Data is at the heart of product management: from forming and validating hypotheses to designing and running experiments to measuring the impact of product changes and understanding the market dynamics.

To find out more about the different aspects of data skills in product management, we spoke to experienced product managers from different companies and industries. Their comments will give you a good understanding of the following:

  • What data skills are important for product managers and why?
  • How do companies evaluate the data skills of people they want to hire for a PM position?
  • How can you improve your data skills?

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We would like to thank all the product managers who shared their experience with us and helped us answer these important questions.

  • Tabish Gilani (Senior PM at Replit, Ex-YouTube Growth manager, ex-Google Product Marketing manager)
  • Dolff Hanke (Product Manager at Etsy, online marketplace for self-made goods valued at $27bn)
  • John Milinovich (Product Manager at Clubhouse, valued at nearly $4bn in a funding round)
  • Andrea Grimaldi (Product Manager at EZCORP, one the biggest American pawn shop operators providing services across the US and Latin America)
  • Christoph Wilhelm (Product Manager at Alasco, Munich-based financial management platform that recently raised €35m)
  • Rebecca Gross (VP of Product at LeagueApps, the leading youth sports management and online registration software )
  • Bryce York (Director of Product at Tatari, Data-driven TV advertising with Calm, Fiverr. and others as clients)

Q: What data skills are important for product managers and why?

The key data skills you need as a product manager depend largely on the product you’re working on, the tools you use, the team structure, and other factors.

However, we have been able to identify some key areas where you should have good skills in any case:

  • Mapping data to user needs, market trends, and product changes
  • Querying/manipulating data and understanding database structures
  • Statistics and math
  • Data visualization and storytelling

Let’s dig deeper into each one of them.

Skill #1: Mapping data to user needs, market trends, and product changes

Many people think that querying, manipulating, and visualizing data are the most important data skills of a product manager. However, most experienced PMs we spoke with agree that their key skill is data intuition, which enables them to make inferences about users, business, and product value based on the data.

John Milinovich (Product Manager at Clubhouse)

Intuition is the most important data skill for PMs. While some PM roles might require deeper technical skills (ie SQL or Statistics), all PMs need to have good instincts when it comes to data. For example, a PM doesn’t need to calculate the p-value for an experiment, but they must absolutely be able to reason about what they’re seeing and develop an opinion about why metrics are moving in the way that they are.

Rebecca Gross (VP of Product at LeagueApps)

PMs must understand the value of data and how it can help with insights on users, the market, and the company’s business. PMs that have this mindset search for ways to get good data and leverage that for better product decisions for problem identification, roadmaps, prioritization, goal setting, and measuring success.

Skill #2: Querying/manipulating data and understanding database structures

SQL or other technical skills for working with data and databases are not must-haves for product managers. But several people we spoke to believe that such knowledge can come in very handy, especially in smaller companies where you do not have a dedicated data team. Even in big teams, such skills enable you to explore data and answer your questions by yourself, which makes you more effective since you depend less on others.

Bryce York (Director of Product at Tatari)

For a well-rounded PM, I’d recommend having good hands-on skills with SQL and data visualization in particular. There’s immense value to getting into your product’s database, navigating the schema, and drawing insights about product usage. You can apply these same skills to your product analytics if you’ve set up the right tools.

Andrea Grimaldi (Product Manager at EZCORP)

SQL basic knowledge has been highly useful in my journey. I don’t need to wait for people sometimes to provide data to me if I can go and get it.

Skill #3: Statistics and math

While as a product manager, you don’t have to be an expert in math and statistics, you should have a solid grasp of fundamental concepts such as confidence intervals, statistical significance, and the difference between correlation and causation. This knowledge usually translates into good intuition. Statistics knowledge is also crucial to designing and setting up experiments, and it helps you better communicate ideas to data analysts or data scientists on your team.

Rebecca Gross (VP of Product at LeagueApps)

A nice-to-have is a more in-depth understanding of statistics for descriptive and inferential statistics so that they can not only understand the meaning of charts, correlations, etc. but do some basic calculations using available tools like Excel, Google sheets, Jupyter Notebook, etc.

Dolff Hanke (Product Manager at Etsy)

Stats and math generally is important for making sure a PM can understand the data they’re seeing, setting up experiments in a way that generates the right learnings, and working closely with ML partners.

Andrea Grimaldi (Product Manager at EZCORP)

Statistics is a must to understand the results of your experiments and study correlation of events, validation of hypothesis, etc.

Skill #4: Data visualization and storytelling

Being able to describe your data in charts and graphics will make sure that you and others have a better understanding of your product. Visualization is especially important when you want to communicate your findings to people who do not have deep knowledge of data and statistics. Equally important is storytelling, the ability to connect the dots in your data and develop a narrative that helps others understand how changes happen and how they can be influenced.

Bryce York (Director of Product at Tatari)

Today, any good PM is expected to bring data to the conversation. That data can be both quantitative and qualitative. When it comes to quantitative data, you’ll almost certainly want to present it visually. A good understanding of data visualization will help you tell your story and make the data both valuable and intuitive. At the very least, you should be aware that pie charts aren’t exactly popular among those in the know.

