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Prior to now 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 seemingly get three conflicting dashboards. Ask finance to reconcile efficiency throughout attribution methods, and also you’ll hear, “It will depend on 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, information groups operated like inside consultancies — reactive, ticket-based, hero-driven. This “data-as-a-service” (DaaS) mannequin was fantastic 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
- Lively consumer
- Obtainable itemizing
Even easy KPIs diversified by filters, sources and who was asking. In management opinions, completely different groups introduced completely different numbers — leading to arguments over whose metric was “appropriate” fairly than what motion to take.
These aren’t expertise 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 related datasets throughout groups.
- Determination drag: Leaders delay or ignore motion as a result of inconsistent inputs.
As a result of information belief is a product drawback, not a technical one
Most information leaders suppose they’ve an information high quality concern. However look nearer, and also you’ll discover a information belief concern:
- Your experimentation platform says a characteristic hurts retention — however product leaders don’t consider it.
- Ops sees a dashboard that contradicts their lived expertise.
- Two groups use the identical metric title, 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 methods weren’t designed for usability, interpretability or decision-making.
Enter: The information product supervisor
A brand new position has emerged throughout high corporations — the info 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 precise individuals have the precise perception on the proper time to decide.
However DPMs don’t cease at piping information into dashboards or curating tables. The most effective ones go additional: They ask, “Is that this really serving to somebody do their job higher?” They outline success not when it comes to outputs, however outcomes. Not “Was this shipped?” however “Did this materially enhance somebody’s workflow or resolution high quality?”
In apply, this implies:
- Don’t simply outline customers; observe them. Ask how they consider 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 funds unlocks or go/no-go product launches.
- Construct inside interfaces — like characteristic shops and clear room APIs — not as infrastructure, however as actual merchandise with contracts, SLAs, customers and suggestions loops.
- Say no to initiatives that really feel refined 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 unhealthy modeling, however from brittle methods: undocumented logic, flaky pipelines, shadow possession. Construct with the belief that your future self — or your alternative — will thanks.
- Resolve horizontally. In contrast to domain-specific PMs, DPMs should continuously zoom out. One workforce’s lifetime worth (LTV) logic is one other workforce’s funds 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 information methods are constructed, ruled and adopted. They aren’t there to scrub information. They’re there to make organizations consider 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 really change a enterprise resolution?” Or worse: We requested, however nobody owned the reply.
As a result of govt selections are actually data-mediated
In in the present day’s enterprise, almost each main resolution — funds 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 make sure that metrics are interpretable, assumptions are clear and instruments are aligned to actual workflows. With out them, resolution paralysis turns into the norm.
Why this position will speed up within the AI period
AI gained’t substitute DPMs. It’s going to make them important:
- 80% of AI venture effort nonetheless goes to information readiness (Forrester).
- As giant language fashions (LLMs) scale, the price of rubbish inputs compounds. AI doesn’t repair unhealthy information — it amplifies it.
- Regulatory strain (the EU AI Act, the California Shopper Privateness Act) is pushing orgs to deal with inside information methods with product rigor.
DPMs will not be visitors coordinators. They’re the architects of belief, interpretability, and accountable AI foundations.
So what now?
In the event you’re a CPO, CTO or head of information, ask:
- Who owns the info methods that energy our greatest 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?
In the event 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.
