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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 probably get three conflicting dashboards. Ask finance to reconcile efficiency throughout attribution methods, and also you’ll hear, “It is determined by who you ask.”
In a world drowning in dashboards, one fact retains surfacing: Information 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 tremendous when information requests had been small and stakes had been low. However as corporations grew to become “data-driven,” this mannequin fractured underneath 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
- Lively consumer
- Out there itemizing
Even easy KPIs assorted by filters, sources and who was asking. In management evaluations, totally different groups introduced 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
- 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 comparable datasets throughout groups.
- Resolution drag: Leaders delay or ignore motion because of inconsistent inputs.
As a result of information belief is a product drawback, not a technical one
Most information leaders suppose they’ve a knowledge high quality challenge. However look nearer, and also you’ll discover a information belief challenge:
- 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.
It is 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 position 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 fitting individuals have the fitting 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 observe, 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. Meaning 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 choices 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. A knowledge pipeline that no group 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. Not like domain-specific PMs, DPMs should continuously zoom out. One group’s lifetime worth (LTV) logic is one other group’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 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 really change a enterprise resolution?” Or worse: We requested, however nobody owned the reply.
As a result of govt choices at the moment are data-mediated
In right now’s enterprise, practically each main resolution — finances shifts, new launches, org restructures — passes via 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 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 is going to make them important:
- 80% of AI challenge 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 Client Privateness Act) is pushing orgs to deal with inside information methods with product rigor.
DPMs usually are not site 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 information methods that energy our greatest choices?
- 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 a knowledge product supervisor.
Seojoon Oh is a knowledge product supervisor at Uber.
