Do you want smarter insights into your inbox? Register for our weekly newsletters to get only what is important for Enterprise AI, data and security leaders. Subscribe now
In the past decade, companies have spent billions on data infrastructure. Petabyte scale warehouses. Real -time pipelines. Machine Learning (ML) Platforms.
And yet – ask your activities to lead why Churn increased last week, and you will probably get three conflicting dashboards. Ask the financing to reconcile the performance with attribution systems and you will hear: “It depends on who you ask.”
In a world that drowns in dashboards, one truth continues to pop up: data is not the problem – product thinking is.
The silent collapse of “Data-as-a-Service”
For years, Data Teams operated as internal consultancy firms reactive, on ticket-based, heroic-driven. This “data-as-a-service” (Daas) model was fine when data requests were small and the interests were low. But as companies became ‘data -driven’, this model broke under the weight of its own success.
Take Airbnb. Before the launch of the statistical platform, product, financing and OPS teams drew their own versions of statistics such as:
- Booked
- Active user
- Available
Even simple KPIs varied through filters, sources and who asked. In leadership reviews, various teams presented different figures – which resulted in arguments about whose statistics ‘correct’ was instead of which action to take.
These are not technology errors. They are product errors.
The consequences
- Data distrust: analysts are confused second. Dashboards are abandoned.
- Human routers: data scientists spend more time explaining discrepancies than generating insights.
- Redundant pipelines: Engineers rebuild comparable datasets between teams.
- Decision resistance: Leaders delay or ignore action due to inconsistent inputs.
Because Data Trust is a product problem, not technical
Most data leaders think they have a problem with the data quality. But look better and you will find a problem with data confidence:
- Your experiments platform says that a function does retention, but product leaders don’t believe it.
- Ops sees a dashboard that contradicts their lived experience.
- Two teams use the same metric name, but different logic.
The pipelines work. The SQL is healthy. But nobody trusts the outputs.
This is a product error, not technical. Because the systems are not designed for usability, interpretability or decision -making.
Enter: the data product manager
A new role has arisen at top companies – the Data Product Manager (DPM). In contrast to generalist PMS, DPMs work on Brosse, invisible, cross-functional terrain. Their task is not to send dashboards. It is to ensure that the right people have the right insight at the right time to make a decision.
But DPMs do not stop with pipe data in dashboards or curation tables. The best go further: they ask: “Does this actually help someone help to do his job better?” They do not define success in terms of output, but results. Not “was this sent?” But “has this improved someone’s workflow or decision -making quality?”
In practice this means:
- Do not only define users; Observe them. Ask how they believe that the product works. Sit next to them. Your job is not to send a data set – this is to make your customer more effective. That means deeply understanding how the product fits into the real-world context of their work.
- Own canonic statistics and treat them as APIs version, documented, ruled and ensure that they are bound by consistent decisions such as $ 10 million budget release or go/no-go-product launches.
- Do not build internal interfaces – such as play stores and Clean Room APIs – as an infrastructure, but as real products with contracts, SLAs, users and feedback klussen.
- Say no to projects that feel advanced, but does not matter. A data pipeline that does not use a team is technical debt, no progress.
- Design for sustainability. Many data products do not fail due to poor modeling, but of brittle systems: logic without papers, flaky pipelines, shadow ownership. Build with the assumption that your future self – or your replacement – will thank you.
- Horizontal dissolution. Unlike domain -specific PMs, DPMs must constantly zoom out. The logic of one Lifetime Value (LTV) of one team is the budget input of another team. A seemingly small metric update can have consequences for the second order for marketing, finances and activities. Fighting that complexity is the task.
At companies, DPMs quietly define how internal data systems are built, arranged and accepted. They are not there to clean data. They are there to make organizations believe in it again.
Why it took so long
For years we have confused the activity with progress. Dataing 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 owned the answer.
Because executive decisions are now being mediated
In the current company, almost every important decision – budget shifts, new launches, org – restructures – first pass through a layer of data. But these layers are often not good:
- The metric version that was used last quarter has changed – but nobody knows when or why.
- Experiments Logic varies between teams.
- Attribution models are against each other, each with plausible logic.
DPMS does not have the decision – they have the interface that makes the decision readable.
DPMs ensure that metrics are interpretable, assumptions are transparent and tools are tailored to real workflows. Without them, decision paralysis becomes the norm.
Why this role will accelerate in the AI era
AI does not replace DPMs. It will make them essential:
- 80% of the AI project efforts still go to Data Readiness (Forrester).
- As large language models (LLMS) scale, the costs of waste entry connections. AI does not dissolve bad data – it strengthens it.
- Regulatory pressure (the EU AI Act, the California Consumer Privacy Act) is on organs to treat internal data systems with product trigor.
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 head of data, ask:
- Who has the data systems that make our biggest decisions of electricity?
- Are our internal APIs and statistics version, discovered, discovered and controlled?
- Do we know which data products are assumed – and which trust quietly undermine?
If you cannot answer clearly, you no longer need dashboards.
You need a data product manager.
Seojoon Oh is a Datproduct Manager at Uber.