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Previously decade, corporations have spent billions on information 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 doubtless 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 considering is.
The quiet collapse of “data-as-a-service”
For years, information groups operated like inside consultancies — reactive, ticket-based, hero-driven. This “data-as-a-service” (DaaS) mannequin was tremendous when information requests had been small and stakes had been low. However as corporations grew to become “data-driven,” this mannequin fractured below the load 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
- Accessible itemizing
Even easy KPIs assorted by filters, sources and who was asking. In management evaluations, totally different groups offered totally different numbers — leading to arguments over whose metric was “appropriate” somewhat than what motion to take.
These aren’t know-how failures. They’re product failures.
The results
- 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 comparable datasets throughout groups.
- Determination drag: Leaders delay or ignore motion resulting from inconsistent inputs.
As a result of information belief is a product drawback, not a technical one
Most information leaders assume they’ve an information high quality problem. However look nearer, and also you’ll discover a information 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 title, however totally 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 position has emerged throughout prime corporations — the information product supervisor (DPM). In contrast to generalist PMs, DPMs function throughout brittle, invisible, cross-functional terrain. Their job isn’t to ship dashboards. It’s to make sure the correct folks have the correct perception on the proper time to decide.
However DPMs don’t cease at piping information into dashboards or curating tables. One of the 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 determination high quality?”
In follow, 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 suits 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 price range 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 subtle however don’t matter. An information pipeline that no workforce makes use of is technical debt, not progress.
- Design for sturdiness. Many information merchandise fail not from dangerous modeling, however from brittle techniques: undocumented logic, flaky pipelines, shadow possession. Construct with the belief that your future self — or your alternative — will thanks.
- Remedy horizontally. In contrast to domain-specific PMs, DPMs should always zoom out. One workforce’s lifetime worth (LTV) logic is one other workforce’s price range enter. A seemingly minor metric replace can have second-order penalties throughout advertising and marketing, finance and operations. Stewarding that complexity is the job.
At corporations, DPMs are quietly redefining how inside information techniques are constructed, ruled and adopted. They aren’t there to scrub information. 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 determination?” Or worse: We requested, however nobody owned the reply.
As a result of government selections at the moment are data-mediated
In in the present day’s enterprise, practically each main determination — price range shifts, new launches, org restructures — passes by way of an information 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, determination paralysis turns into the norm.
Why this position will speed up within the AI period
AI received’t change DPMs. It is going to make them important:
- 80% of AI venture effort nonetheless goes to information readiness (Forrester).
- As massive language fashions (LLMs) scale, the price of rubbish inputs compounds. AI doesn’t repair dangerous information — it amplifies it.
- Regulatory strain (the EU AI Act, the California Client Privateness Act) is pushing orgs to deal with inside information techniques with product rigor.
DPMs will not be site visitors coordinators. They’re the architects of belief, interpretability, and accountable AI foundations.
So what now?
When you’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 information merchandise are adopted — and that are quietly undermining belief?
When you can’t reply clearly, you don’t want extra dashboards.
You want an information product supervisor.
Seojoon Oh is an information product supervisor at Uber.
