As a PM, you need the skill of data analysis. Why?
Part 1 of 3 of the series: Develop the critical PM skill of data analysis
As a PM, analyze data
As a product manager, you need to analyze data at least at a medium competency level. It’s one of the most important skills as a PM. Why?
Data is the language of modern business
Modern businesses, from a small neighborhood HVAC contractor to a large international conglomerate, rely on hard objective data to make everyday decisions. You need to be comfortable at communicating numbers and using data to advance conversations with all different stakeholders. As a PM, you should be primarily focused on usage numbers, since more people adopting your product is usually a pretty good proxy to indicate business success. But it’s also important to understand finance more generally and how that usage translates into actual dollars and cents.
You need to analyze data for product development and customer development
Chances are that at some time in your career, you’ll be working on a fairly mature product with hundreds, if not thousands of consistent paying users. In this case, even a minor change could significantly impact the product, by definition of how many users it effects. It’s thus critical to analyze trends in how users are interacting with your product and make decisions accordingly. As a rule of thumb, as your product becomes more mature, you should weigh data more heavily over say, gut instinct. The risk is just higher for every product iteration you make. This is data-driven product development.
With a less mature product (where you are regularly still adding new features and removing unsuccessful ones), data analysis prior to shipping a product change is still important. But since you are making educated guesses on new ideas, hard data should be weighed less heavily, and play an informative or directional role. Make room for innovation and challenge the status quo. Get domain subject matter experts and even adjacent field experts to chime in. This is data-informed product development.
With a very early stage product, your data analysis should be focused on individuals themselves. Analyze market trends. Perform user research with in-person interviews or structured surveys. This is customer development because you are developing an understanding of your existing and future customers. You’re not actually focused on the product just yet.
In all these cases, data analysis is used in different ways, but you still very much need to have the skill and perform it at least at a medium competency level.
You need to analyze data yourself even if there’s a full time data analyst
Some folks may think, I don’t plan on working in a startup. I’ll always be in stable and mature companies with dedicated roles like engineers, designers, and data analysts. So I won’t need to know data analysis since there will be experts doing it already. First, don’t artificially close doors in the future with a rigid mindset. Furthermore, as I explain in the next two parts of this series, it’s really not that hard of a skill to pick up and improve over time with practical experience. But most importantly, even in medium-sized companies, you may not even have a full-time analyst assigned to your product team, as opposed to developers and designers, who typically are embedded in a team. Data folks typically support multiple departments. For example, it’s common that the initial data hires in a company may be tasked with supporting the finance department, leaving little time to support product data needs.
More generally, an early career PM should get their hands dirty. You should be absorbing and practicing as many skills (technical and soft) as possible. You’re already doing a lot of coordination and delegation of technical tasks with developers, and sharpening those soft skills. So when it comes to data then, you should be as involved as possible. Don’t delegate to a data analyst. It’s your product.
When and how to analyze data
In parts 2 and 3 of this series, Develop the critical PM skill of data analysis, I plan on detailing the circumstances requiring data analysis and showing you the specific technical data skills to use in those cases. So if you want to read the rest of this series, please subscribe, so I’ll finish writing it! Let me know what other topics you’re interested in too. Contact me at firstname.lastname@example.org.
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I’m Victor Wu, Head of Product at Tilt Dev. I’ve been creating digital products for over 15 years, and mentoring folks for much of that time. Subscribe, and reply to email updates with questions you’d ask in a real-life mentoring session. I’ll answer them in future issues.