We know that data plays a massive role in how our society functions. Data is the collection of facts and statistics. It can come in different forms and is collected consistently. If you have a smartphone, your smartphone collects a lot of data from you. This is how it provides you with a tailored-to-you user experience. Almost any industry gathers data to help make decisions and accurate predictions from areas like products and services to sales and revenue. The product management field is no exception.

As a PM, data analytics is a critical part of what you do, and it means a lot when it comes to your product’s success. It’s important to remember, however, that collecting and interpreting data can be challenging, and misinterpretation happens. So if you’re seeking more knowledge when dealing with data, formal training can be an option. Let’s break it down more:

Data-Driven Product Management 

What is data-driven product management? A Medium article by Towards Data Science contributor Luciano Pesci states, “The product managers who seemed to be the happiest all came from organizations where data wasn’t just a priority, it was a deep part of the decision-making process at every level (some product managers took their current job BECAUSE of this fact).” Data-driven product managers base most of their responsibilities and decisions on collected data; they view data as the core of product development. 

Working with data requires PM’s to have a strong understanding of data infrastructure, data modeling, and both statistical and machine learning, in addition to the equally important traditional qualities of a PM. A data-driven PM knows that building successful products with data requires much more than collecting and putting it into a data warehouse. It requires building a data strategy–laying out a plan for how it’ll be used to improve a product or service.  

Data Analytics Categories 

As we mentioned earlier, data comes in all different forms, and it’s generally grouped into categories. 

Data Points

Individual points of data (metrics) that are collected and measured on a specific date and time. 

Segmentation

Put users in groups by common characteristics and usage patterns. Typically focuses on technical data, behavioral data, and demographic data. Segmentation can also be customized. However, it is crucial to make sure you’re using measurable characteristics....

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