In 2016, the Golden State Warriors won nearly 90% of their games, a feat widely attributed to one player: Stephen Curry. But the real story isn’t about a superstar; it’s about what happens when organizations act on data.

“Everybody’s talking about a single reason why they’re winning so many games,” said Dr. Sebastian Wernicke, a 3x TEDx speaker and author of Data Inspired, who spoke at the Next Generation SAP Enterprise Architect Learning Forum 2026. “And that is basketball superstar Steph Curry.”

Curry’s greatness wasn’t just instinctual; it was statistical. Before that season, no player had ever made more than 300 three-pointers in a year. Curry made over 400. The decision to let him shoot at unprecedented volume seemed reckless, until the numbers told a different story.

“They simply looked at the data,” Wernicke explained. “What they found was, on average, he gets 1.53 points per three-pointer, versus 1.36 points per two-pointer. So from a data perspective, it makes complete sense.”

And yet, the real insight isn’t that data works. It’s that it took decades to act on it.

The 30-Year Gap Between Insight and Action

The math behind three-point shooting wasn’t new. According to Wernicke, teams had access to similar data as far back as the late 1980s.

“And yet it took about 30 years for a team to try that,” he said. “So why did it take so long?”

That lag reveals a deeper truth about how organizations use data. Insight alone doesn’t drive change. Systems, habits, and culture often stand in the way.

“It’s easy to say, ‘just tell the basketball player, throw from farther away,’” Wernicke said. “But that’s not how it works. You have to reorchestrate the entire game.”

In business, the same dynamic plays out. Companies don’t fail to generate insights, they fail to transform around them.

The Data Paradox: Priority Without Progress

Few executives would argue that data isn’t critical. In fact, Wernicke points to a striking statistic: nearly every company claims data and AI as a top priority.

“If you ask companies, are data and AI a top priority, 99% of them say yes,” he said.

And yet, satisfaction tells a different story.

“If you ask companies how happy they are with their progress … it’s always two-thirds that say, ‘we’re not entirely happy.’”

This disconnect between ambition and outcome is one of the defining challenges of modern business.

From Data-Informed to Data-Driven

To understand the disconnect, Wernicke breaks the evolution of data into three eras.

1. Data-informed. Think early analytics, dashboards, reports, and even anecdotal methods. Wernicke points to Sam Walton, who famously gauged store performance by flying over parking lots and counting cars. “It’s like business intelligence for billionaires,” Wernicke joked.
2. Data-driven. This is where most companies operate today, embedding analytics into operations, automating decisions, and scaling insights across the enterprise. “If you manage to use data to improve and automate your processes, you are getting ahead of other businesses,” Wernicke said.
3. Data-inspired. In a data-inspired company, data isn’t just used to improve decisions; it’s used to rethink them entirely. “The goal of data is not just to create value,” Wernicke said. “We need to think of data as a change agent.”

That shift changes everything:

• Instead of asking “what’s the answer,” companies ask “what does the answer mean?”
• Instead of automating decisions, they empower people to make better ones.
• Instead of eliminating uncertainty, they embrace it. 

“Data is not going to get rid of uncertainty,” Wernicke said. “It’s just going to show you what’s out there and amplify it.”

Designing for Better Decisions

So, what does it take to become data-inspired? Wernicke outlined four shifts:

• Radical data integrity: Data must remain trustworthy even as systems evolve.
• Connectivity: Value comes from linking datasets, not just analyzing them in isolation.
• Incentives: Organizations must reward experimentation, not just efficiency.
• Decision architecture: Companies must rethink how decisions are made—not just what data informs them. 

But the most important shift may be cultural.

“In the past, you’ve looked to data to say, ‘show me what’s right,’” Wernicke said. “The new default needs to become: ‘show me where I’m wrong.’”

Why Data Alone Can’t Create Greatness

To illustrate the limits of pure data, Wernicke pointed to an art experiment. Researchers asked people what they wanted in a song—length, theme, instruments—and then created “The Most Wanted Song” based entirely on those preferences.

The result was underwhelming.

“Not my words,” Wernicke said, “but a user described it as one of the most boring pieces ever.”

The implication is clear. “Of course, you cannot create a brilliant song just by following the data,” he said. “And yet we’re tempted to believe that we can create a brilliant business by just following the data.”

The Human Barrier to Data

If the path forward is so clear, why don’t more companies follow it?

Because the biggest obstacle isn’t technical—it’s human.

Wernicke calls it the “data deficit theory”: the belief that more data will automatically change minds.

“If only we get more data to the right people… they will act differently,” he said. “I believe it intuitively. I just know it to be wrong.”

In reality, human psychology works against data-driven change. When confronted with information that contradicts beliefs, people often double down. Add in cognitive biases he cites more than 180 known ones and it becomes clear why dashboards alone don’t drive transformation.

“Unless data changes stuff, we’re not getting the value out of it,” he said. “Otherwise, it’s a pretty expensive hobby.”

A Data-Inspired Future

Transformation doesn’t start with massive overhauls. It starts with a simple question.

“Look at one thing that you think your organization is pretty sure about,” Wernicke said. “And then ask: what is the evidence that might change what we believe?”

Curiosity over certainty is what separates companies that merely optimize from those that reinvent.

“Data-driven companies optimize what exists,” Wernicke said. “Data-inspired organizations discover what’s possible.”