People and Purpose: The Unsung Heroes of Your Data Strategy

When it comes to building a data strategy, what’s the first thing you think of?

If you said technology, platforms, or AI, you’re not alone. Most people do. But here’s the thing, the most common reason data strategies fail isn’t the tech. It’s the people.

It’s the part that’s most often underestimated, overlooked, and frankly, the most likely to make or break your entire strategy. Get the people part right, and you’re building on solid ground. Get it wrong, and even the slickest tech stack will fall flat.

The Most Important Pillar

When we work with organisations, through our data consulting services, on their data strategies, we’re clear about something from the very start: the people pillar is the most important one.

It sounds obvious. Of course, your people matter. But when you actually look at how most data strategies get put together, there’s a pattern, and it’s a costly one.

Too often, data strategy gets handed to IT alone. This is an issue not because IT aren’t brilliant at what they do, but because the assumption is that since they own the data platforms that house the data, they must own the whole picture.

The challenge? IT teams aren’t living and breathing the data challenges that exist across the rest of the business every single day. They can tell you a lot about what’s broken at the platform level. But the data insight that really shapes a strategy, the nuance, the frustration, the workarounds people have quietly built into their daily routines, that only comes from the people doing the work.

That’s why the people pillar matters so much. Without it, you’re left with a strategy that reflects one lens. And a strategy built on one lens is set up to fail.

Debunking the IT Myth

Owning a platform and owning data are two completely different things.

IT might look after the infrastructure. But the commercial team owns the CRM data. Finance owns the financial data. HR owns employee data. Marketing owns campaign data. And so on. Each of those teams has a responsibility for the quality, accuracy, and integrity of the data flowing through their systems.

A good data strategy makes that explicit. It draws a clear line between platform ownership (a technical responsibility) and data ownership (a business responsibility). Without that distinction, accountability falls through the gaps, and data quality suffers as a result. As everyone in IT knows, rubbish in, rubbish out. Unless the right people in the business take ownership of the data entering those systems, no amount of clever technology will fix it.

Your fingerprints should be all over it

Here’s what a genuinely effective data strategy looks like in practice: the people across your business can look at it and recognise themselves in it.

When we run data strategy workshops as part of our data strategy consultancy, we talk to as many people across the organisation as possible. Finance, sales, marketing, HR, operations – whoever the data-generating heartbeat of that business is. We keep workshop groups deliberately small, around five to six people, so that everyone gets heard. Not just the loudest voice in the room.

What we’re looking for is the real picture. How is each team using data today? What’s working? What’s costing them time? What decisions are they trying to make, but can’t, because the data isn’t there or isn’t trustworthy?

When people see their specific challenges reflected back in the strategy, something shifts. They’re not passive recipients of a plan someone else made. They’re invested. They’re bought in from day zero, which really matters when it comes to execution.

The hidden cost no one talks about

One of the most eye-opening things that comes out of these workshops is just how much time people are spending wrangling data manually.

Typically, we find an 80/20 split – 80% of time going to fetching, cleaning, and stitching data together, and only 20% actually spent on analysis and decision-making. System A doesn’t talk to System B. Someone in Finance spends two hours every morning pulling together a report in Excel or Power BI. And when that person is on holiday? The process just… doesn’t happen.

These single points of failure are almost invisible at a strategic level. IT might know that two systems aren’t integrated. But they often don’t know that it’s costing someone three hours of their day, or that five other people are dependent on the output.

Good workshops surface this. The same name starts coming up again and again across different conversations. That’s your signal. That’s a risk the strategy needs to address.

The goal is to flip that ratio. 20% sourcing. 80% insight. That’s where the real value lives.

Mapping people to the maturity model

Not every part of your organisation is at the same point in its data journey. And that’s completely normal.

We use a four-level Data Maturity Model to map where different teams are, ranging from level 0 (Ad Hoc, no formal processes, siloed and unstructured data) through to level 3 (Transformational, data as a strategic asset, powering AI-driven decisions and innovation).

Finance teams, for example, are often more data-literate than other areas. But even then, we frequently find manual Excel processes quietly running important decisions behind the scenes, which nudges them back down the maturity scale. Operations might have a fantastic working relationship with an internal data team and be further along than expected.

Mapping each area against the maturity model does two things. First, it shapes the priority order of the strategy, focusing effort on the areas that need it most. Second, it makes the strategy feel fair and inclusive. No team feels forgotten. Those that are already in good shape can see clearly where they’re headed next. Those that are earlier in the journey understand why they’re a priority now.

The platform isn’t a silver bullet

Let’s talk about something we see fairly often: organisations that decide they need a data platform and then want to sprint straight to implementation.

Sometimes that’s the right call. If a platform is going offline and data needs to be archived urgently, a technical solution is exactly what’s needed.

The whole point of a data strategy is to avoid the knee-jerk reaction of throwing technology at a problem. Because the reality is deploying a new platform won’t automatically fix business process issues. It won’t resolve a CRM system that was never designed for the size of organisation you’ve become. It won’t address the fact that three different teams use the same system in completely different ways, with no shared governance in place.

Technology is part of the answer. But the softer side of the strategy – data ownership, stewardship, process redesign, change management – that’s where the real transformation happens.

See where your organisation stands

People and purpose aren’t soft, optional extras in a data strategy. They’re the foundation everything else is built on.

If you’re not sure where your organisation sits on the data strategy journey or you want to understand what a people-first strategy could look like for your business, a Data Discovery Workshop is a great place to start

We’ll assess where you are today, identify the gaps, and give you a clear, practical roadmap for what comes next. No jargon, no hard sell. Just honest expertise.