FAQ: What Skills Should a Data-Driven Product Manager Have?
There’s a lot of chatter about data and Product Management…we should know, we’re currently the ones chatting about it!
But sometimes it’s difficult to cut through the noise and find the answers that you’re looking for. So instead of picking one big meaty topic to dive into, today we’ve combed through our community for the most frequently asked questions about Product Managers and data.
What Skills Should a Data-Driven Product Manager Have?
Data-Driven does not mean all-data-knowing. A data-driven Product Manager is not the same thing as a Data Scientist who is also a Product Manager. A data-driven Product Manager is someone who…
- Know enough about data to ask the right questions and gather the right insights.
- Value data enough to make product decisions based on it.
And that’s basically it! Of course, the more you understand data and the more you’re able to manipulate and leverage it for yourself, the better and more impactful you’ll be as a PM. But being data-driven really isn’t as complicated and difficult as it may seem.
Some other skills that’ll be great to add to your data-driven PM toolkit are:
- Knowing how to work with Data Scientists, and how to speak their language.
- Being able to use SQL and/or Python to quickly gather your own insights, without needing to rely on data teammates.
- Knowing how to present and democratize data, as making it understandable and accessible increases team’s data fluency.
- Being able to prioritize roadmaps based on data.
- Using data to flesh out user personas and get a more accurate understanding of your customers.
How do Product Managers Use Data?
Product Managers use data in a variety of ways. Data isn’t just important for reporting your monthly OKRs and KPIs, it’s an important tool across many layers of decision making. Product Managers use data to:
Data is important to Product Managers not only because it helps them to build products that fill a need or solve a problem, but because it’s a powerful communication tool. When you need to explain why you’ve made a decision or why you have to say no to a request, it’s much easier if you have the data to back yourself up. Otherwise you’re just presenting your opinions.
What Metrics do Product Managers Need?
The metrics you need to track will depend on the questions you’re asking and the answers you’re looking for. But in general, the right metrics can help you keep tabs on the overall health of your product, and help to to measure success.
Common metrics that Product Managers need to track are:
- Monthly and Daily Active Users (MAUs and DAUs)
- Customer Conversion Rate
- Churn
- Customer Retention
- Net Promoter Score
- Customer Satisfaction Score
- Customer Lifetime Value
- Customer Acquisition Cost
- Monthly and Annual Recurring Revenue (MRR and ARR)
To see more about each of these metrics, check out These Are The Metrics Great Product Managers Track
There are also metrics that you can use in team leadership to track your team’s performance and wellness.
Do Product Managers Need SQL and Python?
SQL and Python are the programming languages most commonly used by Data Scientists. SQL is a query language used primarily for accessing data, whereas Python (which is used outside of data science as well) opens up more options for experimentation.
Product Managers are natural learners, and boost your data science skills, picking up one of these languages is a great direction to go in.
It’s important to only pick up one at a time, even if you desperately want to know both. Imagine trying to learn Russian and Korean simultaneously.
Follow Up: Should I Learn SQL or Python?
Figuring out which one is the next step.
First things first, figure out which one is going to be the most useful in your day-to-day work. If you have data experts at your company, or even on your team, ask them which one they would recommend. That’ll help you to know which one will give you that instant gratification of ‘oh my gosh, I can do this now!’
Generally, SQL is the most useful for data beginners, as it helps you extract the data you need in a very simple and methodical way. However, if you’re interested in coding in general, Python can be fun to play around with as it’s more multi-purpose.
If you can’t decide, find some free, introductory classes for both Python and SQL. Try them both out and see which one comes to you more naturally. You’re more likely to stick with learning the language that feels right for you.
Which Product Manager Roles Require More Data Skills?
Product Management job titles are constantly changing depending on the industry, the company, the team, and can even be re-defined by the Product Manager that steps into the role. A PM who happens to be a whizz at data can morph into a Data PM by lending their skills to the role.
It’s true that some entry level Product Manager or Associate Product Manager roles will require more data skills. This could be because:
- The company has no Data Scientist, and so relies more heavily on its Product Managers.
- The company has a data product.
- The company is specifically looking for a Data Product Manager, even if they don’t know it!
If data is an area you’re especially interested in or, conversely, not very confident about, it’s important to understand what will be expected of you.
Should I Become a Data Analyst or a Product Manager?
Both are valid and valuable job roles. But (take everything we’re about to say with a pinch of salt, we might be a little bit biased) Product Management is WAY more fun!
If you’re into data, but you’ve come to a Product Management blog to figure out your career path, then you’ve pretty much answered your own question!
The main thing you have to think about if you’re torn between these two career paths is not how much you want to be involved in data, but what it is specifically that you want to do with it. If you’re a bit of a control freak with data, and you want to be the one who owns it, manages it, and communicates it, you’re a natural Data Manager.
However, if you want to be the one who leverages data, and who decides how it gets used to build a successful product, you’re destined to PM life.
Check out: What Does a Product Manager Do?
How Should Product Managers Work With Data Scientists?
What your relationship with your Data Scientist/Analyst looks like (if you’re lucky enough to have one on your team) will depend on what your own data literacy level is.
- High data literacy: Your relationship with your Data Scientist has the potential to be highly collaborative, as you’re capable of being very hands-on, of diving into the data alongside them and of coming up with insights together. But be careful. Data Management is not your job, it’s theirs. Even if you know a lot about data, it’s important to let people do their jobs to maintain a good, resentment-free relationship.
- Medium data literacy: Use your relationship with your Data Scientist as an opportunity to learn more. Don’t be afraid to ask them questions, and to show you anything you’re curious about.
- Low data literacy: Don’t be afraid of your Data Scientist. Ignoring them because data scares you is not the way forward! Even complete newbies at data can have a great working relationship with data professionals, if you just know how to ask the right questions. You don’t have to speak their language in order to communicate, and you can keep them on your side by figuring out how to use data (and their expertise) to your product’s advantage.
Each professional is different of course. Some will want to keep you at more of an arm’s length, and others will be happy to welcome a collaborator into the fold. As a Product Manager, it’s important to understand and respect your teams’ boundaries, to ensure the most effective working relationship.
How Do I Move From Data Analytics to Product Management?
If you work in Data Analytics and you want to work in Product Management we’ve got good news for you. All paths lead to Product, but data is an especially good one!
Look for companies who are looking for Data PMs, as your data-heavy skillset will really work in your favor and may give you an edge against PMs who don’t have good data experience to leverage. Alternatively, look for Associate Product Manager (APM) roles, which will expose you to the product experience you lack while benefitting from your data experience.
In your cover letters/primary interviews, highlight how you’ve provided data driven insights and how they impacted the product lifecycle. You should also make sure you show off how familiar with product development you are.
If you haven’t worked with a product team yet, there are a few ways to grow your product experience. You could try taking part in a hackathon, or building your own side project. There are ways to show that you’re capable of being a Product Manager without having been one before. It’s all about flexing your transferable skills and getting as close to a product as possible.
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This article was also published on The Product Management Blog, where we share tips, tricks, and industry deep-dives for PMs of all levels.
