How Data-Literate PMs Succeed
A mashup of the most data-literate PM I can imagine
I originally published this on LinkedIn in April 2025, and I felt it was worth sharing here. I’ve lightly edited this version (but the energetic penguin stays).
Everyone should use data to do their job.
That, to me, is the real goal of data literacy. It’s about confidently applying data to make better decisions, regardless of your role.
As I’ve been reading more about data literacy, I often see it framed as a way to reassure people that data isn’t scary, confusing, or intimidating. Instead, analytical skills should be accessible and helpful. That’s fine, but I’m not so convinced the real issue is fear. In my experience, the problem is ambiguity. Many people simply don’t have a clear picture of what it looks like to use data well in their job.
Of course, company culture plays an important role here. I’ll acknowledge that in some organizations, analytics might still be seen as mysterious, highly technical, or out of reach. But in data-savvy companies like the one where I work, this is my take: people aren’t scared of data. They just need examples of what good looks like.
A whole lot of fluff can be written about data literacy if the only objective is to say “don’t be scared.” I believe a more productive path is to show people what’s possible. That means moving beyond the data team and looking at how data is actually used throughout the business. A strong data literacy initiative is not meant for the data team itself - it’s meant for everyone else. I’m certain that’s where the real opportunity lies.
Let me give you a concrete example.
What does a data-literate product manager (PM) look like? How do they behave in their role when it comes to data? Over the course of my 25-year career in analytics, I’ve worked closely with PMs at every stage of product development, from startups to large-scale enterprises. In just the past decade alone, I’ve supported organizations with dozens of PMs, including one with more than 80. The most data-driven PMs I’ve worked with share a set of behaviors that stand out. What follows is a patchwork of the best I’ve seen. A Frankenstein’s monster, if you will, of the most data-literate PM I can imagine.
To be clear: I’m talking about PMs who own and build customer-facing products and features. I know some PMs work within data teams themselves, but that’s not who this article is about. I’m focused on PMs responsible for externally facing products, regardless of where they sit in the org, because that’s where data literacy efforts have the most room to grow and the most impact to offer.
So what does the most data-literate PM do? Analytical skills are woven into every part of their work:
KPIs: They take an active role in defining the KPIs used to measure their product’s performance. They know exactly how those KPIs are calculated. If needed, they’ll write business requirements for data collection, then partner with engineering and data teams to implement them. As a result, they’re seen as the go-to authority on their product’s performance metrics.
Goal-setting and strategy: They create clear, ambitious, and achievable goals centered on those KPIs. For example, they might aim to increase the percentage of eligible customers using their product from x% to y% by the end of the year. Their strategic planning is grounded in data, and they use it to prioritize their product roadmap. Their decisions are backed by quantitative logic, which earns executive trust and support.
Reporting: They actively monitor their product’s performance and are rarely surprised by emerging trends. They understand all available dashboards and self-service tools and know how to use them. They might even write SQL queries themselves - AI assistance makes this easier than ever. When gaps in reporting exist, they advocate for improvements. They only bring in the data team when they’ve reached the limits of their own tools. This reinforces their credibility and improves decision velocity.
Analysis: They synthesize information to understand where their product is succeeding and where it’s falling short. For day-to-day insights, they lead the analysis themselves. This frees up the data team to focus on more strategic work. For deeper questions, they partner with analysts or scientists to run studies using more sophisticated techniques like causal modeling. Both the PM and the data team are proactive in shaping analysis plans. They work together to identify opportunities, surface blind spots, and drive decisions based on evidence.
Experimentation: They own their product’s A/B testing program, from prioritizing test ideas to interpreting results. They collaborate with the data team to design and analyze experiments, but the vision comes from them. Their experimentation strategy centers around achieving the quantitative goals they’ve set for their product.
ML models: If their product involves personalization or recommendations, they define business requirements and partner with scientists to build and productionalize the model. Once deployed, they monitor performance and work with scientists to make improvements.
Data quality: They hold a high bar for the data connected to their product. They collaborate with engineering and data teams to maintain accuracy and completeness because their ability to use data depends on its quality.
The key here is partnership. Data-literate PMs know when and how to collaborate with the data team to get the most out of it. They bring clear questions, well-defined goals, and product context that makes the data team’s work more effective. In doing so, they elevate both their own impact and that of their analytics partners.
By contrast, a less data-literate PM might take a passive approach to KPIs, rely on the data team for basic questions, or prioritize roadmap items based on instinct rather than evidence. They aren’t ineffective, but they’re certainly falling short of their potential. At the same time, PMs who take a “data above all else” approach can get just as stuck as those who ignore it entirely. Gut instinct is important, too.
At this point, it’s worth acknowledging that the role of a PM is already demanding. Adding a layer of analytical rigor may seem like overkill. But data-literate PMs often find that it gives them a competitive advantage. They’re able to reduce wasted effort, spot problems earlier, and make stronger cases for what they want to build. When they collaborate with the data team, their partnership is more focused, more productive, and more likely to drive results.
What does all this have to do with “data culture”? When data-literate PMs succeed because of the way they integrate analytics into their work, they get noticed. They become examples for others to follow. Over time, their approach becomes the default expectation for the role.
The best PMs I’ve seen have a great deal of ownership over the data part of their job, and they refuse to outsource it to others.