Data everywhere, direction nowhere: Which dashboards are wrong and why you need a data product manager

In the past ten years, companies have issued billions for this Data infrastructure. Petabyte scale warehouses. Real-time pipelines. ML platforms for machine learning (ML).

And yet – ask your company why the emigration increased last week, and you will probably receive three contradictory dashboards. Ask the financing of reconciling the performance across attribution systems and you will hear: “It depends on who you ask.”

In a world that drowns into dashboards, a truth keeps appearing: data is not the problem – the product is thinking.

The quiet collapse of “Data-as-a-Service”

For years, Data team Like internal consulting companies, reactivated, ticket-based, heroic-based. This “Data-as-a-Service” model (DAAS) was fine if the data requests were low and the operations were low. However, when companies were “driven”, this model broke under the weight of its own success.

Take Airbnb. Before the introduction of its metrics platform, product, finance and ops teams pulled their own versions of metrics such as:

• Booked nights
• Active user
• Available listing

Even simple KPIs varied depending on the filter, sources and who asked. Different teams showed different numbers in leadership exams – which led to arguments about it whose metric was “correct” and not to take to what measures.

These are not technological failures. They are product losses.

The consequences

• Data misses: Analysts are second largest. Dashboards are given up.
• Human routers: Data scientists spend more time to explain discrepancies than generate knowledge.
• Redundant pipelines: Engineers convert similar data records in teams.
• Decision resistance: Managers delay or ignore measures due to inconsistent inputs.

Because Data Trust is a product problem, no technical problem

Most data leaders believe that they have a problem with data quality. But take a closer look and find a problem with the data confidence:

• Your experiment platform says that a feature violates the bond – but product leaders don’t believe it.
• OPS sees a dashboard that contradicts your experience.
• Two teams use the same metric name, but a different logic.

The pipelines work. The SQL is solid. But nobody trusts the outputs.

This is a product failure, not a technical one. Because the systems were not designed for user -friendliness, interpretability or decision -making.

Enter: the data product manager

A new role has developed in the top companies – the Data Product Manager (DPM). In contrast to Generalist PMS, DPMS work on brittle, invisible and cross -functional terrain. Your job is not to send dashboards. It should make sure that the right people have the right insight at the right time make a decision.

However, DPMS do not stop in dashboards or curated tables from the pipeline data. The best go on: You ask: “Does this actually help someone to do his job better?” They do not define success in terms of outputs, but the results. Not “was that sent?” But “Did this significantly improve the workflow or the quality of the decision?”

In practice, this means:

• Do not only define users. Watch them. Ask how you believe that the product works. Sit next to them. Your task is not to send a data record – it should make your customers more effective. This means deeply understanding how the product fits in the real context of your work.
• You can do your own canonical metrics and treat them as APIS-version, documented, governing that you are tied to follow-up decisions such as $ 10 million budget prolongs or GO/no-go product launches.
• Create internal interfaces -such as Featores and Clean Room -Apis -not as an infrastructure, but as real products with contracts, slas, users and feedback grinding.
• Say no to projects that feel developed but don’t matter. A data pipeline that no team uses is technical debt, not progress.
• Design for durability. Many data products do not fail from poor modeling, but from brittle systems: undocumented logic, scaly pipelines, shadow possession. Create with the assumption that your future self – or your replacement – will thank you.
• Solve horizontally. In contrast to domain -specific PMS, DPMS must constantly reduce. The logic of the lifetime value (lifetime value) of a team is the budget input of a team. An apparently small metric update can have a second order in marketing, finance and operations. Watching this complexity is the job.

In companies, DPMS is quietly redefined how internal data systems are created, ruled and accepted. You are not there to clean data. You are there to believe in organizations.

Why did it take so long

We kept activities with progress for years. Data engineers built pipelines. Scientists built models. Analysts built dashboards. But nobody asked: “Will this insight actually change a business decision?” Or worse: we asked, but nobody had the answer.

Because executive decisions are now data-transferred

In today’s company, almost every important decision – budget shifts, New startsOrgant structure – first goes through a layer of data. But these layers are often unknown:

• The metric version used in the last quarter has changed – but nobody knows when or why.
• The experimental logic differs in the teams.
• Description models contradict each other with plausible logic.

DPMS does not have the decision – they have the interface that makes the decision readable.

DPMS ensure that metrics can be interpreted, the assumptions are transparent and tools geared towards real workflows. Without them, paralysis becomes the norm.

Why this role is accelerated in the AI ​​era

Ai does not replace DPMS. It will make them essential:

• 80% of the AI ​​project efforts are still on the data willingness to data (Forrester).
• As an LLMS scale (LCMS models), the costs of the garbage inputs are compounds. AI does not repair poor data – it strengthens you.
• Regulatory printing (the EU AI Act, the California Consumer Privacy Act), the organization is urging internal data systems to treat with product streng.

DPMS are not traffic coordinators. They are the architects of trust, interpretability and responsible AI foundations.

So what now?

If you are a CPO, CTO or data manager, ask:

• Who has the data systems that participate in our greatest decisions?
• Are our internal APIs and metrics versioned, found and governed?
• Do we know which data products are adopted – and which tacitly undermines the trust?

If you cannot answer clearly, you no longer need dashboards.

You need a data product manager.

SEOJOON OH is a data product manager at Uber.