We used to think data was the gold. Now we know it's the mine.

The problem isn’t that we don’t have enough customer data. Far from it. Most CRM teams are drowning in it — transactional data, behavioural data, channel engagement data, survey data, loyalty status, preferences, location, dwell time, NPS, the occasional emoji.

But here's the rub: most of that data sits idle. Stored. Archived. Passed between platforms like a hot potato no one quite knows how to peel.

The future of CRM isn’t about having more data. It’s about thinking differently about what we already have. And that begins with a deceptively simple question:

What if we treated data like a product?

The Case for Treating Data as a Product

Let’s flip the mental model. Imagine launching a product that nobody asked for, that no one can find, that’s hard to use, and whose benefits are unclear. You’d call that a failure — and rightly so.

Yet that’s exactly how many brands manage their data assets.

CRM teams have a front-row seat to the consequences. Campaigns that can’t segment beyond recency and frequency. Journeys that stall because consent data is locked in another system. Personalisation that relies more on guesswork than insight.

Thinking like a product manager brings much-needed clarity. It forces us to ask:

• Who’s the user of this data?


• What problem does it solve?


• How will they find and trust it?


• Can they use it without needing a PhD in SQL?

These are the same questions we’d ask when launching a new app, feature or service. Apply that lens to CRM data, and suddenly the path forward gets clearer — and more collaborative.

From Centralised Control to Federated Fluency

In many organisations, data is still governed like an exclusive club. Controlled by central teams, accessed only through tickets, and updated on a quarterly sprint — if you’re lucky.

CRM, by nature, operates in real time. Or at least, it wants to.

That’s why many forward-looking businesses are shifting away from centralised models and embracing a federated approach— what some call a “data mesh” mindset. This doesn’t mean chaos. It means putting data stewardship into the hands of the teams who are closest to the customer.

For CRM teams, that’s transformative. It means:

• Real-time access to the data that powers segmentation, triggers, and journeys.


• The ability to co-create data products with analytics, tech, and loyalty teams.


• A culture where data is no longer something you request — it’s something you own.

But this decentralisation only works when data is discoverable, usable, and governed.

That’s where data product thinking really shines.

5 Principles for Turning CRM Data Into Data Products

Whether you're launching a new segmentation model or building out a retention dashboard, the principles below are a solid starting point:

• Start With the Use Case

Don’t start with the data you have. Start with the value you want to create. Whether it’s reducing churn, increasing lifetime value, or improving welcome conversion, work backward from the outcome.

• Make It Discoverable

If only the analytics team knows it exists, it isn’t a data product — it’s a secret. Good data products should be searchable, documented, and available for others to reuse.

• Design for Reuse

That churn model you built for Beauty customers? Could it be adapted for Fashion? Data products that scale across use cases are where the ROI kicks in.

• Keep It Human-Usable

Not every CRM exec is a data scientist. Make the outputs accessible, intuitive, and visually meaningful. Think dashboards, not data dumps.

• Establish Trust Through Governance

Consistent naming, clear provenance, defined owners. Trust isn’t built through fancy UX — it’s built through transparency.

Small Slice, Big Win: The Thin-Slice Approach

One of the biggest blockers to CRM data activation is over-engineering. Projects get stuck in planning purgatory. Teams try to perfect the data layer before anyone sees a result.

Forget that!

Use the “thin slice” approach: build a narrow but fully functional data product that cuts through all layers — collection, processing, output, and usage.

Want to improve onboarding? Start with a thin slice:

• Collect just enough data to personalise the welcome series

• Build a segmentation logic

• Plug it into your CRM platform

• Measure engagement lift

Small wins like this prove value fast, generate internal momentum, and give the business a taste of what “good” looks like.

Data Products Are the Missing Link to AI-Driven CRM

Let’s talk AI — because you can’t not.

Everyone wants to plug AI into their CRM programme. Predictive churn. Next-best-action. Real-time optimisation. But AI is a hungry beast. It feeds on clean, structured, contextual data.

In other words: it feeds on data products.

Without high-quality, well-governed, and well-scoped data products, your AI models will either:

a) break,

b) hallucinate,

c) reinforce bias, or

d) all of the above.

Worse, they’ll erode trust in the CRM function.

With data products, however, AI becomes a value multiplier. You can build pipelines where AI is embedded within the product itself — for example, an AI model that takes CRM behavioural data and predicts optimal re-engagement timing, feeding it directly into journey orchestration.

This isn’t science fiction. It’s happening now — and CRM teams need to be part of that build.

The End Goal: CRM That Moves at the Speed of the Customer*

Customer expectations have outpaced campaign calendars. Real-time is table stakes. Relevance is currency. And data — the right data — is your engine.

By treating CRM data as a product, you bring it closer to the business, the customer, and the decision point. You make it useful, usable, and used.

You also shift the perception of CRM from a “cost centre that sends emails” to a strategic growth engine— one that connects insight to action in milliseconds, not meetings.

That’s the opportunity.

Not to collect more.

But to use what we already have — better.

*or maybe just slightly faster

By gianfranco cuzziol

Keywords: Big Data, CRM, Customer Loyalty

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