Common Misconceptions About Data-Driven Product Management

Data helps businesses build better products, that are used, and help people and businesses in meaningful ways. With so many companies embracing a more data-driven and less opinion-driven approach to product management, there are still some misconceptions about this approach.

Misconception #1: Qualitative data is not needed

Many product managers out there don’t use qualitative data. For some reason, they consider “data-driven” as the equal to “using numbers” and so the words of consumers are not seen as data. This is simply not true.

By ignoring qualitative data, these product managers miss the opportunity to fully understand the emotions of consumers, the why of their behavior which in turn could help explain some of the hard data. Not to mention words create empathy, a key ingredient for any team. Qualitative data goes in hand with quantitative data.

Quantitative data answers “What” (The conversion rate is 40% smaller on visitors with IE browser" ) while quantitative data answers “Why” (“The CTA is not displayed properly, I can’t see it on my screen”)

Misconception #2: “Looking” at data is sufficient

Sometimes when product managers run experiments, they say they’ll look into the data. The problem is, sometimes that’s exactly what they do. They look at the numbers and make conclusions.

However, that’s a wildly ineffective way to do analysis. You need to have a hypothesis and then design a way to test it while making sure the data collected is of high quality and only then start to understand the data within the framework of statistics. To neglect the first few steps of this process can lead to the wrong conclusions while you analyze the data.

Misconception #3: The “data-driven” miracle happens overnight

Well, it doesn’t. One day, the boss says “We need to be more data-driven” and from that moment on not much has changed. Being data-driven requires practice.

It takes time to build up the mindset, the analytical skills and make sure all the required infrastructure to support it is in place. You might design an inconclusive experiment, or end up snowed under so much data that analysis-paralysis discourages you.

You’ll get better over time and that’s normal. Start now, start little. With the mindset, everything else will follow.

Misconception #4: Gathering data is cheap or even free

Many companies want to be data-driven but do not invest enough for it. Being data-driven costs money.

Qualitative data requires time and money to collect feedbacks first hand from your users. Do you want product usage data? You’ll need to invest in a good product analytics tool. Even though some tools are free, high-quality data is not something that happens by accident. Designing, implementing and maintaining a robust tracking system can be difficult and expensive.

At the end of the day, data will help you build eliminate risks, uncertainty, to make a better product but it’s not a panacea. You’ll still need all the other ingredients, the most important one being the right people in your team, and the mindset for a data-driven culture to flourish.