The Benefits Of Data Product Thinking

Connor Quinn  — September 6, 2025

To my mind, the shift of focus to data products in recent years is a response to the fact that very few businesses feel they are being well served by their data. Data becomes a costly and time consuming effort that is rarely at the heart of how the business makes decisions.

Adopting a data product mindset aims to shift the focus to what will be most valuable to users. Making decisions about how best to manage, clean, curate, and serve data becomes simplified when the users are treated as the highest priority.

Despite this, it can be difficult for teams to break out of established ways-of-working without some extenal support. At Kelvin Analytics, we can partner with your data leaders and delivery teams to establish data product thinking and ways of working that your teams can carry forward.

The importance of Data Product Owners

A common pitfall when building data products is to skip the step of agreeing a data product owner. Usually that is because the existing org structure doesn’t have a role fitting this description and so instead, the job is fobbed off to someone already up to their eyeballs with work. Finding the right person to engage with the business is hard, and so there is a temptation for the data teams to name a data product and then send it out into the world without clear ownership.

This is a mistake.

The result of this omission will be that data teams make well-intentioned but poorly informed decisions about the data, while the business becomes increasingly removed from the data. Back to the common pattern of the data team working hard but the business being left unsatisfied.

The data product owner should sit between the business and the data teams, guiding priorities according to what will add most value for users. Their overriding concern should be what will make our users happy. They are accountable not only for the delivery of the product, but also for publicising it, making it discoverable, monitoring its use and performance, and working to align the semantics across the wider business.

One final key aspect of the role is knowing when to retire a data product. By shifting to a data product mentality means that we should have clear vision of how well the product is being used by the business. When the product stops justifying its existence, the product owner should decide to turn it off. Finally your data can have a lifecycle where the burden does not only ever increase.

Learn more about how we can support your data strategy

Check out this excellent blog post: Building high quality data products.