I was asked recently the following question: “What’s a skill that you’re seeing as increasingly important in product management?”
When I started in product 20 years ago, a lot of user data was locked away because of the way software was being used. People spent hours every day working on client tools like Office and Adobe on their desktop computers that still weren’t Internet-connected. Companies had on-premise servers managing emails and document storage that didn’t relay information outside the company. The web was still a small part of people’s lives, and tools like Google Docs and Slack were years away. Getting qualitative user feedback took weeks of identifying users and hosting in-person focus groups or interviews.
Fast forward 20 years, and we now have a wealth of user data. People are spending most of their lives on cloud-connected devices and services for work and play, making it easier to receive large amounts of anonymized user behavior data. But also, the tools have gotten better. For example, there’s FullStory to review user sessions, Heap Analytics to do ad-hoc customer journey analytics, and UserTesting to get qualitative feedback from participants in a matter of hours.
This wealth of data creates a new challenge: synthesis. Product managers can get buried in all of this data, spending hours sifting through it trying to decide what’s important. The problem worsens if there’s conflicting data, such as user surveys or sessions disagreeing with web metrics. This can paralyze decision-making as product managers try to rationalize all of the conflicting data points.
The way through this problem, and the skill that will differentiate good product managers from great ones moving forward, is having a framework that enables one to decide what data is important and synthesize it into a cogent product plan quickly. A framework can quantify user feedback so one can understand what’s being heard most often. It can create a scaling system so a product manager understands what is most severe. It can minimize recency or familiarity biases that steer someone to overweight some data over others. It can guide someone to spend more time analyzing data if the resulting product decision is not easily reversible, or move more quickly if it is.
20 years ago, we wish we had the data we have now. Today, the key is to use a framework to make sense of data quickly and make better product decisions as a result.