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If you’re a leader tasked with generating business and org. value through ML/AI and analytics, you’ve probably struggled with low user adoption. Making the tech gets easier, but getting users to use, and buyers to buy, remains difficult—but you’ve heard a ”data product” approach can help. Can it? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting designer’s perspective on why creating ML and analytics outputs isn’t enough to create business and UX outcomes. How can UX design and product management help you create innovative ML/AI and analytical data products? What exactly are data products—and how can data product management help you increase user adoption of ML/analytics—so that stakeholders can finally see the business value of your data? Every 2 weeks, I answer these questions via solo episodes and interviews with innovative chief data officers, data product management leaders, and top UX professionals. Hashtag: #ExperiencingData. PODCAST HOMEPAGE: Get 1-page summaries, text transcripts, and join my Insights mailing list: https://designingforanalytics.com/ed ABOUT THE HOST, BRIAN T. O’NEILL: https://designingforanalytics.com/bio/
Episodes
Tuesday Jul 14, 2020
Tuesday Jul 14, 2020
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
Quotes from Today’s Episode
“You can become a product manager by building products. You don't need to be a software engineer. You don’t need to have an MBA. You don't need to be an incredible, inspiring visionary. This is stuff that you can learn, and the best way to learn it is by doing it.” - Carlos
“A product manager is a generalist. And in order to become a generalist, usually you have to have some sort of [specialty] before. So, we define product management as the intersection in between business, engineering, and design. And you can become a good product manager from either of those options.” - Carlos
“If you have [a power trio of technology, product, and design] and the energy is right, and the relationships are really strong, boy, you can get a lot of stuff done, and you can iterate quickly, and really produce some great stuff.” - Brian
“I think part of the product management mindset... is to realize part of your job now is to be a problem finder, it’s to help set the strategy, it's to help ensure that a model is not the solution.” - Brian
“I think about a bicycle wheel with the hub in the center and the spokes coming out. Product management is that hub, and it reports up into the business, but you have all these different spokes, QA, and software engineering, maybe data science and analytics, product design, and user experience design. These are all kind of spokes.” - Brian
“These are people who are constantly learning, but not just about their products. They’re constantly learning in general. Reading books, practicing sports, doing whatever it is, but always looking at what's new and wanting to play around with it, just to be dangerous enough. So, I think those three areas: obsession with a customer based on data; obsession with empathy; and then obsession with learning, or just being curious are really critical.” - Carlos
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