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Interviews with expert PMs

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. 

While most product managers know that using data in their daily activities can have tremendous benefits, sometimes they find themselves in an environment where doing so is not easy. Perhaps they don’t have access to the data they need, the data is unreliable, or there is no support in place to incorporate data into their processes. These product managers are not in a data-driven culture.

A data-driven culture is when an organization embraces data to make decisions at all levels. The organization has the infrastructure and talent needed to collect, transform, and analyze data, along with reliable and trustworthy data sources. There is an importance on using data to support hypotheses and resolutions. Data-driven cultures embrace data and bake it into their everyday processes.

But a data-driven culture doesn’t just happen on its own. It needs both top-down and bottom-up support in the organization. Upper management must make the decision to invest in data and infrastructure while the teams must believe that using data in their daily jobs is beneficial. And while data enthusiasts in an organization can plant the seed, the entire organization’s support is needed for a data-driven culture to blossom.

Product managers use data at the heart of their decision making. There are two types of data that they rely on: quantitative and qualitative. Quantitative data refers to numerical data that can be measured; examples include number of clicks, number of users, and monthly recurring revenue. Qualitative data cannot be counted, but instead describes traits or features such as ease of use, user likes and dislikes, and motivations behind user actions. 

Metrics help companies to achieve goals and transform business by pinpointing areas for improvement. But sometimes, metrics can actually be harmful and counterproductive. Excessive focus on metrics can cause product teams to neglect customers’ needs.

Many people have made a successful transition from an engineering career to a product management one. These two paths have a lot in common. They’re both focused on meeting customer needs and building great products. The two roles must work together to ensure the right solution is built. But of course there are differences. Product managers focus more on the “why” and the “what” while engineers focus on the “how.” Product managers uncover unmet customer needs and create a vision to address them, while engineering actually builds out that vision.

As more companies aspire to be data-driven, the role of the data analyst is becoming crucial both to the organization and to product managers themselves. In fact, the World Economic Forum found that the data analyst/scientist role had the most increase in demand in 2020. Clearly these positions are incredibly needed.

What does a data analyst do? A data analyst is responsible for gathering, organizing, and interpreting data to provide business insight. Typically this insight is used to solve an issue, make a decision, or determine performance. Simply put, a data analyst interprets data to drive better business outcomes, which is exactly why product managers must collaborate with them effectively.

Product/market fit is the make-or-break factor for a company. It helps businesses understand whether their product has market appeal and they can dive into the product growth stage with confidence.

Working with data helps companies across the board to unlock their potential and become more productive and better at making decisions. However, making people in the team and company rely on data involves a lot of work. Product managers must often set a strategy, reinvent processes, and change organizational behavior. 

Data is an essential part of the work of every product manager. It helps to form and validate hypotheses, provide more insights about user behavior, and make better decisions and track product changes.

But the misuse of data can be harmful. One important example is selecting only data that confirms a particular hypothesis and ignores relevant contradictory evidence.