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In the past decade, companies have spent billions on data infrastructure. Warehouse on Peta byte-paid. Real time pipelines. Machine Learning (ML) Platform.
And still – ask your operations why Mandor increased last week, and you will find three contradictory dashboards. Ask finance to reconcile with performance in attribution systems, and you will hear, “it depends on who you ask.”
In the world that drowned in dashboards, there is a truthful surfing: data is not a problem – product thinking.
The calm elimination of “a service as data”
For years, data teams run like internal consultation-a process, ticket-based, hero-driven. When the data applications were small and the stake was low, this “Data Service” (DAAS) model was fine. But as companies became “data -driven”, the model was broken under the weight of its success.
Take Air BNB. Prior to the start of their matrix platform, products, finance and OPS teams pulled their version of their matrix such as:
- Books at night
- Active user
- Available list
Even simple KPI is different from who was asking filters, sources and who was asking. In leadership studies, different teams presented different numbers – which resulted in the arguments that the matriculation was “correct” rather than what to do.
These are not technology failures. They are product failures.
Results
- Data Disadvantages: Analyst is estimated to be another estimated. Dashboards have been abandoned.
- Human Routers: Data scientists spend more time explaining more contradictions than creating insights.
- Uncontrolled pipelines: Reconstruct similar datases in engineers teams.
- Decision drag: Leaders delay or neglect the proceedings due to contradictory inputs.
Because the data trust is a product problem, not technical
Most data leaders believe they have a data quality problem. But look closely, and you will find the Data Trust problem:
- The platform of your experience says that a feature hurts to maintain – but the product leaders do not believe it.
- The OPS sees a dashboard that contradicts their living experience.
- Two teams use the same metric name, but different logic.
Pipelines are working. The SQL is stable. But no one trusts the results.
This is a product failure, not engineering. Because these systems were not designed for use, interpretation or decision -making.
Enter: Data Product Manager
Data Product Manager (DPM) – A new role has emerged among top companies. Unlike the generalist PMS, DPM works in the Breittle, invisible, cross -function region. Their job is not to send a dashboard. This is to ensure that the right people will have the right insight at the right time to decide.
But DPMS does not refrain from piping data in dashboards or curating tables. The best people move further: they ask, “Is this in fact helping someone improve their work?” They explain success, not in terms of output, but in terms of consequences. No “Was it sent?” But “did it improve the quality of one’s workflow or judgment?”
In practice, it means:
- Don’t just explain users. Witness them. Ask how they believe that the product works. Sit down with them. Your job is not to send a datastate – this is more efficient to your user. This means that deeper it means how the product fits in the context of the real world of their work.
- Owners of a Canonal Matrix and treat them like APIs-documentation, documentary, documentary, government-government-and make sure they are linked to fruitful decisions such as $ 10 million budget unlock or go/new product launches.
- Create an internal interface – such as feature stores and clean room APIS – not as real products with contracts, SLAS, users and feedback.
- Don’t say plans that feel sophisticated but it doesn’t matter. A data pipeline that no team uses is a technical loan, not development.
- Designed for durability. Many data products fail, not bad modeling, but easily from broken systems: non -documentary logic, astronomical pipelines, shadow ownership. Make it with the assumption that your future itself – or your place – will thank you.
- Solve horizontally. Unlike domain -related PMS, DPM should zoom out. One team’s Lifetime Value (LTV) logic is the other team’s budget input. Apparently minor metric updates can have other order results in marketing, finance and operations. It is the responsibility of this complexity.
In companies, DPMs are quietly explaining how internal data systems are created, govern and adopt. They are not available to clear the data. They are there to believe the organizations again.
Why did it take such a long time?
For years, we misunderstand the activity for development. Data engineers made pipelines. Scientists made models. Analysts made dashboards. But no one asked: “Will this insight really change a business decision?” Or worse: We asked, but no one had the answer.
Because executive decisions are now mediocrely mediation of data
In today’s enterprise, almost every major decisions – go through budget shifts, new launches, org reorganization – the first data layer. But these layers are often foreign:
- The last quarter’s used matriculation version has changed – but no one knows when or why.
- The logic of experiments is different in teams.
- The models of attribution contradict each other, each with proud logic.
DPMS does not own the decision – he owns the interface that enables the decision.
DPMs ensure that the matrix is able to translate, assumptions are transparent and tools are associated with real workflow. Without them, the decision becomes normal.
Why would this character be faster in the AI era
AI will not replace DPMs. This will make them mandatory:
- 80 % of the AI project still goes on data manufacture.
- As a large tongue model (LLMS) scale, the price compounds of trash inputs. AI does not fix bad data – it increases it.
- Regulatory Pressure (the EU AI Act, California Consumer Privacy Act) is strictly emphasizing the orgs for the treatment of internal data systems with the product.
DPM is not a traffic coordinator. They are the foundations of trust, interpretation and responsible AI.
So what now?
If you are head of CPO, CTO or data ask:
- Who owns the data system that strengthens our biggest decisions?
- Is our internal APIS and matrix forming, discovering and governance?
- Do we know which data product has been adopted – and who is quietly damaging confidence?
If you can’t answer clearly, you do not need more dashboards.
You need a data product manager.
Seojon is a data product manager in Ober.
