I still recall the effort it took, not that long ago, to build a proper marketing segmentation. The ETL work to pull and prepare the data alone took weeks. Then a dedicated team of analysts and data scientists spent months iterating on the models: testing, validating outputs, making sure the segments were actually actionable rather than just statistically elegant. It required specialist resources, deep expertise, and a significant time investment.
Today, a business analyst with the right AI tools can have a working segmentation model running in an afternoon. A marketing team can produce customer insights on demand, in less time than it used to take just to brief the data science team.
The technical barriers that used to shape data strategy are dissolving faster than most organizations realize. The question "what can we build?" is becoming irrelevant. And that changes everything about what data leadership needs to be.
Because when every team can build, the question shifts. It's no longer about capability. It's about direction. And that is a fundamentally different problem.
I came across the best description of this challenge in the most fitting way: pulling an Oliver Burkeman essay from my read-it-later list, which led me to Nicholas Carr's original piece on ambient overload. Carr draws a sharp distinction between two types of information problems. The old one was finding a needle in a haystack. The new one? Being confronted by haystack-sized piles of needles. Every one relevant. Every one potentially valuable. That's what data and AI leadership looks like today. Not a scarcity of options, but a crushing abundance of good ones.
The New Bottleneck Isn't Technology
For years, the limiting factor was capability. Can we integrate these data sources? Can we get the data clean enough to trust? Those were real constraints that shaped strategy by default. You prioritized what was possible.
AI is removing those constraints faster than organizations can adapt. When the technical ceiling lifts, what's left is the harder question: where should we actually focus? Which initiatives align with where the business is heading, and which ones are distractions dressed up as innovation?
I've seen this play out firsthand. A top strategy consultancy recommends focusing on data value. The right advice. But it stays in the boardroom deck. It never translates into how teams actually work with data. The shift I've been driving is to close that gap from two directions: focus governance requirements on data that has genuine strategic impact, and strengthen business ownership by replacing the complex technical language of data management with clear decision authority and accountability. One creates the push, the other creates the pull.
This isn't a technology question. It's a strategy question. And it's why the role of the data leader, whether Head of Data, Chief Data Officer, or whatever your organization calls the person leading data and AI, just became significantly more important.
The Distinction Most Organizations Get Wrong
The difference between a data value strategy and a data strategy is fundamentally misunderstood, quietly forgotten, or skipped entirely.
A data value strategy starts with the business. Where does the company need data to create competitive advantage? Which strategic bets depend on better information? It translates business ambition into data priorities.
A data strategy is the operational answer: how do we organize, govern, and technically deliver the data to get there? Architecture, tooling, data entities, quality standards. All valuable and necessary work.
Too many CDOs jump straight to the second without ever properly defining the first. They build beautifully organized data infrastructure with no clear connection to business outcomes. The conversation shifts from competitive advantage into IT roadmaps and platform decisions. Everything looks productive. Nothing moves the needle.
This happens because the CDO role too often skews toward technical leadership. But the crucial differentiator — the thing that separates a data leader who transforms an organization from one who merely manages its plumbing — is business acumen. The ability to sit in a strategy discussion, understand where the business is heading, and translate that into clear data priorities before a single technical decision gets made.
Why This Matters Now
The organizations that will thrive with AI aren't the ones building the most. They're the ones building the right things. And identifying the right things requires a data leader who starts with business value, not technical capability.
As someone who lives and works in a country where a significant part of the land sits below sea level, I've grown up understanding that you don't fight the water. You decide where it flows. The data leader's job isn't to dam the river. It's to know exactly where and how to steer the current.