TEI 258: How product managers can work effectively with data scientists – with Felicia Anderson & Rich Mironov

Be prepared for the intersection of data science and product management

Organizations are developing robust data science capabilities, adding the role of “data scientist” to their ranks. As the importance of data science increases in organizational strategy analysis and operations, it is also impacting product management. Product managers are being asked to work with data scientists. We are still at the forefront of this and figuring out how product management and data science intersect.

To explore the topic, we are joined by two past guests who have been working at this intersection. In episode 117 Felicia Anderson shared how she was building a product management council at Piney Bowes and in 055, Rich Mironov shared how product managers can navigate organizational challenges. For the past year, they have been helping product managers work with data scientists.

If this topic isn’t impacting your product work yet, it will in the future. This is information you need.

Summary of some concepts discussed for product managers:

[2:19] What are some examples of how you use data science as product managers?

In commerce services, data science can predict where shipped parcels are and which are at risk of being delayed, and determine when volumes of parcels will arrive so the company receiving them can optimize staffing and other resources.

One trend I see is that instead of using data analytics to give ourselves internal insights that we then hard-code into our applications, we’re using AI to build data analytics into the products themselves, such as using natural language processing to spot trends in long-form text documents. Software can make recommendations to the end-consumer. The challenge is that this kind of data analytics is never perfect. You have to consider edge cases and problems that might occur if the software makes a bad recommendation or data is missing. Product managers need to think about the difference between type one and type two errors. If we tell somebody a thing’s going to happen and it doesn’t, what are the bad outcomes? If we tell somebody it’s not going to happen and it does, what are the bad outcomes? You want your errors to collect on the side with less damage.

[11:23] How do you bring data science and product management together?

Sometimes the business leads us into data science. In other cases, you build the data science teams and bring the product managers and business side onboard. You have to pair up the product management knowledge with the data science team because neither half can make it work alone.

[12:36]  Do you usually see data scientists in product management teams or more separate?

I’m mostly seeing them separated, but if a company is building data science products, like using machine learning, then data science is a core part of engineering. A data science team for internal insights tends to be a separate team that investigates problems brought to them and spots trends. Then they have to find the internal consumer who cares about what they found, which brings them back to the product managers who know what they need for their product. When we leave data scientists in their own separate department, what they learn is not very valuable because most of the company finds it totally obvious. On the other hand, product managers and others come up with crazy, fictional ideas about how to apply data and need data scientists to bring them down to earth.

[14:57] How can product management become excessively data-driven to the detriment of good product management processes?