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Interviews with expert PMs

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.

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. 

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. 

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.

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.

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.