Increasing adoption of data products, by design

Look no further than Brian T. O’Neill’s bio to tell you that’s what he does best.

Our special guest this week knows that low adoption of data products are enterprises biggest enemy. Not just in terms of quantity, but also quality of these investments. Why are teams so often creating technically right, effectively wrong data products? Why do people fail to adopt when it’s them post crucial part of becoming data driven organizations?

These burning questions have answers.

Join hosts Juan, Tim, and guest Brian T. O’Neill on this weeks episode of Catalog & Cocktails.

Key takeaways:

• [00:06 - 03:30] Intro & Cheers
• [03:33 - 04:54] What is your instrument of choice and what would your band name be?
• [04:59 - 07:55] Honest no BS definition of a data product
• [07:59 - 11:17] Alternative definitions of data as products
• [11:18 - 14:11] Data products can be many things, and definitions are broad
• [14:11 - 19:39] How human centered design interplays with defining data products
• [19:47 - 25:17] Data therapists, knowledge scientists and engineers
• [25:21 - 33:48] Where does the burden lay, and with whom
• [33:49 - 38:35] Brian's perspective on data product management versus software product management
• [38:38 - 43:59] How do you achieve good adoption?
• [44:05 - 46:39] What is the best way people can start learning about human-centered design?
• [46:42 - 47:20] Can dashboards be data products, and can machine learning be data products?
• [47:32 - 49:45] Should companies be investing in data managers
• [49:45 - 52:45] Value and adoption, should companies track data ROI
• [52:58 - 58:20] Takeaways
• [58:23 - 01:02:09] Three questions