Global organizations often invest heavily in data platforms, advanced analytics, and artificial intelligence (AI) capabilities. Yet despite the investment, business leaders still ask a familiar question: why is value not materializing at the expected pace? For Yorck F. Einhaus, a former Chief Data Officer and Chief Information Officer with more than 20 years of experience leading enterprise data and AI transformations across global insurance environments, the answer is rarely technical. “High-performing global data teams don’t happen by accident,” he says. “They are intentionally designed, empowered, and aligned to business value.” Having built and led global data organizations of more than 200 professionals across North America, Europe, and Asia, Einhaus has identified three principles that consistently separate high-performing teams from underperforming ones.
Anchor Everything in Business Value
The first principle is clarity of purpose. Too often, data teams are structured around platforms, tools, or reporting lines. While these elements matter, they do not create performance. What drives results is a direct and visible link to business outcomes. “High-performing data teams are not built around tools or org charts,” Einhaus explains. “They are built around clear business outcomes.” Every team member, from data engineer to analytics lead, should be able to answer a simple question: how does my work create value for the customer, the partners, or the business?
When initiatives are explicitly tied to growth, profitability, risk reduction, or customer experience, then priorities sharpen, decision cycles shorten, and engagement increases. Teams are less likely to pursue technically elegant solutions that lack commercial relevance. In practice, this requires leaders to repeatedly connect data strategy to tangible impact. This includes: revenue uplift, cost efficiency, improved underwriting accuracy, faster claims processing, and reduced operational risk. The connection must be explicit and reinforced consistently. “When people understand the value they are creating,” Einhaus says, “performance accelerates.”
Design a Team of Teams Model
Global scale introduces complexity. Centralized control may create consistency, but it often slows execution and weakens business alignment. Fully decentralized models, on the other hand, risk fragmentation and duplication. Einhaus advocates for a team of teams operating model. “In a global environment, scale doesn’t come from central control,” he notes. “It comes from trust, shared standards, and local accountability.”
The model combines global alignment on governance, data standards, and architectural principles with empowered local teams that sit close to the business. Enterprise-level guardrails ensure consistency and regulatory compliance, while local teams retain the autonomy to innovate and respond quickly to market-specific needs. This structure increases speed and improves adoption. It enables experimentation where it matters most, without compromising enterprise coherence.
Invest in People, Not Just Technology
Technology enables transformation and people deliver it. Einhaus is unequivocal on this point. “High-performing data teams thrive when leaders listen, create psychological safety, and actively invest in skill development,” he says. In large global organizations, data professionals operate in complex environments with evolving tools, regulatory demands, and shifting business priorities. Sustained performance requires more than technical training. It requires leadership development, cross-functional exposure, and clear career pathways.
Encouraging experimentation, allowing room to learn from failure, and building strong data leaders at every level fosters ownership. When individuals feel trusted and supported, they take initiative. Innovation follows. This focus on people also strengthens retention, a critical factor in a competitive talent market.
From Asset to Advantage
When these three principles are in place, data becomes more than a reporting function or a compliance requirement. It becomes a strategic lever. “Focus on three things,” Einhaus advises. “Anchor everything in business value, design for collaboration through a team of teams model, and lead with a deep commitment to your people.” Organizations that get this right do not simply produce more dashboards or models. They make better decisions. They respond faster to market shifts. They allocate capital more effectively. They embed analytics into everyday operations rather than treating it as a separate capability.
In a global, AI-enabled economy, data is widely available. Competitive advantage comes from how effectively it is organized, interpreted, and applied. High-performing global data teams are not the result of superior tools alone. They are the product of intentional design, disciplined alignment, and sustained investment in people.
Connect with Yorck F. Einhaus on LinkedIn for more insights.
