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When we founded MetadataWorks, we weren’t just trying to create another tech company. We were trying to solve a recurring human problem:
Data existed: valuable, meaningful, often brilliant data, but nobody could find, understand, or use it.
I worked with organisations where:
- datasets remained invisible for years
- analysts recreated work because they didn’t know data already existed
- researchers made decisions based on fragments instead of full context
- catalogues existed but nobody used them
- data frameworks were written but never lived
The problem wasn’t capability. It wasn’t intelligence. It wasn’t effort. It was trust.
I wanted to build environments where people felt oriented, where discovery felt natural, where documentation felt like storytelling rather than bureaucracy, and where the act of using data felt like a partnership rather than an exam.
Metadata has traditionally been something that lets machines talk to one another — but the real value is when it helps humans understand one another too.
The Data User Journey
Over years of working with organisations of all sizes, I began to see a pattern in how people interact “well” with data. It didn’t matter whether the user was a clinician, researcher, analyst, policymaker, or data scientist — to achieve the best value, the journey was always the same;
And if you couldn’t help them navigate this journey, you’ve missed a whole lot of value. Let's look at each step of the journey and how an example user might work through it;
1. Awareness
User: Is there any data that can help me solve my problem?
Some estimates suggest that around 20% of business professionals’ time is spent just searching for data. That’s an enormous, largely invisible tax on the system.
If you run a data service, your first job isn’t access or governance — it’s awareness.
People need to know your data exists.
If they don’t know it’s there, they won’t use it.
It really is that simple.
2. Consideration
User: Okay — you have data that might help. But how do I know if it’s relevant to my problem?
Imagine shopping online with no filters. No sizes. No brands. No categories. Inconsistent labels everywhere. You’d give up in minutes.
Data works the same way.
If a data service can’t quickly help users judge relevance — what the data is, what it’s good for, what it isn’t, and whether it fits their use case — they won’t proceed.
Confusion kills curiosity.
3. Conversion
User: Should I invest my most valuable resource — my time — trying to access and use this data?
Most of us have lived this moment:
You’re sent an Excel file with bizarre column names, overlapping fields, and no explanation. You spend hours trying to work out the difference between two almost-identical columns. Eventually, you give up and promise Dave from the finance team a pint after work if he can run the report for you.
If a data service can’t persuade users that the data is worth the effort, one of two things happens:
- They don’t use it at all, or
- They use it inefficiently, consuming specialist time on work they could have done themselves
That might be fine if your business model is selling expert time. But for most public sector data services, it’s pure waste.
4. Adoption
User: How do I get up to speed with all of this?
On healthcare programmes I’ve worked on, large pharma teams would come in to interrogate datasets — and it often took three months before new team members felt confident enough to do anything genuinely useful.
Three months. Five people. £80k+ salaries.
There is enormous value in reducing that ramp-up time — not just financially, but psychologically. Confidence unlocks momentum.
5. Impact
User: I’ve built a great dashboard… or paper… or model. Now what? How does this actually change anything?
Data doesn’t create impact on its own. Even the best insights have limited value unless they’re acted upon. So the real questions become:
- How do insights reach decision-makers?
- How are they translated into action?
- How do you know whether the data actually made a difference?
A mature data service doesn’t stop at analysis. It actively supports the journey from insight to change — and measures what happens next.
6. Advocacy
User: That was genuinely helpful. I’ll be back — and I’ll tell others.
This is the quiet endgame.
Advocacy doesn’t come from perfection. It comes from trust.
A single moment of friction at any stage can derail the whole experience. Trust is hard-earned and easily lost.
Which is why the real work of data is not building systems that look impressive — but building experiences that feel intuitive, respectful, and human.
I’ll go into more detail in the next few chapters, with practical patterns and checklists for each stage of this journey.
So, how can I use the data user journey to get more from my data?
A simple but powerful way to apply this in practice is to run a short exercise with your team that identifies who your data users actually are, and captures their expectations and blockers at each stage of the journey. Start small: one product, one user, one journey
The key is that you don’t need to solve everything at once. In fact, the fastest way to surface meaningful insight is to map the journey for a single data product and a single user type end-to-end. That one journey will almost always reveal a set of friction points — and the interventions that remove them — that are generalisable across other products and other user groups.
You can run the worksheet yourself, or you can contact MetadataWorks to run the session for you - free of charge and with no obligation to work together in the future.