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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 doubtless get three conflicting dashboards. Ask finance to reconcile efficiency throughout attribution programs, and also you’ll hear, “It is determined by who you ask.”
In a world drowning in dashboards, one fact retains surfacing: Knowledge 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 tremendous when information requests had been small and stakes had been low. However as corporations turned “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
- Obtainable itemizing
Even easy KPIs diversified by filters, sources and who was asking. In management evaluations, completely different groups introduced completely different numbers — leading to arguments over whose metric was “right” relatively 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 related datasets throughout groups.
- Resolution drag: Leaders delay or ignore motion because 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 difficulty. However look nearer, and also you’ll discover a information belief difficulty:
- 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 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 programs weren’t designed for usability, interpretability or decision-making.
Enter: The information product supervisor
A brand new function 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. The most effective ones go additional: They ask, “Is that this really 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 more practical. 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 selections like $10 million finances unlocks or go/no-go product launches.
- Construct inner 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 group makes use of is technical debt, not progress.
- Design for sturdiness. Many information merchandise fail not from unhealthy modeling, however from brittle programs: undocumented logic, flaky pipelines, shadow possession. Construct with the idea that your future self — or your alternative — will thanks.
- Clear up horizontally. In contrast to domain-specific PMs, DPMs should continually 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 and marketing, finance and operations. Stewarding that complexity is the job.
At corporations, DPMs are quietly redefining how inner information programs are constructed, ruled and adopted. They aren’t there to wash 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 really change a enterprise determination?” Or worse: We requested, however nobody owned the reply.
As a result of govt selections are actually data-mediated
In as we speak’s enterprise, practically each main determination — 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 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 gained’t change DPMs. It should 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 stress (the EU AI Act, the California Client Privateness Act) is pushing orgs to deal with inner information programs with product rigor.
DPMs will not be visitors coordinators. They’re the architects of belief, interpretability, and accountable AI foundations.
So what now?
In case you’re a CPO, CTO or head of knowledge, ask:
- Who owns the information programs 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?
In case 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.
