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Transcript for this episode (link) provided by Starburst. See their Data Mesh Summit recordings here and their great data mesh resource center here. You can download their Data Mesh for Dummies e-book (info gated) here.

João's LinkedIn: https://www.linkedin.com/in/joaoantoniosousa/

João's Medium: https://joao-antonio-sousa.medium.com/

Brent Dykes' LinkedIn: https://www.linkedin.com/in/brentdykes/

In this episode, Scott interviewed João Sousa, Director of Growth at Kausa.ai. To be clear, he was only representing his own views on the episode.

The "four types" will often be throughout this summary. The four types refers to the types of analytics: descriptive - what is happening; diagnostic - why is it happening; predictive - what might happen in the future; and prescriptive - what actions should we take.

Some key takeaways/thoughts from João's point of view:

1. Of the four types of analytics, diagnostic analytics is VERY underserved. The other three - descriptive, predictive, and prescriptive - are where most organizations are focusing more so there's a "diagnostic analytics gap."
2. ?Controversial?: Of the four, diagnostic analytics requires the most domain/business expertise.
3. Tips for improving your diagnostic analytics: 1) show the value of drilling down in to the why - find a few use cases and communicate the value well; 2) promote a closer collaboration between data and business people; 3) improve your definitions around data roles; 4) very clear communication of expectations and who does what; 5) don't get into firefighting mode, have a structured approach to diagnostic analytics; and 6) automate the repetitive parts.
4. There are 3 levels of diagnostic analytics immaturity: getting "stuck