From Data Products to Product Thinking: Why Data Teams Must Evolve
In a recent panel discussion with Malcolm Hawker, Brian T. O’Neill, and Mark Stouse, we tackled a hard truth: too many data teams are still confusing outputs with outcomes. I wanted to provide you with an overview of the conversation we had and how the panel played out. Thanks to all those who attended, and for those who didn’t, follow me, and you will be notified of our next panel.
But, back to the panel. As I put it during the session:
“Too many data teams are still delivering outputs, not outcomes. A dashboard or a model is not a product, until it really makes a business impact and changes decisions and results.”
Some folks out there may disagree with that, but we have seen, heard and some have experienced what happens when data teams don’t deliver value and don’t embed the products they build into the processes and DNA of the organization.
The Core Challenges
- Outputs mistaken for value Dashboards and models are produced in volume, but adoption remains dismal. Deliverables are often celebrated, even when no one uses them: “If only 1 percent of dashboards are being used, then where is the value exchange? Adoption is the real test of a data product, not how many you’ve built.” Samir Sharma
- Inside-out vs. outside-in As Mark Stouse warned, too many teams still build what they think is valuable: “If you build data products inside-out, like bringing your customer a dead lizard and saying ‘look what I made for you’ don’t be surprised when they’re not impressed.”
- The cult of precision Data science still chases academic-style accuracy instead of usable insights: “A 58% model shipped in two weeks can create more value than a 95% model delivered after seven months, because by then, the business has already moved on.” Brian T. O’Neill
- Lack of customer orientation Malcolm Hawker shared a telling stat: “When I asked a room of 400 data professionals how many refer to their stakeholders as customers, only three hands went up.”
Recommendations for Change
- Be relentlessly customer-driven As Malcolm put it: “Stop putting data on a pedestal. Forget being data-driven. Start being customer-driven, if there are no customers, there is no data team.”
- Adopt product management methods Brian stressed that the real game isn’t in outputs but in outcomes: “Most data teams are stuck in the feature factory: shipping outputs, not outcomes. The real game is downstream, what changes because of the thing you built?”
- Measure success by business impact I added: “The feedback loop is missing. Data teams measure pipelines and quality, but not the business impact. That’s the real metric of success.”
- Embrace imperfection and speed A model doesn’t need to be perfect; it needs to be useful. As Mark noted, CFOs don’t want precision for its own sake, they want forward visibility that helps them act.
- Unlearn old mindsets Malcolm urged us to drop deterministic thinking: “These binary ways of looking at the world don’t work in a probabilistic, AI-driven world. We need to be comfortable with ambiguity.”
The Mindset Shift
The key lesson? A “data product” is not the end goal. The goal is to deliver outcomes that business stakeholders would pay for in budget, in behavior change, or in trust.
“We need to elevate data teams out of just shipping stuff and into building outcomes that have a deeper, meaningful relationship with the business.” Samir Sharma
Data teams that embrace product thinking will:
- See themselves as problem-solvers, not deliverable factories.
- Work iteratively with customers, not in isolation.
- Measure success by business impact, not vanity metrics.
Those that don’t risk being sidelined. Those that do will become indispensable.
The future of data belongs to teams who think like product managers and embrace the product life-cycle mentality.
If your team isn’t there yet, the time to start is now.
