Traditional data delivery models have collapsed in the face of scaling organizations
For a long time, businesses treated data teams like service desks, filing tickets, fetching numbers, responding to ad hoc requests. This “data-as-a-service” model came out of a simpler time, when teams didn’t have that much data to begin with. It was fine, back then.
But that model doesn’t scale. It breaks under pressure the moment a company starts investing real money in data systems. You can have a petabyte-scale warehouse and real-time streaming pipelines, but if everyone pulls a slightly different version of the same metric, you’re not data-driven.
Take Airbnb. Before they launched a centralized metrics platform, teams in product, operations, and finance each had their own interpretation of key numbers, like “nights booked” or “active users.” When leadership asked for answers, teams brought up different metrics, based on different filters, from different dashboards. So instead of deciding what to do next, meetings turned into debates about which number was “right.”
This drains trust. At scale, every number matters. When important teams don’t trust data, they stop using it. Or worse, they keep using it selectively to validate what they already believe. That’s how you end up with smart people making slow decisions.
Internal data systems weren’t designed with usability, version control, or decision-making in mind. And when you build systems that no one trusts, decisions stall, no matter how fast or flexible the infrastructure looks on paper.
Proliferation of dashboards has sown mistrust and forced inefficient decision-making
There are too many dashboards. Most of them don’t work the way people think they do. Different filters, different timelines, different assumptions. The result? Smart executives get three dashboards with three stories. That’s not insight, it’s noise.
Ask your ops team why churn increased last week. You’ll likely get multiple answers, each one based on a different dashboard. Ask finance to reconcile growth with attribution reports, and you’ll hear it depends “on who you ask.” And more often than not, none of them trust the numbers entirely.
It’s a lack of coherence. Analysts spend their days explaining small discrepancies. Engineers waste time rebuilding datasets that already exist, just with slightly different logic. Leaders hesitate to act because they don’t trust the inputs.
When there’s data everywhere but no clear truth, people default to intuition. That’s regression. Dashboards were supposed to accelerate understanding. But if they’re leading to second-guessing instead, something critical has broken.
Executives need to look past the volume of dashboards and ask a better question: what’s actually being used? Which systems are adopted, trusted, and directly tied to high-stakes decisions? If a dashboard can’t stand up to that test, it’s just noise. More dashboards aren’t the answer. Better ownership, coherence, and product thinking are.
The root problem lies in poor product design thinking applied to internal data tools
Too many companies treat data as a byproduct, something you store, process, and visualize, without treating it like a product anyone actually needs to use. The systems are technically sound. The SQL runs. The pipelines move. And yet nobody trusts the results. That’s because the tools weren’t built with people in mind. No clear definitions. No managed metrics. No version control.
You can’t expect users to believe in data if they’re constantly working around it. When two teams use the same term but apply different logic, confusion becomes the default. When dashboards look identical but show different results, trust erodes. And once people start bypassing official tools in favor of DIY spreadsheets and side channels, the damage spreads fast.
This is about invisible complexity. Internal data tools often lack consistent interfaces, user experience, and governance. They’re treated as deliverables, not experiences. There’s no clear ownership, no unified onboarding, and no real accountability for the outcomes. When systems lack interpretability, even perfect pipelines lead nowhere.
Decision-making demands clarity. That means designing internal data tools the same way you’d build customer-facing products, intuitively, reliably, and with clear contracts. Mistaking internal tooling for infrastructure is why organizations struggle. Until you apply real product thinking, versioning, usability, documentation, you’ll keep having the same arguments in every meeting.
The emergence of Data Product Managers (DPMs) offers a solution to these systemic issues
Data Product Managers are starting to show up in companies that take internal data seriously. And they’re not there to manage dashboards, they’re there to make sure data actually supports decisions. That means overseeing the full experience of internal data systems, from design to delivery to impact.
A good DPM doesn’t count success by how many dashboards shipped. They focus on whether the right people got the insight they needed, when they needed it, and whether it helped them make a better call. They define value based on whether someone’s workflow improved, whether a feature got launched faster, or whether a strategic pivot was made with greater confidence.
DPMs go deeper than just curating clean tables. They own core metrics like APIs, fully documented, versioned, and tied to real stakes like multimillion-dollar budgets or launch gates. They build internal tooling, feature stores, access APIs, cleanrooms, as actual products that people use, governed by real SLAs and feedback loops. If something goes wrong, they take responsibility and fix it.
They also know what not to build. A shiny pipeline that no one uses is technical debt, not progress. A new dataset that replicates existing logic adds risk. DPMs are there to say no to work that looks impressive but doesn’t matter, so the team can focus on what does.
This role works horizontally. Finance’s metrics affect marketing’s forecasts. Product’s adoption logic shapes operations’ planning. DPMs make sure that when data crosses functions, it doesn’t break. They zoom out, connect dots, and create shared understanding. That’s how you restore trust across leaders. That’s how your teams move faster, with fewer surprises.
Data product managers focus on impact over output volume
Most internal data work is judged by volume, how many pipelines delivered, how many dashboards shipped. But volume doesn’t translate to value. If those dashboards don’t influence a decision or improve a workflow, they’re noise. Data Product Managers work differently. They don’t celebrate delivery. They measure results.
