What is a Data Product and How does it differ from a Data Project?

In meetings, community talks, or through navigating data, you have probably come across the term data product. It is a concept — and an approach — that has been gaining traction in data teams across industries. But what does it actually mean? You will find out in this very module.

A data product is any output built from data that is designed to deliver value to its users, reliably and repeatedly. It is not a one-off analysis or a report produced for a single meeting.

Think of it this way: a spreadsheet someone pulls together to answer a specific question is data work. A self-refreshing dashboard that any team can consult at any time to monitor performance? That is a data product.

What makes something a data product?

A data product is a product build with data or around data with specific user goal. It comes with key characteristics:

• Clear Users: A data product is build with a specific audience in mind such as a customer service team spotting recurring issues
• Trustworthy: A data product is developed with reliable and accurate data and up to date where users can rely on
• Reusable: A data product is not built to answer a single question or serve a single team. It is designed to adapt — addressing new goals as they emerge and delivering value across the organisation
• Maintained: A data product has owners who ensure it keeps workings and stays relevant over time

Crucially, a data product is user-centric, meaning it is designed to solve users' problems. It is the business team's responsibility to request a data product by explaining the problem they are facing.

Data products come in many shapes. A recommendation engine suggesting what to watch next on a streaming platform is a data product. So is an internal report that automatically updates every Monday with the previous week's sales figures.

Why does it matter?

In an era where AI is transforming the habits of users and redefining how work gets done, organisations face a quiet but critical challenge: making sure their data is ready for it. Not just collected, not just stored — but structured, trusted, and accessible. That is what a data product is built to be.

Adopting a data product mindset is as much a cultural change as a technical one. The classic data project delivers something specific: a sales dashboard for Q3, built for one request, consulted briefly, then forgotten. A data product flips that logic — the dashboard is designed from the outset to be reused, refreshed, and consulted by anyone, across any period.

For the business, the payoff is significant. Instead of commissioning a new analysis every time a question arises, teams can turn to something already built, already trusted, and already maintained.

In conclusion, data projects answer a question. Data products keep answering questions. That distinction, simple as it sounds, is at the heart of how modern organisations are learning to get more from their data.