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Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.

Darren's LinkedIn: https://www.linkedin.com/in/darrenjwoodagileheadofproduct/

Darren's Big Data LDN Presentation: https://youtu.be/vUjoJrl_MEs?si=WzB0sBStVIAyqDJs

In this episode, Scott interviewed Darren Wood, Head of Data Product Strategy at UK media and broadcast company ITV. To be clear, he was only representing his own views on the episode.

Scott note: I use "coalition of the willing" to refer to those willing to participate early in your data mesh implementation. I wasn't aware of the historical context here, especially when it came to being used in war, e.g. the Iraq war of the early 2000s. I apologize for using a phrase like this.

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

1. Overall, when thinking about moving to product thinking in data, it's as much about behavior change as action. You have to understand how humans react to change and support that. You can't expect change to happen overnight - patience, persistence, and empathy are all crucial aspects. Transformation takes time and teamwork.
2. ?Controversial?: In data mesh, it's crucial to think about flexibility and adaptability of your approach. Things will change, your understanding of how you deliver value will change. Your key targets will change. Be prepared or you will miss the main point of product thinking in data.
3. When choosing your initial domains and use cases in data mesh, think about big picture benefits. You aren't looking for exact value measurements for return on investment but you also want to target a tangible impact, e.g. if we do X, we think we can increase Y part of the business revenue Z%.
4. Zhamak defines a data product quite well in her book on data mesh. But data as a product is a much broader definition of bringing product management best practices to data. That's harder to define but quite important to get right.
5. When thinking about product discovery - what do data consumers actually need...