Data everywhere, alignment nowhere: What dashboards are getting wrong, and why you need a data product manager

In the last ten years, companies spent with billions Information infrastructure. Petabyte scale warehouses. Real-time pipelines. Machine learning (ML) platforms.

And yet – the increase in the last week of your operations will probably get three contradictory dashboard. Give financing to extend the performance between Fincane’s attribute systems and “It depends on whoever you want.”

In a world drowned in the stone boards, a truth continues to the surface: data is not a problem – product thinking.

The quiet collapse of “Information-AA-Service”

For the years Data groups It operates as internal consultants – jet, ticket-based, managed by the hero. This “Information-AAA-Service” model was good when the information inquiries are small and distributed. However, as companies are “information”, this model is broken by the weight of its success.

Accept Airbnb. Before the use of the metrics platform, the products, finances and OPS teams also took their unique version:

• The nights were ordered
• Active user
• List of available

Even simple KPIs are different by filters, sources and asks. Different teams in leadership reviews presented different numbers – the metrics resulted in the “correct” arguments instead of the action.

These are not technological failures. It is a product shortage.

Consequences

• Data Infability: Analysts are second guess. The dashboard has been abandoned.
• Human routers: Data scientists spend more time to explain more inconsistencies than creating ideas.
• Necessary pipelines: Engineers have restored similar databases between teams.
• Decision Drag: Leaders postpone or ignore the action due to inconsistent entries.

Because information is not a technical but product problem

Most information leaders think that the quality of information is. But look closer and you will find the issue of information confidence:

• Your practice platform says that the product leaders do not believe in the seizure of a feature.
• Ops sees a dashboard that is contrary to the experience they live.
• The two teams use the same metric name, but are different logic.

Pipelines are working. SQL is the sound. But no one trusts the results.

It is not engineering, but a product shortage. Because the use of systems is designed to make abilities, interpretations or decision.

Enter: Information Product Manager

The best companies – a new role in the information product manager (DPM). Unlike the generalized PMS, the DPMS works through a fragile, invisible, cross-functional area. Their job is not to withstand the dashboard. The right people are to ensure that the right thoughts are properly decide.

However, the DPMS data does not stand in the pipelines to taslons or cleansing schedules. The best goes forward: “Does it really help someone to work better?” They determine the success in terms of results, but the results. “Is this sent?” But “this has increased someone’s work flow or decision quality?”

In practice this means:

• Not only identify users; observe them. Ask how they believe in the work of the product. Sit next to. Your job is not to send a database – it is to make your customer more effective. This is deeply understanding how the product works in the true world context.
• Öz kanonik ölçümlərini özünüzə uyğunlaşdırın və APIS kimi müalicə edin – versiyası, sənədləşdirilmiş, idarə olunan və 10 milyon dollarlıq büdcə açılması və ya Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Go / Get
• Build built-in interfaces – feature stores and clean room API – not like infrastructure, such as real products with contracts, slas, users and feedback loops.
• Say no advanced but not different projects. An information that no team uses is a pipeline, not developing technical debt.
• Design for durability. Many data products do not use bad modeling but fragile systems: undocumented logic, vial pipelines, shadow property. Set up with the assumption you thanked with your future yourself or replacement.
• Solve the horizontal. Unlike the PMS, which are domain specific, DPMs must be constantly reduced. The cost of a team’s life (LTV) is the budget of another team. A small metric update that can be a second order between marketing, finance and operations. Control control of complexity.

In companies, the DPMS silently redefines how internal data systems are built and adopted and accepted. They are not there to clear the data. They are there to believe in the organizations again.

Why it took so much

We have taken action for years of progress. Information engineers have built pipelines. Scientists built models. Analysts built the tables. But no one can ask: “Will this idea change a job decision?” Or worse: We asked, but the answer did not have anyone.

Because executive decisions are already a means of information

At today’s enterprise, almost every big decision – budget turns, New releasesOrg Reconstruction – Passing the data fold first. However, these layers are often nounced:

• The metric version used in the last quarter has changed – but when or why no one knows.
• Experience differs between logical teams.
• Attribution models are contrary to each other, each is a suitable logic.

DPMS does not decide – they have an interface that makes the decision clearly.

DPMS ensures the interpretation of measurements, assumptions are transparent and the tools correspond to real work flows. With a decision becomes paralyzed.

Why will this role be accelerated in AI period

AI will not replace DPMs. Will make them important:

• 80% of the AI ​​project efforts are still going to the preparation of information (Forrester).
• Large language models (LLS) scale, cost of combinations of garbage cost. AI does not fix bad data – it strengthens.
• Adjust pressure (EU AI ACT, California Consumer Privacy Act), pushing orgs to treat internal data systems with a product line.

DPMS are not traffic coordinators. They are architects, interpretations and architects of responsible AI foundations.

So what about now?

If you are a CPO, CTO or the head of the information, ask:

• Who owns the data systems that strengthen our greatest decisions?
• Do our Internal APOs and sizes can be detected and managed?
• Do you know what information products are accepted – and what is trusting?

If you can’t answer clearly, you don’t need more dashboard.

You need a data product manager.

Seojoon OH is a data product manager in Uber.