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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 Dec 01, 2020
Tuesday Dec 01, 2020
In this episode of Experiencing Data, I speak with Jesse Anderson, who is Managing Director of the Big Data Institute and author of a new book
titled, Data Teams: A Unified Management Model for Successful Data-Focused Teams. Jesse opens up about why teams often run into trouble in their efforts to build data products, and what can be done to drive better outcomes.
In our chat, we covered:
- Jesse’s concept of debugging teams
- How Jesse defines a data product, how he distinguishes them from software products
- What users care about in useful data products
- Why your tech leads need to be involved with frontline customers, users, and business leaders
- Brian’s take on Jesse’s definition of a “data team” and the roles involved-especially around two particular disciplines
- The role that product owners tend to play in highly productive teams
- What conditions lead teams to building the wrong product
- How data teams are challenged to bring together parts of the company that never talk to each other – like business, analytics, and engineering teams
- The differences in how tech companies create software and data products, versus how non-digital natives often go about the process
Quotes from Today’s Episode
“I have a sneaking suspicion that leads and even individual contributors will want to read this book, but it’s more [to provide] suggestions for middle,upper management, and executive management.” – Jesse
“With data engineering, we can’t make v1 and v2 of data products. We actually have to make sure that our data products can be changed and evolve, otherwise we will be constantly shooting ourselves in the foot. And this is where the experience or the difference between a data engineer and software engineer comes into place.” – Jesse
“I think there’s high value in lots of interfacing between the tech leads and whoever the frontline customers are…” – Brian
“In my opinion-and this is what I talked about in some of the chapters-the business should be directly interacting with the data teams.” – Jesse
“[The reason] I advocate so strongly for having skilled product management in [a product design] group is because they need to be shielding teams that are doing implementation from the thrashing that may be going on upstairs.” – Brian
“One of the most difficult things of data teams is actually bringing together parts of the company that never talk to each other.” – Jesse
Links
- Big Data Institute
- Data Teams: A Unified Management Model for Successful Data-Focused Teams
- Follow Jesse on Twitter
- Connect with Jesse on LinkedIn
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