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Up 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 doubtless get three conflicting dashboards. Ask finance to reconcile efficiency throughout attribution methods, and also you’ll hear, “It depends upon who you ask.”
In a world drowning in dashboards, one fact retains surfacing: Information isn’t the issue — product considering is.
The quiet collapse of “data-as-a-service”
For years, information groups operated like inner consultancies — reactive, ticket-based, hero-driven. This “data-as-a-service” (DaaS) mannequin was positive when information requests have been small and stakes have been low. However as corporations turned “data-driven,” this mannequin fractured beneath 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
- Out there itemizing
Even easy KPIs assorted by filters, sources and who was asking. In management opinions, completely different groups offered completely different numbers — leading to arguments over whose metric was “right” slightly than what motion to take.
These aren’t know-how failures. They’re product failures.
The implications
- Information mistrust: Analysts are second-guessed. Dashboards are deserted.
- Human routers: Information scientists spend extra time explaining discrepancies than producing insights.
- Redundant pipelines: Engineers rebuild related datasets throughout groups.
- Resolution drag: Leaders delay or ignore motion as a consequence of inconsistent inputs.
As a result of information belief is a product drawback, not a technical one
Most information leaders assume they’ve a knowledge 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 imagine 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 info product supervisor
A brand new function has emerged throughout high 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 proper individuals have the proper 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 truly 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 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 finances unlocks or go/no-go product launches.
- Construct inner 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 subtle however don’t matter. An information pipeline that no staff 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 idea that your future self — or your alternative — will thanks.
- Remedy horizontally. Not like domain-specific PMs, DPMs should continuously zoom out. One staff’s lifetime worth (LTV) logic is one other staff’s finances 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 inner information methods 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. Information engineers constructed pipelines. Scientists constructed fashions. Analysts constructed dashboards. However nobody requested: “Will this perception truly change a enterprise resolution?” Or worse: We requested, however nobody owned the reply.
As a result of government selections at the moment are data-mediated
In right this moment’s enterprise, almost each main resolution — finances shifts, new launches, org restructures — passes by way 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 certain 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 function will speed up within the AI period
AI received’t exchange DPMs. It’ll make them important:
- 80% of AI mission effort nonetheless goes to information readiness (Forrester).
- As massive 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 Client Privateness Act) is pushing orgs to deal with inner information methods with product rigor.
DPMs will not be site visitors coordinators. They’re the architects of belief, interpretability, and accountable AI foundations.
So what now?
Should you’re a CPO, CTO or head of information, ask:
- Who owns the information methods that energy our largest selections?
- Are our inner APIs and metrics versioned, discoverable and ruled?
- Do we all know which information merchandise are adopted — and that are quietly undermining belief?
Should you 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.
