Ahh, the life of a data-driven Product Owner. It’s easy to romanticize it as the domain of an analytical mastermind, a hybrid of Sherlock Holmes and a Excel-obsessed management consultant, orchestrating product strategy with the precision of an air traffic controller. The reality?
Picture a sleepless diplomat stuck between warring factions of business intuition and statistical reality, armed only with a dubious dashboard and an ever-expanding collection of spreadsheets.

During my years in product development and product ownership, I've seen both the power and the challenges of being data-driven. Data should be a given, an essential tool for making informed decisions. But it's not a magic wand. Numbers can reveal patterns, guide priorities, and reduce uncertainty, they cannot replace judgement, context, or human insight.

1: The data collection odyssey

The journey begins with an idealistic quest: to gather reliable, actionable data. In theory, data is objective, clean, and structured – a beacon of truth. In practice, it’s a Rorschach test, interpreted differently by every stakeholder in the room.

• Step one: request existing data.
• Step two: receive several spreadsheets updated a few years ago, filled with an unholy mix of inconsistent metrics and column headers written in three different languages.
• Step three: question the definition of “active user” for the tenth time in a week while a senior executive confidently references a data point that doesn’t exist in any system you’ve ever seen.

This is the data-driven equivalent of assembling IKEA furniture without the manual – except half the pieces belong to a completely different product.

2: The theater of analysis

Aka the circus of interpretation.

Congratulations! After some digital archaeology, you’ve uncovered something resembling usable data. Now comes the real test: making sense of it.

• The marketing team swears conversion rates are skyrocketing, while the sales report describes a black hole where customers should be.
• One dashboard says feature adoption is soaring; another warns that engagement is dropping.
• A stakeholder triumphantly shares a graph from an expensive consulting firm and asks why your numbers don’t look as impressive (without realizing the two datasets measure completely different things).

At this point, your role has evolved from product owner to high-stakes negotiator, trying to determine whether discrepancies are due to differing methodologies, faulty tracking, or the fact that Karen^[1] accidentally filtered out half the dataset.

And then there’s the data analyst - the guardian of statistical truth. To them, there is no reality outside of numbers. They watch in mild horror as stakeholders attempt to bend data to fit their narratives, clinging on to their SQL queries like sacred texts. Any attempt to introduce nuance, business context, or qualitative insights is met with a skeptical eyebrow raise. “If the data doesn’t show it, it doesn’t exist” they declare, entirely unbothered by the fact that half of the tracking scripts were broken last quarter.

Understanding a single metric is easy - understanding what it actually means is an entirely different sport. A 5% drop in users could be a UI issue, a seasonal dip or just customers finally found our pricing page. Without domain knowledge, data is just numbers.

3: The labyrinth of Data Maturity

Aka navigating bureaucratic depths.

Even when the numbers add up, another formidable obstacle remains: the organization's data maturity.

• “Why do we need data when we have 20 years of experience?” (Yes, the belief that “Data is just a trend”, “How hard can it be”, “I know it's like this.”)
• “Let’s A/B test everything!” (Including office lighting, the lunch menu and where to put the coffee machine.)
• “I need a single source of truth!” (Sure, right after we unify 12 tracking systems and convince finance to standardize the definition of “active user”.)
• “The data says we’ll grow by 200% if we just do this one thing!” (Where this one thing is an entirely untested feature with zero validation, but hey, optimism is free.)

Convincing an organization to embrace data-driven thinking is like trying to steer a cruise ship with a kayak paddle – progress is slow, resistance is inevitable, and every small victory deserves a celebration.

Conclusion: The art of being Data-Informed

After navigating the swamp of analysis, stakeholders, and ever-changing definitions, what’s the real takeaway? Data is powerful, but it’s not a magic wand. Not even the most sophisticated analytics stack can eliminate uncertainty, and no perfectly curated dataset can replace good judgment.

The true art of a Product Owner is not to be enslaved by data but to be data-informed – to combine data with intuition, experience, and the occasional strategic gamble. The best decisions come from embracing ambiguity, using numbers wisely, and knowing when to trust your gut (but always double-checking with at least one pivot table and Karen 😉).

Fellow Product Owners, step into the battle. Be the pirate or the diplomat. And when in doubt, let the data guide you – but don’t let it drive you insane.

^[1] No Karen were harmed during the writing of this blog post. Karen is a fantastic and competent colleague.