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Interviews with expert PMs

So you want to pursue a career in product management—great! However, the product manager job posts you’ve come across list having product management experience—not great! Don’t worry! Here’s some guidance on how you can start your product management journey.

Considering a move from product analytics to product management? It’s a natural progression that reflects the desire to expand beyond data analysis and strategic recommendations into actively guiding the product development journey. This move combines analytical expertise with creativity to transform imaginative ideas into concrete, market-ready products.

Are you considering a career in product management? Before taking the leap, it’s important to assess whether this path aligns with your skills, interests, and goals. With product management roles varying across industries, product types, and company sizes, it can be challenging to determine if it’s the right fit for you.

Junior product managers face both opportunities and challenges in the constantly evolving tech industry. They are learning to oversee the development and launch of new products while developing a deep understanding of the market, user behavior, and technology. Despite the industry’s ongoing changes, the potential for these new product managers to shape the product’s future and make a significant impact is vast.

Although the term “product sense” may sound like just another buzzword, it’s important to understand the concept behind it. 

Having a strong product sense means being able to create solutions that truly address your customers’ problems. By developing your product sense, you can improve your ability to identify gaps in the market, anticipate customer demand, and create products that truly resonate with your audience. So while it may be tempting to dismiss product sense as an industry fad, taking the time to truly understand its value can have a significant impact on your product’s development and your own professional growth.

Data drives product management. Product managers rely on data for everything from creating an easy buying experience to determining if a product should even be developed. And while data is used to inform all aspects of managing a product, are there different considerations for using data for long-term strategy vs. daily decisions? 

It turns out that the answer is yes. While there are many similarities, like needing to ensure success is clearly defined, for strategic decisions more context, expertise, and qualitative data is required. Data used for strategic decisions tends to be higher quality, take longer to collect, and be more expensive than data used for everyday decisions. Data mistakes, such as bias or incorrect benchmarking, are also more costly to the organization when they happen in the context of creating a strategy.

A quantitative data-driven culture is generally seen as very positive. It’s great for understanding performance and tweaking strategy. Quantitative data is useful for reducing risk and can be used for decisions that need a high degree of certainty. These situations are usually high enough of a priority that adequate time is allotted to use data to verify various experiments before the decision is made. 

But there can be very real downsides. Data alone isn’t particularly useful for big transformations or concepts that don’t exist in the market. Henry Ford, founder of Ford Motor Company, famously said, “If I had asked my customers what they wanted they would have said a faster horse.” Ford’s customers actually wanted a faster way to travel, but lacked the language and vision to describe an automobile. Ford had to make the leap from “faster horse” to “car.” Data cannot do that.

Where a product manager works affects their life, skills, and career. Startups and enterprises, i.e. large IT companies, are at opposite ends of the tech company spectrum, but they do have some things in common. Core product management skills are used in both environments, and both places have motivated, smart, and skilled professionals that want to help their customers. Both settings offer rich experiences that grow talent, however they differ in the types of skills that are most developed. 

A startup teaches product managers how to move fast, tackle new problems, and wear many hats; an enterprise provides a chance to hone the product management craft and learn from successful experts in the field. Enterprises typically move slower than startups by design, and much of the extra time is spent communicating and negotiating with stakeholders. Because of the large user base, the impact of a product manager is usually broader than at a startup.

Product managers know that understanding their market and competition inside and out is vital to the success of their products. Comprehensive market knowledge tells you what problems your customers are trying to solve, what they want, and ultimately what new features or products to build. Knowing the competitive landscape helps to set your business apart, allows you to create a strategy to deal with new competitive developments, and helps to arm your sales team to win against the competition. 

When an organization says they are data-driven, they typically mean that they base decisions on data. But there can be vast differences with how data is used to make these decisions. Is data only being used to validate straightforward decisions? Are multiple sources of data combined with other factors to determine priorities like the features to be worked on next quarter? Or is an exploration of data being used to spark innovation and determine new strategy? Each situation requires different skills, tools, and ways of working with data to be successful.

This is why the concepts of data-informed and data-inspired are being added to the data-driven discussion; they allow for a more nuanced definition of how data is actually used in an organization. Data-informed and data-inspired decisions consider depending not only on data for clear-cut decisions, but on using data in conjunction with other important influences and to invent something new.

Some may argue that adding the terms data-informed and data-inspired to the data-driven discussion adds complexity and muddles the discussion around data. While that may be true in some cases, really understanding how to correctly use data based on a particular need is critical to creating products that customers love. In the end, the terminology isn’t as important as making sure you’re getting the most out of data.