Product managers can prepare themselves by getting some formal education on data science. I did this a couple of years back by getting certified on courses meant for data scientists. However, I believe that may not be necessary and may not be feasible for most product managers.
Last year, John Hopkins University released a certification in data science for executives. This is a good course for product managers to take. I took it this year and found it very valuable. Since then, I tried out some of the concepts in the real world. The concepts taught in this course work in the real world. I highly recommend this course for all product managers planning to or aspiring to work with data scientists.
The Key Responsibilities
I want to outline some of the key responsibilities of a product manager working on a data science product. This may not be a comprehensive list. I am listing the things I have observed so far. I may continue to update this list.
1. Define the purpose of the data science project
The product managers needs to define the capability your company will have once the data science project is complete. I can give you two examples from the work of my teams at Castlight Health. a) We wanted to identify segments of people who are similar and predict their healthcare needs for a certain period in the future. b) We wanted to look at two doctors with the same name in a directory of doctors and determine if the doctors are the same person or not. This is important to create a reliable directory of doctors for our users. Keep the capability description to one page. There is no need to write a long requirements document.
2. Break down the milestones and deliverables for the data scientists
Because any data science project is a research project, it is harder to breakdown work that falls into the cadence of a scrum team. This is an area where a product manager can play a useful role. The product manager need not tell the data scientist what to do. Most product managers will not have the skill to do so. How ever a product manager can specify what needs to be accomplished. To do this a product manager needs to understand how data scientist works to solve a problem. The course on Data Science for executives from Johns Hopkins can help you with that. Don't just browse through the course. Get the certification.
3. Temper the expectations of your colleagues and business leaders
A product manager needs to explain a data science project and the expected deliverables in simple language to executives, who may or may not be educated about how data scientists work. It is important to explain that the results of a data science project are not always predictable. After a month of work the data science team may come to the conclusion that a particular model does not work as expected and may have to go down a different path. It is the product managers responsibility to set reasonable expectations and communicate results.
4. Product Managers can perform tasks such as creating test data sets
Data product managers can even perform tasks in a data science project. For example, they can create a test data set to evaluate the efficacy of a machine learning model. Product managers do not need engineering backgrounds or have knowledge of programming to do so. I created a test data set recently for our doctor directory matching project using the tools, my data science colleagues created. They are command line tools. So some familiarity with command line tools and some curiosity about data are pre-requisites. Participating in such tasks will help product managers understand the data and the business problem intimately. It will also help you build credibility with the data scientists.
I plan to write more about the role of product managers in a data or data intelligence product. If you have played the role of a product manager for a product involving data scientists, please share your thoughts. You may have noticed that I use the terms data products and data intelligence products. I believe that data product are different from data intelligence products. More about that later.