A capable DPM is always asking, “Did this help someone do their job better? Did it lead to a faster decision, fewer mistakes, higher precision?” If the answer is no, the work wasn’t worth doing, no matter how technically sound it was. That mindset shifts the goal from activity to outcome. It pushes teams to stop building things no one uses and start solving problems that actually matter.
You’ll see the difference in what gets prioritized. Projects that look sophisticated but have no clear user are shut down. Redundant datasets don’t get rebuilt, they get challenged. Engineering hours aren’t spent chasing edge cases unless there’s a clear business requirement. And when tradeoffs happen, impact beats elegance.
DPMs keep the team focused on what matters, from first commit to production usage. They track data product adoption, user feedback, and business alignment. When something underperforms, they course-correct fast. That’s how they turn internal data into a trusted platform for decision-making, not just another backlog of deployments.
Executive decisions are increasingly mediated by data
Most major decisions now pass through a data layer. Whether it’s unlocking a budget, deciding to launch a feature, or restructuring a team, all of it is supported, validated, or challenged using internal metrics and models. If those data systems are inconsistent or unowned, the decision-making process starts to collapse.
Here’s the risk: last quarter’s logic changes silently. Metrics drift. Attribution models don’t align. One team believes the experiment was a success, another says it wasn’t. That’s dysfunction and it exists because no one owns the full picture. When every team builds in isolation, you end up with a fragmented system no one fully trusts.
This is a governance issue, instead of an infrastructure one. Every key metric needs clear ownership, someone accountable for how it’s calculated, versioned, and applied across teams. Internal APIs, dashboards, and data products need lifecycle management. You need rules around what counts as “truth,” because otherwise you have multiple, contradicting sources surfacing during high-stakes conversations.
DPMs fill that gap. They don’t own the decisions, but they own the interface, how data flows, how logic is standardized, how context is preserved. That ownership matters. It creates alignment. With a DPM in place, you avoid conflicting interpretations at key moments, and you get closer to real clarity when it counts.
Executives should be asking straightforward questions: Who owns the data powering our decisions? Are our metrics governed? Are our internal interfaces usable and trusted?
The rise of artificial intelligence intensifies the need for robust data management
Artificial intelligence is only as good as its data. That’s operational reality. Right now, most of the effort in AI projects isn’t going into training models. It’s going into cleaning, structuring, and aligning the data pipelines that feed them. According to Forrester, around 80% of AI project effort is still focused on data readiness. That number hasn’t dropped because most internal systems weren’t built to support this new level of precision.
As large language models and other AI tools scale across the enterprise, low-quality data becomes a direct threat to performance. AI doesn’t fix bad data; it amplifies its effects. A misleading metric or silent logic drift can lead to flawed outputs, fast. In regulatory environments, that’s more than a technical concern. It’s a compliance issue. Regulatory bodies like the EU (via the AI Act) and California (via the Consumer Privacy Act) are now pressing organizations to bring governance discipline to internal data environments.
That’s why Data Product Managers have become non-optional. They aren’t traffic coordinators or passive intermediaries, they’re the builders responsible for making sure foundational data systems are structured, governed, and trustworthy. In AI-heavy environments, DPMs are essential.
They ensure input data is reliable, logic is transparent, and governance is active. They set the contracts and feedback loops that make AI deployment sustainable at scale. Without that structure, AI performance will be unpredictable, and potentially damaging. If you’re betting your future on AI, you’d better start with disciplined data.
Executives must critically assess data ownership to restore trust and efficiency in decision-making
Executives spend a lot of time making decisions based on data, but far less time questioning where that data comes from, who owns it, or how well it’s understood. That gap is starting to hurt. Internal data infrastructure is growing too fast, and without clear ownership, it introduces more risk than reward.
If you can’t answer basic questions like “Who owns the metric that supports this budget decision?” or “When was this attribution model last updated?” then you have a governance failure, not a pipeline issue. Every executive should understand what data systems are driving decisions, and more importantly, whether those systems are being used correctly.
Data Product Managers are central to restoring that accountability. They bring ownership, structure, and transparency to internal data tools. They don’t focus on more dashboards, they focus on higher confidence. They track adoption, challenge low-impact work, and structure metrics with the expectation that real money, real customer experience, and real strategy depend on them.
At the executive level, the right question isn’t “Do we have enough data?” It’s “Do we trust the systems feeding our decisions?” If your answer isn’t immediate and confident, that’s the signal. More tools won’t fix the issue. Ownership and alignment will. Get a DPM on it.
Final thoughts
If your teams are drowning in dashboards but starving for alignment, the problem isn’t data volume, it’s how that data is owned, governed, and delivered. You don’t fix decision drag with another BI tool. You fix it by treating internal data like a product, not a warehouse.
Data Product Managers aren’t nice-to-haves. They’re the ones making sure your metrics are trustworthy, your internal tools are usable, and your company’s biggest bets are grounded in clarity. They see the seams that disconnect teams. They close them. Quietly, consistently, and with impact.
As decision-making becomes more data-dependent and AI tightens the need for clean, governed inputs, this role only grows in importance. So if your organization is still guessing which version of the truth to believe, it’s time to move past the toolkit, and invest in the people who make the tools work.
