Data all over the place, alignment nowhere: What dashboards are getting incorrect, and why you want a knowledge product supervisor

In the previous decade, corporations have spent billions on knowledge infrastructure. Petabyte-scale warehouses. Real-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 depends on who you ask.”

In a world drowning in dashboards, one reality retains surfacing: Data isn’t the issue — product considering is.

The quiet collapse of “data-as-a-service”

For years, knowledge groups operated like inside consultancies — reactive, ticket-based, hero-driven. This “data-as-a-service” (DaaS) mannequin was effective when knowledge requests had been small and stakes had been low. But as corporations turned “data-driven,” this mannequin fractured underneath the load of its personal success.

Take Airbnb. Before the launch of its metrics platform, product, finance and ops groups pulled their very own variations of metrics like:

• Nights booked
• Active consumer
• Available 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 “correct” relatively than what motion to take.

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

The penalties

• Data mistrust: Analysts are second-guessed. Dashboards are deserted.
• Human routers: Data scientists spend extra time explaining discrepancies than producing insights.
• Redundant pipelines: Engineers rebuild related datasets throughout groups.
• Decision drag: Leaders delay or ignore motion resulting from inconsistent inputs.

Because knowledge belief is a product drawback, not a technical one

Most knowledge leaders suppose they’ve a knowledge high quality subject. But look nearer, and also you’ll discover a knowledge belief subject:

• 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 identify, however completely different logic.

The pipelines are working. The SQL is sound. But nobody trusts the outputs.

This is a product failure, not an engineering one. Because the techniques weren’t designed for usability, interpretability or decision-making.

Enter: The knowledge product supervisor

A brand new position has emerged throughout high corporations — the info product supervisor (DPM). Unlike 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.

But DPMs don’t cease at piping knowledge into dashboards or curating tables. The finest ones go additional: They ask, “Is this actually helping someone do their job better?” They outline success not by way of outputs, however outcomes. Not “Was this shipped?” however “Did this materially improve someone’s workflow or decision quality?”

In apply, 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. That means deeply understanding how the product suits into the real-world context of their work.
• Own canonical metrics and deal with them like APIs — versioned, documented, ruled — and guarantee they’re tied to consequential selections like $10 million funds unlocks or go/no-go product launches.
• Build inside 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 refined however don’t matter. An information pipeline that no crew makes use of is technical debt, not progress.
• Design for sturdiness. Many knowledge merchandise fail not from unhealthy modeling, however from brittle techniques: undocumented logic, flaky pipelines, shadow possession. Build with the idea that your future self — or your alternative — will thanks.
• Solve horizontally. Unlike domain-specific PMs, DPMs should continuously zoom out. One crew’s lifetime worth (LTV) logic is one other crew’s funds 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 inside knowledge techniques are constructed, ruled and adopted. They aren’t there to scrub knowledge. They’re there to make organizations imagine in it once more.

Why it took so lengthy

For years, we mistook exercise for progress. Data engineers constructed pipelines. Scientists constructed fashions. Analysts constructed dashboards. But nobody requested: “Will this insight actually change a business decision?” Or worse: We requested, however nobody owned the reply.

Because govt selections are actually data-mediated

In right now’s enterprise, almost each main choice — funds shifts, new launches, org restructures — passes by a knowledge layer first. But 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. Without them, choice paralysis turns into the norm.

Why this position will speed up within the AI period

AI received’t substitute DPMs. It will make them important:

• 80% of AI mission effort nonetheless goes to knowledge readiness (Forrester).
• As giant language fashions (LLMs) scale, the price of rubbish inputs compounds. AI doesn’t repair unhealthy knowledge — it amplifies it.
• Regulatory stress (the EU AI Act, the California Consumer Privacy Act) is pushing orgs to deal with inside knowledge techniques with product rigor.

DPMs will not be visitors coordinators. They’re the architects of belief, interpretability, and accountable AI foundations.

So what now?

If you’re a CPO, CTO or head of knowledge, ask:

• Who owns the info techniques that energy our greatest selections?
• Are our inside APIs and metrics versioned, discoverable and ruled?
• Do we all know which knowledge merchandise are adopted — and that are quietly undermining belief?

If you may’t reply clearly, you don’t want extra dashboards.

You want a knowledge product supervisor.

Seojoon Oh is a knowledge product supervisor at Uber.