Enterprises worldwide are still making big investments in data, analytics, and AI. But for some, strong technology and uneven results are the result. For Pier Martin, VP Data and Analytics at ZEAL Network SE, the issue is rarely the tooling itself. Instead, it is the way teams operate within the organization.
Martin describes this capability layer as the data operating system. The term deliberately echoes software architecture, but the focus is human. It reflects how teams prioritize work, communicate insight, and position themselves as partners to the business.
“We invest in tooling, we invest in AI, but are we investing in our people? Are we investing in the operating system that allows our teams to create value and communicate effectively,” Martin says. “For me, that is what the data operating system really is. It is how teams function to deliver value, not just how they use the next shiny tool.”
The idea resonates with many senior leaders who increasingly recognize that technical maturity does not automatically translate into organizational influence. The most sophisticated stack cannot compensate for weak alignment between data and business priorities.
Why Strong Tech Stacks Still Fall Short
Technology is essential, of course. But Martin cautions against treating it as the central strategy. Tools enable insight generation, but they cannot (yet) drive organizational context or shape decision-making dynamics.
“Technology is meant to be an enabler, but our biggest tool is still our knowledge, our context, and our ability to translate insight into action. Tools do not do that for us right now. It is important to have a great tech stack, but it is just one part of the equation.”
Frameworks designed to structure prioritization can also introduce unintended rigidity. Scoring models may indicate where effort should be allocated, yet they rarely capture political nuance, timing pressures, or dependencies that shape real-world decisions.
Martin argues that effective prioritization requires dialogue. Data leaders must understand what matters to stakeholders beyond what appears measurable on paper. Without this perspective, teams risk delivering technically impressive outputs that fail to influence outcomes.
Speaking the Language of the Business
One of the most consistent barriers to impact lies in translation. Data teams often communicate progress using technical indicators that fail to resonate with executive audiences.
Martin points to a familiar scenario when presenting to finance leadership.
“Saying our code quality is 30 percent better does not speak to an executive, especially not a CFO. Saying that improvement reduces our cost by $200,000 a year is a completely different conversation,” Martin says. “We have to translate technical progress into business terms so that the value is clear in that moment.”
The shift requires more than presentation skills. It requires a reframing of how success is defined. Metrics must reflect organizational priorities, not only technical excellence.
This distinction is particularly important as scrutiny on data investment increases. Executive teams are asking sharper questions about return on investment and operational efficiency. Leaders who can connect insight to financial outcomes are more likely to sustain long-term support.
Storytelling as a Strategic Capability
Communication does not end with metrics. Martin emphasizes the importance of narrative structure in shaping how insights are received. Many teams still present analysis as they were trained academically, beginning with methodology before revealing conclusions.
This approach, while logical to analysts, often dilutes urgency for decision-makers.
“We often approach it the way we were taught in school: show all the work first, then put the answer at the end. But that doesn’t drive the needle in business,” Martin points out. “If you start with the number, the tension point, what we found and what we want someone to do, that storytelling framework becomes much more powerful.”
Storytelling also contributes to trust. When business stakeholders understand how conclusions are reached, they are more willing to engage in iterative improvement. This trust becomes particularly valuable when initiatives encounter challenges.
Rather than interpreting setbacks as failure, organizations with strong relationships view them as opportunities to refine direction.
Signals of a Healthy Data Culture
Martin defines healthy data cultures as environments where collaboration begins early. Data teams are included in both the framing of problems and the validation of solutions. And as result, the relationship between technical specialists and business stakeholders becomes more fluid, enabling better communication.
Curiosity plays a central role. Data professionals who seek to understand commercial drivers tend to identify more relevant opportunities. Equally, business leaders who invite analytical input earlier create conditions for stronger outcomes.
Warning signs often appear in language. When teams describe each other as obstacles rather than partners, the operating system requires attention. These signals indicate translation gaps that undermine shared accountability.
Investing in the Operating System
Improving how teams operate does not necessarily require additional budget or new infrastructure. It often begins with reconsidering how priorities are framed, how insights are communicated, and how relationships are built across the organization.
“I hope people leave understanding they are not alone on this journey buty, but also recognizing they may need to invest more in their people and their mindset. It does not cost more money, but it can significantly improve the return on investment they see from data. The opportunity is not another tool, it is how we operate.”
As data functions continue to mature, competitive advantage may increasingly depend on leadership capability rather than platform selection. Refine operating models alongside technology strategies is key to achieving this.
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Join us in Berlin on April 28 at CDAO Germany to explore these themes and more, alongside senior leaders shaping the future of data, analytics, and AI.