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.”