Rebecca Gross (VP of Product at LeagueApps)

Having knowledge of at least one data visualization tool like Tableau, Looker, Amplitude, MixPanel, Heap, etc. can help PMs analyze and present data for product proposals and decisions.

Other data skills for PMs

Here are some other data-related skills that the GoPractice team thinks are useful in product work:

  • Know the limits of your data: Depending on how you look at your data, you can draw right and wrong conclusions. You should be able to know when your data is misleading and should be further complemented by other sources of information.
  • Gather data from public sources: You should be able to work with platforms that provide data on other products to form/validate your hypotheses
  • Data science and machine learning: With products having data that is increasingly rich in volume and context, training and machine learning models can help a lot in simulating and forecasting user behavior and the effect of product changes.
  • Data engineering: Data engineering will help you create your own data pipelines, pull data from different data stores, and access them in a unified way, which can help a lot in creating data models and experiments.

Q: How do you evaluate the data skills of people you want to hire for a PM position?

When it comes to hiring product managers, the best candidates are those who can show their skills in interpreting data and using it to make decisions. While hands-on experience with data and analytics tools is highly crucial, most experts we spoke to were more interested in the candidate’s high-level skills in using data to develop hypotheses, design experiments, make decisions, etc.

They used one or more of the following methods to test the data skills of potential hires:

  • Scenario-based tests: The PM applicant is taken through a scenario, in which they must use data to come up with and test hypotheses, draw conclusions, and communicate results.
  • Problem-solving tests: The applicant is provided with some sample data and asked questions that require querying/processing skills and an understanding of data structure and relations.
  • Experience tests: The applicant is asked to recite specific experiences they’ve had in working with data.

Tabish Gilani (Senior PM at Replit, Ex-YouTube Growth manager, ex-Google Product Marketing manager)

For evaluation – the biggest thing I’m looking for is how do they interpret the data. Are they wary of the data, do they trust it, how do they build confidence in the data they see, and what other sources do they look at to mitigate bias and risk in their analyses. Some of these are best evaluated through case study or hypothetical scenario questions asking people to think deeply about the metrics and how they would react based on how certain metrics change/drop/increase.

John Milinovich (Product Manager at Clubhouse)

The way I evaluate data skills is dependent on the role. In general, I like to talk through a scenario that involves analytical rigor such as analyzing an experiment or understanding a growth funnel.

Rebecca Gross (VP of Product at LeagueApps)

In interviews, I ask candidates about how they integrate data into their decision making for identifying problems, roadmaps, setting goals for hypotheses, running experiments, measuring success, and prioritizing for iterations.

Bryce York (Director of Product at Tatari)

In our interviews, we include a couple of data-related questions that look to evaluate how the candidate thinks about data for a typical PM role. For example, what data would you want to use to calculate our company’s average customer lifetime value? For more data-heavy roles, we have a dedicated interview stage that presents them with a spreadsheet of hardcoded data. We then have them reason how the columns relate and identify the potential root cause of certain anomalies (and see if they notice them before we point them out). Above all, we focus on evaluating how you think about numbers and data rather than testing specific technical skills like asking you to write a SQL query on a (virtual) whiteboard.

Q: How do you improve your data skills?

As the product landscape continues to evolve and change, product managers should constantly hone their data skills and learn new ones. Here are the key recommendations from experts we spoke to:

  • Improve your data intuition skills: You can achieve this through practice, working with team members who have different skillsets and backgrounds, studying other people’s work, and reading books from experienced product managers and thought leaders.
  • Learn new hard data skills that you can apply in your work: Whether it’s new programming languages such as Python and R, new analytics tools such as Amplitude and Tableau, or data hosting/querying platforms such as Google Bigtable and BigQuery, see what new tools your organization is employing and try to learn those that will give you the edge in your work.

John Milinovich (Product Manager at Clubhouse)

For PMs looking to improve their data skills I’d recommend finding an interesting data set and coming up with a question you want to answer from that data.

Tabish Gilani (Senior PM at Replit, Ex-YouTube Growth manager, ex-Google Product Marketing manager)

I find interesting datasets at work or tables to run some analyses on and see if I can find something interesting. Another very valuable tactical thing for me is trying to read other people’s queries and breaking them down to see what each part did (kind of like taking apart code blocks).

Bryce York (Director of Product at Tatari)

I built my data skills primarily through curiosity! I find this aspect of the product world really interesting and rewarding, so finding motivation and trying different resources wasn’t too challenging.

Tools like DataCamp and GoPractice are invaluable for self-directed learning. I’d also recommend combining self-paced learning with “pair programming” style sessions with people from your company or network who have more of the skills you desire and work through problems together.

Dolff Hanke (Product Manager at Etsy):

  • I learn a lot from working closely with my analytics partner.
  • When asking for data, I also ask for the SQL used to get it (when applicable) so that I could tweak the query to apply to similar future cases.
  • Talk through what you’re trying to learn and how you’re approaching it with others. Talking out loud helps to improve your thinking.