Enterprise data strategies often falter because leaders conflate consistency with uniformity and mistake well-designed architecture for actual business impact. Too often, global standards are applied without regard for local context, and success is measured by platform delivery rather than by whether data changes decisions, behaviors, or outcomes. For Yorck F. Einhaus, a two-time Fortune 100 Chief Data Officer, a global data strategy only works when it is adapted locally. Most importantly, it must be treated as a shared responsibility between technology and the business.
“You can have a joint objective, but you need to adapt the strategies to the local, regional, and cultural differences to make it successful,” says Einhaus, who has spent more than two decades leading technology, enterprise data, AI, and transformation programs across multinational organizations. “Basically, one size does not fit all and never will.”
Global objectives, local strategy
Einhaus has led large, distributed teams across regions that shared language, tools, and enterprise goals. Even with that common foundation, he found that the same message could land very differently depending on where it was received. What seemed clear and motivating in one market could feel misaligned in another, shaped by local expectations around leadership, accountability, and communication.
He learned that lesson unintentionally and the hard way. Coming from a mainland European background, he expected the U.K. and U.S. to operate similarly because they share a language. “There are very significant differences, even though the language might be the same,” says Einhaus. What looked like a clean, scalable message from headquarters could feel awkward on the ground. The fix, in this case, was to adjust the framing so it matched the local context.
It was a formative experience that shaped his approach to enterprise data programs that span markets. A global strategy can define the destination, but regional leaders need room to tailor the route. And when teams feel safe providing feedback, misalignment can be corrected early. In Einhaus’s case, trust enabled candor. His U.K. team could say, in effect, this will not work here.
Translating complexity into business decisions
AI, modern data platforms, and governance are now table stakes in large organizations, but the value of those investments is often lost in translation. Einhaus calls the underlying machinery a “necessary evil,” not because it is unimportant, but because it is not what business leaders ultimately buy into.
“The interesting piece for the business leaders is what’s in it for me?” he says. “What insights do I get that I didn’t have before? What can I do tomorrow that I was not able to do today?”
This is where many enterprise data and technology efforts stall. Leaders present roadmaps full of tooling choices, architectural diagrams, and model details, then wonder why business sponsorship is uneven. Einhaus argues that the narrative must start with decisions and outcomes. Technical teams can and should manage platforms, integration patterns, and operational resilience. But business partners need a clear line from data to action.
Some leaders want to understand the mechanics of AI, while others want confidence that the company is not falling behind competitors. Either way, the discussion has to center on impact. “Basically, will it or will it not hit the top or bottom line?”
Governance is not optional, and it is not only IT
The fastest path to AI disappointment is assuming governance is someone else’s problem. “They need to be aware that there is accountability on the business side as well,” he says. “You are very much responsible and accountable for the business data that is being used within the AI models. And therefore, if that is garbage, then don’t complain if your model spits out garbage, because the underlying data that was used is also your responsibility.”
Einhaus frames governance as a quality system that protects credibility. The business wants speed and insight, and governance makes both sustainable by ensuring data is accurate, consistent, and used in line with privacy expectations.
It also acknowledges reality. Many companies are trying to build the future while fixing the past. “A lot of companies right now are spending money both on building the capabilities for the future and on having to address their legacy systems and clean up some of the sins of the past,” he says.
A roadmap that starts with business outcomes
Einhaus starts with an inversion: begin with the business strategy, not the data estate.
“I really always try to say business strategy, business outcome, and then derive a data capability,” he says. That sequence forces discipline. If the business outcome is increased profitability in a set of markets, the data strategy must enable the capabilities required to manage that goal, such as understanding profitability at the broker and client level. If the organization cannot create the capability, the strategy is misaligned, no matter how modern the platform looks.
From there, he maps capabilities onto a roadmap that functions as an execution plan, sequencing dependencies and ensuring teams know what must be true before value can be delivered. It is a practical antidote to “data strategy for the sake of data,” which he considers a dead end. “We are cost centers, we are not profit centers,” he says, describing the mandate of technology and data functions. Their purpose is to support the business, not to showcase tools.
Trust and calm leadership determine whether strategy sticks
“Regardless of how loud, chaotic, and stressful the situation might be, if you want to lead your team through it, you have to be able to stay calm and make rational decisions,” he says. The perspective reflects lessons shaped by his experience in the Austrian Armed Forces, where composure and decisiveness are essential in chaotic environments. In complex enterprises, waiting for more and more information can be more damaging than acting with the data you have right now.
For global organizations trying to transform through data, that leadership posture is a strategic advantage. Enterprise data strategies depend on shared accountability, cross-functional partnership, and the willingness to adapt without losing the plot. The leaders who succeed are the ones who can hold a global ambition steady while letting local teams shape how it becomes real.
Just as important is trust. “If they don’t trust you, they will not follow you. If you can’t trust people to have your back, then you’re always going to be distracted from the actual task,” he adds.
Follow Yorck F. Einhaus on LinkedIn for more insights.
