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

Previously decade, firms have spent billions on knowledge 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: Knowledge 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 advantageous when knowledge requests have been small and stakes have been low. However as firms grew to become “data-driven,” this mannequin fractured beneath 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
• Lively person
• Obtainable 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 “right” reasonably 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.
• Determination drag: Leaders delay or ignore motion as a consequence of inconsistent inputs.

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

Most knowledge leaders assume they’ve a knowledge high quality challenge. However look nearer, and also you’ll discover a knowledge 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 identify, 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 information product supervisor

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

However DPMs don’t cease at piping knowledge into dashboards or curating tables. The most effective 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 resolution 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. Which means 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 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. An information pipeline that no group makes use of is technical debt, not progress.
• Design for sturdiness. Many knowledge merchandise fail not from dangerous modeling, however from brittle methods: undocumented logic, flaky pipelines, shadow possession. Construct with the idea that your future self — or your alternative — will thanks.
• Remedy horizontally. In contrast to domain-specific PMs, DPMs should consistently 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 firms, DPMs are quietly redefining how inside knowledge methods 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. Knowledge engineers constructed pipelines. Scientists constructed fashions. Analysts constructed dashboards. However nobody requested: “Will this perception truly change a enterprise resolution?” Or worse: We requested, however nobody owned the reply.

As a result of government selections are actually data-mediated

In right this moment’s enterprise, almost each main resolution — finances shifts, new launches, org restructures — passes by 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, resolution paralysis turns into the norm.

Why this position will speed up within the AI period

AI received’t exchange DPMs. It would make them important:

• 80% of AI challenge effort nonetheless goes to knowledge readiness (Forrester).
• As giant language fashions (LLMs) scale, the price of rubbish inputs compounds. AI doesn’t repair dangerous knowledge — it amplifies it.
• Regulatory stress (the EU AI Act, the California Shopper Privateness Act) is pushing orgs to deal with inside knowledge methods 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 case you’re a CPO, CTO or head of knowledge, ask:

• Who owns the info methods that energy our largest 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?

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.