of cloud storage)—the insurance industry ruled that data wasn’t considered property and was explicitly excluded from policies. And, since that was the case and courts backed them up, the accounting profession said, incredibly, that data is not an asset. Its value can’t be included on a balance sheet, even if the express purpose of the company is to buy and sell data.

So, aside from one Senate hearing on the matter, the entire insurance and accounting professions are ignoring data.

“The greatest source of value in the economy today is something that we can't recognize on our balance sheets,” he says. “And it's creating undue market volatility, and a lack of transparency into what companies are doing.”

Governance

Finally, the ever-increasing complexity introduced by the emergence of AI—especially generative AI and the LLMs that underpin it—is another example of how entire organizations are going to have to change their thinking and accept that data is everyone’s responsibility.

Compliance with regulations, protecting employees and customers, data security and more are governance concerns that transcend company board rooms.

Tony Mazzarella, the director of Enterprise Data Enablement & Governance for Voya Financial, laid out challenges inherent in trying to make the use of AI and data safe and how various parts of an organization become responsible.

“Fifteen years ago when we talked about governance, it was all about security, access and usage, which fell under IT,” Mazarella says. “As data has gotten more complex, however, so has governance. We’re thinking about things like data quality and master data management, which is now falling under your data management offices. We now see things like biases in data sets, ethics and transparency because of generative AI fall under the responsibility of the governance function within organizations. And it's not static. Generative AI will introduce regulatory and legislative requirements that we don't even know what they are yet. That's introducing additional complexities, particularly for those organizations that are global or multinational.”

So, what it means to be “data-driven” or “AI-first” is essentially that employees and managers at every level of every organization have a stake in data. It’s their problem, too. In this environment, philosophy, culture and strategy are perhaps more important than ever. Clear communication remains the most important way to ensure every employee understands their role in advancing a data-driven culture and how they support the data team, not abandon their responsibility to it.

“Data and AI empower every person and organization on the planet to achieve more,” Mazarella concludes. “Businesses need to look at every product, every service line, every capability, it should all be to drive that mission. Everybody in an organization should be building toward enabling data teams to solve the world's toughest data and AI problems.”