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:
“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
142 - Live Webinar Recording: My UI/UX Design Audit of a New Podcast Analytics Service w/ Chris Hill (CEO, Humblepod)
141 - How They’re Adopting a Producty Approach to Data Products at RBC with Duncan Milne
140 - Why Data Visualization Alone Doesn’t Fix UI/UX Design Problems in Analytical Data Products with T from Data Rocks NZ
139 - Monetizing SAAS Analytics and The Challenges of Designing a Successful Embedded BI Product (Promoted Episode)
138 - VC Spotlight: The Impact of AI on SAAS and Data/Developer Products in 2024 w/ Ellen Chisa of BoldStart Ventures
137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen
136 - Navigating the Politics of UX Research and Data Product Design with Caroline Zimmerman
135 - “No Time for That:” Enabling Effective Data Product UX Research in Product-Immature Organizations
134 - What Sanjeev Mohan Learned Co-Authoring “Data Products for Dummies”
133 - New Experiencing Data Interviews Coming in January 2024
132 - Leveraging Behavioral Science to Increase Data Product Adoption with Klara Lindner
131 - 15 Ways to Increase User Adoption of Data Products (Without Handcuffs, Threats and Mandates) with Brian T. O’Neill
130 - Nick Zervoudis on Data Product Management, UX Design Training and Overcoming Imposter Syndrome
129 - Why We Stopped, Deleted 18 Months of ML Work, and Shifted to a Data Product Mindset at Coolblue
128 - Data Products for Dummies and The Importance of Data Product Management with Vishal Singh of Starburst
127 - On the Road to Adopting a “Producty” Approach to Data Products at the UK’s Care Quality Commission with Jonathan Cairns-Terry
126 - Designing a Product for Making Better Data Products with Anthony Deighton
125 - Human-Centered XAI: Moving from Algorithms to Explainable ML UX with Microsoft Researcher Vera Liao
124 - The PiCAA Framework: My Method to Generate ML/AI Use Cases from a UX Perspective
123 - Learnings From the CDOIQ Symposium and How Data Product Definitions are Evolving with Brian T. O’Neill
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