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May 2, 2019

Product Managers Needed for Analysis?

Summary:

Some data science teams have matured beyond offering advice and are making products. Do they need a data science product manager?

Photo Courtesy of Pixabay

I mentioned, in a debrief from the Data Leaders Summit, the rise of the product manager role within data science teams.

This surprised me. I’ve become used to hearing about the need for more data engineers or analysts to complement data scientists. But the focus on product managers and product development life-cycles was a new one.

This was not an isolated incident from only a speaker or two. Many leaders confirmed that they had product manager roles. What is going on?

In this post, I will share a combination of my initial thoughts and resources I have discovered. I hope to help  you decide whether product managers are needed in your team.

What Is a Data Science Product Manager?

Let’s define what is meant by this new job title.

Some data science teams have matured beyond offering advice. Their output was no longer decision support analysis, providing models or insight to influence leaders. Increasingly, these teams were making products. These could be deployable models (for decisions, optimization, categorization) or even entire automated processes. Developing and deploying these into live business operation requires some additional skills. It is to meet that need that product manager roles have evolved.

Taken from the historical role of product managers in operational or marketing teams, these roles own a life cycle, from initial innovation (e.g. insight generation sessions) through design and development into deployment.

See also: 5 Key Effects From AI and Data Science

This article, from the IoT for All blog, helps bring the role to life. It is not prescriptive (as frankly the role is still evolving) but highlights some of the key skills needed.

The references to facilitation and communication skills reminded me of the need for softer skills. Those matter across so many data science or analytics roles. But the product manager skillset also reminded me of how I used to define analytics business partners. One key difference is the judgment and knowledge needed to manage a production line and pilots.

How Do You Develop Data Science Products?

How do product managers and others develop data science products?

A number of skills are needed, and the most appropriate development methodology will vary by business. But product managers sound some common themes.

I’ve heard speakers draw on influences from analytics, systems thinking, agile working and design thinking and stress the role of product development workflow.

In this article from Harvard Business ReviewEmily Glassberg Sands shares a high-level view of how to build great data products.

Is This an Opportunity for Other Product Managers?

Given the emphasis on product management skills, does this role represent an opportunity for product managers working outside any data or analytics field.

My experience with crossovers is mixed, but the data science product manager may be a different case. The mastery of product development and management skills appears to be key.

See also: The Entrepreneur as Leader and Manager  

This interesting blog post from Cohort Plus reads as if aimed at product managers in the technology space but is still a useful introduction for others in such a role.

If you are a product manager and interested in making the move into a data science team, this introduction should help (apologies, but posts from Medium will not display snippets).

Do You Need a Data Science Product Manager?

It would be great to have comments or feedback from both those who see the value of a product manager in these teams and those who think it’s a fad.

I’m sure more roles will evolve as these teams mature. Customer Insight Leader blog will keep a weather eye on ones that matter.

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About the Author

Paul Laughlin is the founder of Laughlin Consultancy, which helps companies generate sustainable value from their customer insight. This includes growing their bottom line, improving customer retention and demonstrating to regulators that they treat customers fairly.

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