Suneet Nigale’s Post

I recently came across this post, it resonated with me on several levels. In past roles, I had faced the challenge of being asked to "provide more data." However, without the proper measurement tools in place, we struggled to obtain the necessary data. It was frustrating to encounter glaring problems without the supporting data to address. As I transition into a new role, I am striving to strike a balance between developing the right data and incorporating anecdotes to drive progress along the way. During recent stakeholder interviews, the recurring theme of communication gaps was brought to my attention. I didn't need to conduct a formal survey to gather this information and track progress against it; rather, I heard about it multiple times and observed gaps in the process. We addressed these issues without relying on a specific metric; it simply needed to be done! While data is certainly valuable and insightful, it's important not to overlook the significance of anecdotes along the way.

A great reminder about data-based decision-making. Data is always multifaceted and really easy to misinterpret depending on the perspective you use. There are many ways to validate your data perspective and making sure it aligns with what people are saying ("anecdotes" in Jeff's language) is essential. If your data tells you one thing but your people tell you another, that is a flashing red light that your data perspective needs validation.

When you work in Tech, it’s always, “Follow the data”, “The data is always right”, “What does the data say?” Data. Data. Data. But once you’ve been in the engine room, you come to learn how data is *selectively* used to make incorrect points of view sound reasonable. Data is not fact. It’s just as prone to bias as opinion. It’s why I love this story from Jeff Bezos about anecdote vs. data – and how when the two diverge, the anecdote is more often an accurate signal of what’s going on. Data is critical but so are people’s stories and experiences to diagnose underlying truths.

CRO Tip: I don't believe there's such thing as a 'losing' test The objective is data Data helps make decisions No matter what, you learn something What you learn from a losing test often turns into a big win The key is consistently learning from & iterating from the data

Let's forget about data. Data is a distraction. The question isn't: "What interesting insights can you find in this dataset?" It's: "What will you do if I give you interesting insights?" For example, if I tell you that user_XYZ is likely to churn out of your platform/service, what are you going to do about that? If there's no thoughtful action or experiment, then let's move on to the next thing. Once we find something interesting that we could do, some lever we could pull, conditional upon having the right data and information, then let's invest our efforts there. That's where data analysis should start... with thinking, not data.

Because you should be fearless when it comes to your approach to data.

An introduction to data is very useful for explanations.

The things that we measure tend to improve. This is why I’m so passionate about data.

What is the importance of clear data?