I am a firm believer that one of the reasons that data science and analytics has a high failure rate is a lack of product management and design. To me, product is about a mindset just as much as a job title, and I am repeatedly hearing how more and more voices in the data community are agreeing with me on this (Gartner CDO v4, International Inst. for Analytics, several O’Reilly authors, Karim Lakhani’s new book on AI, and others). This is even more true as more companies begin to leverage AI. So many of these companies fear what startups and software companies are doing, yet they do not copy the way tech companies build software applications and enable specific user experiences that unlock the desired business value.

Integral to building software is the product management function—and when these applications and tools have humans in the loop, the product/UX design function is equally as important to ensure adoption, usability, engagement, and alignment with the business objectives.

In modern tech companies, the overlap between product design and product management can be significant, and frequently, product leaders in tech companies come up through both design and engineering ranks and indeed my own work heavily overlaps with product. What this tells me is that product is a mindset, and it’s a role many can learn if they believe it’s critical.

So why aren’t more data science and analytics leaders forming strong product design and analytics functions? I don’t know, so I decided to bring Carlos onto the show to talk about his company, Product School, which offers product management training and features instructors from many of the big tech companies on how to do it. In this episode, Carlos provides a comprehensive overview of why he launched Product School, what makes an effective product manager, and the importance of having structured vision and alignment when developing products.

This conversation explores:

• Why Carlos launched the Product School for professionals who want to learn on the side without quitting their job and putting their life on hold.
• The type of mentality  product managers need to have and whether specialization matters within product management.
• Whether being a product manager in machine learning and AI is different than working with a traditional software product.
• How product management is not project management
• Advice for approaching executive decision makers about product management education
• How to avoid the trap of focusing too heavily on process
• How product management often leads to executive leadership roles
• The “power trio” of engineering, product management, and design, and the value of aligning all three groups.
• Understanding the difference between applied and academic experience
• How the relationship between design and PM has changed over the last five years
• What the gap looks like between a skilled PM and an exceptional one.

Resources and Links

The State of Product Analytics (Also referred to as The Future of Product Analytics in the audio)

Mixpanel, company that they partnered with to create the above report

Episode 17 of Experiencing Data

Twitter

Main Company Site

ProductCon

Productverse

 

Quotes from Today’s Episode

“You can become a product manager by building products. You don't need to be a software eng