Product Data Interview: Brent Peterson

This is part of our Product Data Interviews series, where we ask product managers and leaders across the industry the same set of questions about how they use data, what slows them down, and what helps them make better product decisions.

In this interview, Brent Peterson discusses his experience leading Content Cucumber, how he approaches data-related product decisions, and what it takes to turn insights into consistent action.

This interview has been edited for clarity and flow.

1. What kind of product decisions are/were you personally responsible for?

As CEO, I had ultimate responsibility for product decisions across the business. Day-to-day ownership sat with our product management team, including a lead PM. They managed the ongoing product work, while I remained accountable for the overall direction.

2. When you're evaluating whether something is working (or worth building), what signals matter most to you?

AI has made it much easier to build an app, which makes differentiation and going to market especially important when deciding what is worth building.

I look at two things: can you clearly explain what your solution does and how it differs from the alternatives, and do you have a plan to reach the market ahead of other teams building something similar? The ability to build the product is only part of the equation. You also need a clear reason for customers to choose it and a plan to get it in front of them.

3. Walk me through a recent product decision you made that involved data. What did the process actually look like?

I start with the practical questions that shape how we can use the data: where does it live, how will we access it, who needs to see it, and how much of it are we working with?

I also consider whether it makes sense to vectorize the data so it can be searched by meaning. Those questions help frame the work required to make the data accessible and useful for the people who need it.

4. How confident do you generally feel in the data available to you when making product decisions? What tends to increase or reduce that confidence?

I'm generally very confident in the data available to support my decisions. The range of models and information we can now search across gives me more ways to find what I need, and that broader access contributes to my confidence.

5. What's the most frustrating or time consuming part of getting the insights you need to make a decision?

Poor data quality and weak sourcing are the biggest frustrations. An answer is harder to use when the underlying information is unreliable or the citations don't lead to a valid source.

In my experience, that has been improving. I'm seeing fewer bogus links and fewer problems with citations than I used to.

6. How self-serve is data access for product managers at your company today?

We built an internal app that gives the team access to the data they need for their work. That's how we've approached self-service: providing a tool our team can use to get the information they need to do their jobs.

7. What's the hardest thing about turning data into action rather than just more dashboards or reports?

The hardest part is consistently identifying the right actions to take from the data. That requires a repeatable process for turning findings into action items and presenting them in a consistent format.

The structure around the insights matters. You need a way to make useful actions emerge regularly, rather than treating each report as a separate exercise.

8. Are there product metrics or definitions that people at the company regularly interpret differently?

I suspect that happens within teams, though I don't have a specific example to point to. Where I've seen it more clearly is with LLMs: if a metric isn't well defined, the model can interpret it differently than you intended.

That makes clear definitions especially important when you're using AI to work with data. You need to be explicit about what a metric means to get a consistent interpretation.

9. What's a surprising or overlooked source of product insight that you think more teams should pay attention to?

We use Google Search Console extensively, and it contains a wealth of useful data. One limitation that's easy to overlook is that its performance history only goes back 16 months.

If you start capturing that data in your own data store, you can build a much longer history over time. That gives you access to longer-term trends that would otherwise fall outside Search Console's reporting window.

10. What advice would you give another PM at a startup trying to make better product decisions with data?

Ask a lot of questions, and make sure you're hearing from the people involved. Talk to clients about what matters to them, then have those conversations with developers and other stakeholders too.

Understanding those different priorities gives you a better foundation for deciding which questions the data needs to answer.