Knowledge in all places, alignment nowhere: What dashboards are getting fallacious, and why you want an information product supervisor

Previously 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 possible get three conflicting dashboards. Ask finance to reconcile efficiency throughout attribution techniques, 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 pondering 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 advantageous when information requests had been small and stakes had been low. However as corporations grew to become “data-driven,” this mannequin fractured beneath 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
• Energetic consumer
• Obtainable itemizing

Even easy KPIs diverse by filters, sources and who was asking. In management evaluations, completely different groups offered completely different numbers — leading to arguments over whose metric was “right” somewhat than what motion to take.

These aren’t expertise failures. They’re product failures.

The implications

• 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 comparable datasets throughout groups.
• Choice drag: Leaders delay or ignore motion resulting from inconsistent inputs.

As a result of information belief is a product drawback, not a technical one

Most information leaders assume 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 identify, 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 techniques weren’t designed for usability, interpretability or decision-making.

Enter: The information product supervisor

A brand new position has emerged throughout prime corporations — the info 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 suitable individuals have the suitable perception on the proper time to decide.

However DPMs don’t cease at piping information into dashboards or curating tables. One of the best 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 determination 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 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 choices 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. 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 techniques: undocumented logic, flaky pipelines, shadow possession. Construct with the belief that your future self — or your alternative — will thanks.
• Remedy 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 inner information techniques are constructed, ruled and adopted. They aren’t there to wash 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 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, practically each main determination — finances 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 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 position will speed up within the AI period

AI gained’t exchange DPMs. It’ll make them important:

• 80% of AI challenge 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 stress (the EU AI Act, the California Shopper Privateness Act) is pushing orgs to deal with inner information techniques with product rigor.

DPMs usually are not 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 techniques that energy our largest choices?
• 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 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.