Twenty years ago, during the dot-com boom period, everyone was building websites and data analysis was much of an afterthought. To understand outcomes, we started using spreadsheets to keep track of some basic metrics. However, there were two challenges with the spreadsheet approach. Obviously, the spreadsheets grew un-manageable quickly and secondly, the data analysis was not real-time. Hence, the need for online, real-time data capture and data representation began. Omniture (now Adobe Analytics) and Google Analytics solved that problem. In fact, a large part of the industry continues to use these tools today.
Product mangers are spending a lot of time on these tools. All they should care about are their KPI goals. AI tools based on causal inferences today are smart enough to tell what’s important to try and achieve these KPI goals.
I have successfully used data science techniques to grow products while not spending hours on data analytics. Will love to share with the community.
- Why is current way of using product analytics wrong
- How data science can help PMs save hours every day
- How should you think about data driven product management