A data product is not a file. It is a promise.

Timo Lindström, CEO, DB Pro Oy & DB Pro Services Oy.

“Data as a Product” sounds like an abstract term. In practice, it means one simple thing: someone is responsible for ensuring that specific data is accurate and available when it is needed.

In many organisations, no one is responsible for this.

What does ‘data product’ mean in everyday life?

Think about how software teams manage code. It has a version history. It has an owner. It has quality control. It has a user interface and APIs that others can call reliably.

This is not always the case with data. Data consists of files, tables and integrations that have been created at different times for different purposes. No one knows exactly where a particular column comes from or when it is updated. The documentation is either stored in people’s memories or has gone missing.

A data product means that this is changing. Data has an owner. It has a defined quality. It has a clear interface. And people are held accountable for it, just as they are for software.

A data product is a promise to the user. When you use this data to make decisions, you can rely on it.

Why does this change everything when it comes to using artificial intelligence?

AI models need reliable data. Not one-off data, not manually cleaned data, not data that nobody owns.

Reliable, continuously maintained, quality-assured data that an AI model can use time and time again, relying on it every single time.

Data products form the infrastructure of artificial intelligence. Without them, an AI project is built on sand.

How should we go about this?

With DB Pro Services’ ‘Data as a Product’ service, we help build data products based on business use cases, rather than a technology-first approach. Key considerations include which data generates the most value, for whom, with what level of accuracy, and at what update frequency.

The result is a data ecosystem that can be used for reporting, artificial intelligence, integrations and new digital services, without each project having to rebuild the same data from scratch.