The True Cost of Data Projects (and How to Get Better ROI)

You’ve got data. You know it has value. But turning that value into real returns? That’s where most organisations hit a wall.

At face value, data projects seem simple to scope. You look at what needs to be done, calculate the hours, find someone to do the work, and away you go. But if you’ve been through this before, you know it’s rarely that straightforward.

Many teams underestimate what it really takes to deliver value from data. What looks like a low-cost project often ends up going over budget, dragging out for months, or landing with a thud instead of impact. And by then, the true cost is far higher than expected and the project fails to achieve the desired ROI.

So, what’s going wrong?

The real cost isn’t just financial

When organisations talk about return on investment, they tend to look at the obvious numbers: salaries, hourly rates, invoices. But that’s only part of the picture.

The bigger costs are harder to see. These are the ones that quietly eat into your ROI:

• Time is money. A project that drags on for months or years is not delivering value.
• Fixing mistakes is expensive. Poor quality data work often needs to be redone, and that takes extra time and budget.
• Delays create opportunity cost. While your project is stalled, competitors may already be using their data to make better decisions, improve operations, or win customers.
• Rework kills momentum. Teams lose motivation when they have to keep starting over or explaining the same issues again.
• Missed insights slow growth. When decisions are based on incomplete or outdated data, opportunities can slip through the cracks.
• Opportunity costs. When your team is focused on a new data project, they aren’t performing the core work that keeps your business humming and allows other teams to pursue their own projects.

These costs don’t show up on a balance sheet, but they absolutely affect your bottom line.

Skill gaps slow things down

One of the biggest hidden costs in data work is trying to do too much with too little. Hiring a data analyst or a contractor can seem like a good way to get started, but no single person can cover everything.

Modern data work spans analytics, engineering, cloud infrastructure, governance, AI, and more. Expecting one hire to deliver across all these areas is unrealistic. When those skill gaps show up mid-project, they can create major delays, technical debt, or worse, unusable outputs.

What different approaches really cost

There’s no single right way to resource a data project. The best approach depends on what you need, how quickly you need it, and what internal capability you already have. But it’s important to go in with a clear understanding of what each option can realistically deliver, and where the risks are.

We’ve seen organisations try to manage data projects entirely in-house to save money, only to hit roadblocks when their internal teams are stretched or missing key skills. By the time they bring in outside support, they’ve already lost valuable time, budget, and momentum.

Here's a quick comparison chart to help you choose what’s right for your organisation:

Choosing the right approach depends on your goals. If you’re looking for quick reporting fixes, a freelancer might be fine. If you're dealing with complex infrastructure or cloud migration, internal teams may need external support to move quickly and avoid blockers. And if you're chasing a major transformation but don’t have six months to write a strategy document, a local partner who can deliver and adapt as you go might be the better fit.

You also need speed and structure

Delivering ROI isn’t just about what gets built. It’s about how fast you can show value, and how easy it is to keep that momentum going.

That’s where a lot of in-house teams struggle. They’re often caught between reporting requests, system upgrades, and constant fire fighting. Strategy takes a backseat, and even the best ideas end up in limbo.

External teams or specialist partners are structured to move. They’re focused on outcomes, not distractions. And they can often deliver progress much faster than internal teams with split priorities.

What we see in the real world

Here are a few patterns we’ve seen again and again:

• Internal teams with good intentions but no bandwidth. Projects get started, then pushed back week after week as urgent tasks pile up.
• One-person data teams trying to cover too much. A talented hire gets overwhelmed with conflicting expectations and slow-moving decisions.
• Contractors brought in for quick wins but left without a plan. Work gets done, but no one knows how to build on it once they’re gone.

Each of these starts with the goal of being cost-effective. But in the end, they often cost more in time, budget, and missed opportunity.

How do you actually improve ROI?

It starts with shifting your mindset. Data isn’t a cost to control. It’s an investment that should deliver measurable value, but only if it’s managed well.

That means being clear on what you’re trying to achieve, getting the right mix of skills to deliver it, and setting your projects up to show progress early, so value is delivered incrementally, not just at the finish line. The longer you wait for results, the harder it is to keep momentum and support.

Your data strategy doesn’t need to be massive. It just needs to be effective, repeatable, and focused on outcomes that matter.

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Want help figuring out how to get more value from your data? Let’s talk.