Historically, whenever I have seen a company investing in a data transformation program, the focus is almost always on technology. What platform are we using? Do we go with New Tech A or New Tech B? Should we push everything to the cloud or should we stay on-prem?

These are important decisions, but they’re not what makes or breaks a data transformation.

What really matters, what determines whether you’ll see real value, is adoption. If people don’t use the data, don’t trust it, or don’t know how to work with it, then all the best tools in the world won’t make a difference.

The hard truth about data adoption

Technology and data design are the easy parts. The hard part is getting people to change how they work with data. And if they don’t, and don’t use the solution, nothing else matters.

Without adoption:

• Data remains locked in silos — because people keep defaulting to their old ways of working.
• Self-service analytics doesn’t work — if users don’t trust or understand the data, they won’t use it.
• Governance breaks down — because teams start bypassing rules and sticking with spreadsheets.
• Business teams see data transformation as an IT project, not something that helps them.

I’ve seen companies spend millions on modernizing their data stack, only to find that people still copy and paste numbers into Excel because it’s what they know. That’s when leadership starts asking, “Why haven’t we seen the benefits yet?”, and the answer is simple: because people weren’t brought on the journey.

The real challenges to adoption

The biggest barriers to adoption come down to people, process, and culture, not technology.

People: If they don’t see the benefit, they won’t change

• Business teams don’t adopt new data tools because they don’t see how it makes their life easier.
• If the transformation is driven by IT, it feels like something being done to them, not for them.
• Training is often an afterthought; people are expected to just “figure it out.”

Process: If it doesn’t fit how people work, it won’t stick

• Data platforms are often designed without a clear understanding of how teams actually use the data.
• Self-service analytics fails when users don’t have clear guidance on what data to use and how to interpret it.
• Data governance can feel like a barrier rather than an enabler if it’s too restrictive.

Culture: If leaders don’t push it, people won’t buy in

• Old habits die hard, people will keep using Excel and manual processes if no one challenges them.
• If leadership isn’t fully behind data adoption, business teams won’t see it as a priority.
• The company says it’s “data-driven,” but decisions are still based on gut instinct.

How to drive adoption the right way

So how do you fix this? It comes down to putting adoption first, not as an afterthought. Here’s what I’ve found works:

1. Involve business teams from day one

Too many data transformations are IT-led, with business teams only brought in at the end to “adopt” a system they had no say in designing. That approach guarantees resistance.

Instead, adoption starts with co-designing solutions with the people who will actually use them:

• Identify real pain points early and show how the data solution can solve them.
• Run pilot programs, or focus on low-hanging fruit, where business teams can try out solutions before they’re rolled out company-wide.
• Create data champions, people within departments who can advocate for change and support their peers.

The goal isn’t just to roll out technology; it’s to make people feel invested in the transformation.

2. Make training an ongoing effort, not a one-time event

Most organizations treat training as a checkbox, one session, a few slide decks, and they assume people will figure out the rest. That doesn’t work.

• Training needs to be ongoing and role-based. A finance analyst, an underwriter, and an actuary all work with data differently, so their training should reflect that.
• Just-in-time learning is critical, help resources should be embedded within the tools people use, not buried in documentation no one reads.
• Data literacy programs should be a core part of business training, not just for “data teams.”

The goal is not just to train people on new tools but to build their confidence in using data itself.

3. Build trust in the data before asking people to use it

This is where a lot of self-service analytics or AI programs fail. If users don’t trust the data, they’ll ignore it, no matter how sophisticated the platform is.

• Make data quality and lineage transparent. Show users where the data comes from, how it’s maintained, and what governance is in place.
• Proactively fix issues. If people report problems with the data and nothing changes, they’ll assume all the data is unreliable.
• Use real-world examples to show how better data leads to better decision-making.
• Build a trusted data foundation, not a blank canvas open to data challenges.

Trust is the foundation of adoption, without it, people will default back to what they know.

4. Balance data ownership with accessibility

Data needs ownership for quality, governance, and compliance, but ownership should not mean control.

• Every dataset or data product should have a clearly defined owner who is responsible for its accuracy, security, and access policies.
• Governance should enable data usage, not block it. If processes are too restrictive, people will find workarounds (often outside of governed systems).
• The mindset should shift from “owning” data to being accountable for it, business teams should feel empowered to use data while data owners ensure its integrity.

A well-governed data model doesn’t mean locking data away, it means making it usable in a trusted, structured way.

5. Track adoption and keep communicating the impact

If you’re not measuring adoption, you don’t know if your data transformation is actually working.

• Look at logins, report usage, and query activity. If engagement is low, you have an adoption problem.
• Communicate success stories and showcase teams that have gained real benefits from using governed data.
• Act on feedback, if people aren’t using the platform, find out why and adjust.

Adoption isn’t a one-time event; it’s something you need to monitor, refine, and reinforce over time.

The bottom line

You can buy the best data tools on the market, and build the most elaborate solutions, but your people aren’t using them, it’s wasted money.

Adoption needs to be a core part of any data transformation strategy, not an afterthought. That means:

• Bringing business teams along for the journey.
• Making sure the new ways of working actually fit their data use case needs.
• Building trust in the data foundation.
• Creating a culture where using governed, reliable data is the norm.

Get that right, and the technology will take care of itself. Get it wrong, and no amount of tech will save you.

*The views and opinions expressed in this article are my own and do not necessarily represent Enstar Group’s position or opinion.

About the Author:

At the helm of Enstar Group’s data strategy, David Tuppen’s role as Chief Data Officer encompasses spearheading data platform modernization and guiding enterprise-wide transformation initiatives. Championing enterprise data management and client solutions, he drives the realization of data’s full potential to underpin decision-making and operational excellence.

Tuppen’s previous leadership roles include organisations such as GFT Technologies, Wipro, and Athene, particularly in Data & AI and the Insurance sector, which has enriched his approach to customer-centric data solutions. His team’s commitment to delivering transformative data solutions aligns with his goal of propelling organizations to the forefront of data-driven innovation.