When people ask me what I actually do as “the data as a product guy”, I often start with a simple line:

“My job is to make sure data feels less like a warehouse shelf and more like a good app: easy to find, easy to use, and actually solving a problem.”

That’s really what data as a product is about.
Not buzzwords. Not yet another framework.
It’s simply: treating data like something people genuinely want to use.

In this post I’ll walk you through what that means, and why product thinking is such a game-changer for data teams, business stakeholders, and anyone who’s tired of fighting with half-baked dashboards and cryptic tables.

Why “just having data” isn’t enough

Most organisations today don’t suffer from a lack of data.
They suffer from a lack of useful, trustworthy, usable data.

Maybe this sounds familiar:

• You have multiple dashboards answering “almost” the same question.
• Nobody really knows which table is the “official” one.
• Definitions like customer, churn or active contract vary by team.
• Business users keep asking IT for extracts “just this one time” because they don’t trust the existing ones.

Technically, there is data.
But from a user perspective, it doesn’t feel like a product. It feels like raw material.

That’s the gap data as a product tries to close.

So… what is a “data product” in plain language?

Forget for a moment about technology, tools, and architectures.

A data product is:

A piece of data with a clear purpose, a clear owner, clear quality expectations, and a simple way to consume it.

It’s not just a table, API, or report.
It’s the combination of:

• Who it’s for (its users)
• What problem it solves (its use-case)
• How you access it (its interface)
• How good it is (its quality & guarantees)
• Who looks after it (its owner)

If I say, “This is our Customer 360 data product,” I should be able to answer questions like:

• Who uses it? Sales, marketing, customer service?
• What decisions is it meant to support?
• Where do I find it? (URL, catalog entry, API endpoint…)
• What’s inside? (schema, business definitions, example queries)
• How fresh is it? How often is it updated?
• Who do I talk to if something breaks or I need an enhancement?

If I can’t answer those, I don’t really have a product.
I have a dataset on a drive.

What is “product thinking” and why does data need it?

Product thinking is the mindset that great product teams use to build apps, services, and experiences people love.

Instead of starting with “What can we build?” they start with:

“Who are we building this for, and which problem are we solving for them?”

Applied to data, product thinking shifts us:

• From tables to outcomes
• From projects to lifecycles
• From deliverables to users
• From “done at go-live” to “continuously improved based on feedback”

This matters because most data initiatives fail not for technical reasons, but for human ones:

• Nobody uses the thing that was built.
• It’s too hard to find or too hard to interpret.
• Trust is lost after a few bad experiences.
• Ownership is unclear, so nothing really evolves.

Product thinking gives us a way to design data so that it is:

• Valuable – solves a real problem
• Usable – easy to discover and understand
• Reliable – behaves in a predictable way
• Evolvable – has a roadmap, not just a deadline

The core principles of product thinking for data

Let’s make it concrete. Here are the aspects I focus on most as a data-as-a-product person.

1. Start with the user, not the schema

The traditional way:

“We have a CRM, ERP, and Web logs – let’s model them into a big unified data model.”

The product-thinking way:

“Sales managers need to understand pipeline health and conversion rates. Customer care wants a 360° view to handle calls faster. What data product would really help them?”

Instead of designing by source system, we design by decision, workflow, and user:

• “Lead-to-opportunity funnel product” for sales
• “Customer 360 timeline product” for service
• “Claims analytics product” for operations

The schema is still important, of course. But it’s a consequence of the problem, not the starting point.

2. Clear ownership and accountability

Every good product has a product owner or product team.

For data, that means:

• Someone is accountable for definitions (“this is what ‘churn’ means here”)
• Someone decides on priorities (“we add this attribute next, deprecate that metric later”)
• Someone cares about user feedback and adoption

Without ownership, we get:

• Zombie dashboards: nobody maintains them, nobody dares to delete them.
• Conflicting numbers: nobody can explain why.
• Stalled improvements: every change request disappears into a black hole.

With ownership, we can say:

“The Customer 360 data product is owned by the Customer Insights team. Here’s their contact, roadmap, and documentation.”

That’s a huge cultural shift—and a very healthy one.

3. Contracts, quality, and reliability

When you use a good app, you implicitly rely on a contract:

• It loads when you need it.
• Buttons work.
• Data is up to date.

