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Previously decade, corporations have spent billions on knowledge infrastructure. Petabyte-scale warehouses. Actual-time pipelines. Machine studying (ML) platforms.
And but — ask your operations lead why churn elevated final week, and also you’ll possible get three conflicting dashboards. Ask finance to reconcile efficiency throughout attribution techniques, and also you’ll hear, “It is dependent upon who you ask.”
In a world drowning in dashboards, one reality retains surfacing: Knowledge isn’t the issue — product pondering is.
The quiet collapse of “data-as-a-service”
For years, knowledge groups operated like inside consultancies — reactive, ticket-based, hero-driven. This “data-as-a-service” (DaaS) mannequin was nice when knowledge requests have been small and stakes have been low. However as corporations grew to become “data-driven,” this mannequin fractured beneath the burden of its personal success.
Take Airbnb. Earlier than the launch of its metrics platform, product, finance and ops groups pulled their very own variations of metrics like:
- Nights booked
- Energetic person
- Out there itemizing
Even easy KPIs assorted by filters, sources and who was asking. In management evaluations, completely different groups introduced completely different numbers — leading to arguments over whose metric was “appropriate” moderately than what motion to take.
These aren’t know-how failures. They’re product failures.
The implications
- Knowledge mistrust: Analysts are second-guessed. Dashboards are deserted.
- Human routers: Knowledge scientists spend extra time explaining discrepancies than producing insights.
- Redundant pipelines: Engineers rebuild related datasets throughout groups.
- Determination drag: Leaders delay or ignore motion resulting from inconsistent inputs.
As a result of knowledge belief is a product downside, not a technical one
Most knowledge leaders suppose they’ve a knowledge high quality problem. However look nearer, and also you’ll discover a knowledge belief problem:
- Your experimentation platform says a function hurts retention — however product leaders don’t imagine it.
- Ops sees a dashboard that contradicts their lived expertise.
- Two groups use the identical metric identify, however completely different logic.
The pipelines are working. The SQL is sound. However nobody trusts the outputs.
This can be a product failure, not an engineering one. As a result of the techniques weren’t designed for usability, interpretability or decision-making.
Enter: The information product supervisor
A brand new function has emerged throughout prime corporations — the information product supervisor (DPM). Not like generalist PMs, DPMs function throughout brittle, invisible, cross-functional terrain. Their job isn’t to ship dashboards. It’s to make sure the appropriate individuals have the appropriate perception on the proper time to decide.
However DPMs don’t cease at piping knowledge into dashboards or curating tables. The very best ones go additional: They ask, “Is that this truly serving to somebody do their job higher?” They outline success not by way of outputs, however outcomes. Not “Was this shipped?” however “Did this materially enhance somebody’s workflow or choice high quality?”
In apply, this implies:
- Don’t simply outline customers; observe them. Ask how they imagine the product works. Sit beside them. Your job isn’t to ship a dataset — it’s to make your buyer simpler. Which means deeply understanding how the product matches into the real-world context of their work.
- Personal canonical metrics and deal with them like APIs — versioned, documented, ruled — and guarantee they’re tied to consequential selections like $10 million finances unlocks or go/no-go product launches.
- Construct inside interfaces — like function shops and clear room APIs — not as infrastructure, however as actual merchandise with contracts, SLAs, customers and suggestions loops.
- Say no to tasks that really feel refined however don’t matter. An information pipeline that no crew makes use of is technical debt, not progress.
- Design for sturdiness. Many knowledge merchandise fail not from dangerous modeling, however from brittle techniques: undocumented logic, flaky pipelines, shadow possession. Construct with the idea that your future self — or your alternative — will thanks.
- Clear up horizontally. Not like domain-specific PMs, DPMs should always zoom out. One crew’s lifetime worth (LTV) logic is one other crew’s finances enter. A seemingly minor metric replace can have second-order penalties throughout advertising, finance and operations. Stewarding that complexity is the job.
At corporations, DPMs are quietly redefining how inside knowledge techniques are constructed, ruled and adopted. They aren’t there to scrub knowledge. They’re there to make organizations imagine in it once more.
Why it took so lengthy
For years, we mistook exercise for progress. Knowledge engineers constructed pipelines. Scientists constructed fashions. Analysts constructed dashboards. However nobody requested: “Will this perception truly change a enterprise choice?” Or worse: We requested, however nobody owned the reply.
As a result of govt selections at the moment are data-mediated
In at present’s enterprise, almost each main choice — finances shifts, new launches, org restructures — passes by means of a knowledge layer first. However these layers are sometimes unowned:
- The metric model used final quarter has modified — however nobody is aware of when or why.
- Experimentation logic differs throughout groups.
- Attribution fashions contradict one another, every with believable logic.
DPMs don’t personal the choice — they personal the interface that makes the choice legible.
DPMs be sure that metrics are interpretable, assumptions are clear and instruments are aligned to actual workflows. With out them, choice paralysis turns into the norm.
Why this function will speed up within the AI period
AI received’t change DPMs. It would make them important:
- 80% of AI undertaking effort nonetheless goes to knowledge readiness (Forrester).
- As giant language fashions (LLMs) scale, the price of rubbish inputs compounds. AI doesn’t repair dangerous knowledge — it amplifies it.
- Regulatory strain (the EU AI Act, the California Client Privateness Act) is pushing orgs to deal with inside knowledge techniques with product rigor.
DPMs aren’t site visitors coordinators. They’re the architects of belief, interpretability, and accountable AI foundations.
So what now?
If you happen to’re a CPO, CTO or head of knowledge, ask:
- Who owns the information techniques that energy our largest selections?
- Are our inside APIs and metrics versioned, discoverable and ruled?
- Do we all know which knowledge merchandise are adopted — and that are quietly undermining belief?
If you happen to can’t reply clearly, you don’t want extra dashboards.
You want a knowledge product supervisor.
Seojoon Oh is a knowledge product supervisor at Uber.
