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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 probably 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 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 have been small and stakes have been low. However as corporations grew to become “data-driven,” this mannequin fractured below 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 person
- Accessible itemizing
Even easy KPIs different by filters, sources and who was asking. In management critiques, completely different groups introduced completely different numbers — leading to arguments over whose metric was “right” moderately 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 result of inconsistent inputs.
As a result of information belief is a product downside, 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 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.
It is a product failure, not an engineering one. As a result of the techniques weren’t designed for usability, interpretability or decision-making.
Enter: The info 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 precise folks have the precise perception on the proper time to decide.
However DPMs don’t cease at piping information 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 when it comes to 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 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 choices like $10 million funds 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. A knowledge 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 techniques: 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 consistently zoom out. One staff’s lifetime worth (LTV) logic is one other staff’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 techniques 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. Information 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 choices are actually data-mediated
In at this time’s enterprise, almost each main determination — funds 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 be certain 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 function will speed up within the AI period
AI received’t exchange DPMs. It can make them important:
- 80% of AI undertaking 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 inside information techniques with product rigor.
DPMs are usually 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 knowledge, ask:
- Who owns the information techniques 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.