Data products need the same kind of contract.

Typical elements of a data product contract:

• Schema and business definitions
• Update frequency and freshness targets
• Quality criteria (e.g. completeness, valid values, no duplicate IDs)
• SLOs (e.g. “Daily snapshot ready by 07:30 CET, 99% of days per month”)

This doesn’t need to be heavy or bureaucratic. But there should be something more than “we’ll try our best”.

Why? Because:

• Downstream teams build logic on top of it.
• Machine learning models depend on consistent structures.
• Executives make decisions based on numbers they expect to be right.

Product thinking pushes us to make those expectations explicit and to design the data product to meet them.

4. Discoverability and user experience

A product nobody can find might as well not exist.

For data, UX is things like:

• Can people discover the data product in a catalog or portal?
• Do they have a short, human-readable description?
• Is there a simple “Getting Started” example?
• Are the most common questions documented? (“If you’re trying to do X, start with Y.”)

This is where many data teams underestimate the work:

We spend months building pipelines and models, then ship a bare table or a technical API and expect people to just “figure it out”.

Good data products come with onboarding:

“Here’s what this product is, when to use it, how to join it with other key products, and a few example queries to get you going.”

It’s the difference between being handed a box of parts and a ready-to-use tool.

5. Lifecycle and roadmap (not one-off projects)

In project mode, success is usually “we deployed it”.

In product mode, success is:

• Are people actually using it?
• Are they getting value from it?
• What should we improve next?

That means every data product should have at least a lightweight lifecycle:

• Launch – initial version that solves a clear use-case
• Learn – gather feedback, track usage, spot issues
• Iterate – refine definitions, extend attributes, improve performance
• Retire or replace – when it’s no longer needed or a better product exists

This is especially important as organisations grow their data landscape. Without lifecycle thinking, you end up with hundreds of overlapping “products” that are effectively abandoned.

6. Measuring value, not just usage

Product thinking pushes us to ask:

“How do we know this data product is successful?”

Some simple but powerful signals:

• Adoption
  How many teams or users consume it? Is usage growing?
• Impact
  Did it reduce manual reporting work? Speed up a process? Improve a KPI?
• Trust
  Do people prefer this source over local extracts and shadow Excel files?
• Feedback
  Do users come back with ideas for improvement, or do they quietly work around it?

The goal is not to gamify vanity metrics, but to see where to invest.
Some data products deserve more love, others can be simplified or archived.

Why this matters even more in an AI world

You can plug a large language model into a chaotic data landscape, but you won’t get magic. You’ll get:

• Confident answers on top of bad data
• Conflicting truth depending on which source the model picked
• Compliance headaches if access rules and definitions aren’t clear

AI is only as trustworthy as the data products it relies on.

If you want AI to:

• Explain business KPIs
• Recommend actions
• Generate reports
• Support agents in real time

…then you need well-defined, well-governed, productised data behind it.

Data as a product is the foundation that makes AI useful, safe, and aligned with the business—instead of just impressive in demos.

How you can start, today

You don’t need a full-blown reorganisation to begin thinking this way. You can start small:

1. Pick one important data asset
   Something people already use a lot (or complain about a lot).
2. Give it a name and a purpose
   “Sales Pipeline Analytics”, “Customer 360 for Service”, “Claims Overview”.
3. Define an owner
   A person or small team that cares about its success.
4. Write a one-page “product card”
   • Who it’s for
   • What problem it solves
   • Where to find it
   • Basic contract (schema, refresh, quality expectations)
   • How to request changes
5. Ask for feedback
   Talk to 3–5 real users and ask: “Does this help you? What’s missing?”

Then iterate. That’s product thinking in action.

Closing thoughts from the “data as a product guy”

For me, data as a product is not a trend.
It’s a way to make sure that all the work we do in data actually lands:

• In better decisions
• In smoother processes
• In happier users
• In safer, more trustworthy AI

If you start treating your core data assets like products – with users, purpose, ownership, contracts, and a roadmap – you’ll notice something interesting:

The conversations change.

Suddenly it’s less about “Can you pull me this extract?”
And more about “Can we extend this product so it covers my new use case?”

That’s when you know you’re moving in the right direction.

If you like this perspective and want to go deeper into patterns, contracts, and concrete examples, let me know – the “data as a product guy” always has a few more stories to share.