Let me be direct with you: most data strategies fail before they ever get a chance to succeed. Not because of bad technology, not because of the wrong tools, and not because the data team isn’t talented. They fail because the people who run the business aren’t personally in the room when data decisions get made — and they’re not looking at the outputs when decisions do get made.
I’ve worked with enough organisations to recognise a pattern. A company invests heavily in a data warehouse, hires a team of analysts, and maybe even brings in a Chief Data Officer. Reports get built. Dashboards get deployed. And then… nothing changes. The same gut-feel decisions keep getting made. The same spreadsheets keep getting passed around in meetings. The data platform becomes an expensive back-office function that nobody in the executive team quite knows how to explain to the board.
This is what I call the “IT trap” — and it’s more common than most leaders want to admit.
Why Data Strategy Gets Buried in IT
There’s a logical reason this happens. Data starts in IT. Someone has to build the infrastructure, manage the pipelines, maintain the governance frameworks. Those are legitimate technical responsibilities. The problem isn’t that IT is involved — the problem is when IT becomes the owner of what is fundamentally a business strategy.
When data strategy sits entirely within IT, it gets evaluated on IT terms: uptime, query speed, storage costs, pipeline reliability. These are real and important metrics. But they are means to an end, not the end itself. The end — the actual purpose of a data strategy — is better business decisions. Faster market responses. Clearer understanding of your customers. More confident resource allocation.
None of that lives in a server room. All of it lives in the decisions your leadership team makes every single day.
The C-Suite Accountability Question Nobody Wants to Answer
Here’s a question I ask every senior leader I work with, and I want you to sit with it honestly before you read on:
In the last 30 days, how many significant business decisions did you personally make that were meaningfully shaped by data?
Not “data was available.” Not “someone on my team looked at the numbers.” I mean: you, personally, looked at an output, understood what it was telling you, and let it inform or challenge your thinking before you made a call.
If the answer is zero — or close to it — that tells you more about your organisation’s data culture than any audit, maturity assessment, or consultant’s report ever will.
The organisations that consistently extract real value from data share one defining characteristic: their C-suite is personally invested in the outputs. Not managing the technology, not attending quarterly data reviews as a formality, but actively using what the data reveals to stress-test assumptions, challenge legacy thinking, and make better calls.
What “Personally Invested” Actually Looks Like in Practice
I want to be careful here because this phrase gets misused. Being personally invested in data doesn’t mean the CEO should be writing SQL queries or understanding the difference between a star schema and a snowflake schema. That’s not their job, and it would be a waste of their time.
What it does mean:
Executive teams set the questions, not just the budgets. The most data-mature organisations I’ve seen have leadership teams that come to their data function with hard business questions — “Why did our customer retention drop in Q3?” “Where are we losing margin in this product line?” — rather than just signing off on infrastructure spend.
Data outputs appear in strategy conversations. When a board is reviewing growth plans or market entry decisions, they’re looking at actual analysis, not just financial projections built on historical assumptions. The data is a participant in the conversation, not a footnote.
Leaders push back using evidence. When a business unit head proposes a new direction, the healthy challenge isn’t “prove it works” — it’s “what does the data say?” This only happens if leaders are literate enough in data to ask that question with confidence.
Accountability flows both ways. Just as the data team is accountable for the quality and accessibility of data, the business is accountable for using it. This is a reciprocal relationship, and it breaks down when only one side holds up their end.
The Real Cost of Leaving Data in IT
I’m not going to dress this up. If your organisation has been investing in data capability for more than 18 months and you still can’t point to clear, measurable improvements in decision quality — that’s a leadership problem, not a data problem.
The cost isn’t just the wasted spend on tools and talent, though that’s significant. The real cost is competitive. Markets move faster than they did five years ago. Customer behaviour shifts in ways that traditional intuition doesn’t catch quickly enough. Supply chain disruptions, pricing pressure, talent markets — the organisations navigating these challenges better are doing so, in part, because they’re closer to their data.
Being a data-driven organisation is not a technology aspiration. It is a competitive necessity. And it cannot be delegated entirely to a function that reports three layers below the people making strategy.
What Needs to Change — and Where to Start
If you’re reading this and recognising your own organisation in what I’ve described, the good news is that the fix doesn’t start with a new platform or a bigger data team. It starts with a conversation at the leadership level.
Specifically, I’d suggest three starting points:
Audit your decision log. Look back at the last quarter’s major decisions. For each one, ask whether data played a genuine role or whether it was used post-hoc to validate a decision that had already been made. Be honest about what you find.
Move data into your leadership rhythm. If data only appears in scheduled reporting cycles, it’s reactive. Build it into how you run strategy sessions, how you evaluate performance, and how you challenge assumptions in real time.
Make executive data fluency a priority. This doesn’t mean turning your CFO into a data scientist. It means ensuring your senior team has enough literacy to ask good questions, challenge weak analysis, and understand the difference between a metric that matters and one that’s just easy to measure.
The Bottom Line
I’ve said this to clients directly and I’ll say it here: if your data strategy lives in IT, it will eventually die in IT. Not because your IT team isn’t doing their job — they probably are. But because no technical capability, however well-built, survives without demand from the business.
That demand has to come from the top.
The organisations winning with data right now are not necessarily the ones with the most sophisticated tech stacks. They’re the ones where the people running the business have decided — personally and collectively — that data is how they want to operate.
That decision belongs in the boardroom. Everything else follows from there.