#147 Mapping Out Your Data Product Suite - Building Your Roadmap to Maximizing Business Value - Interview w/ Gunjan Aggarwal

Sign up for Data Mesh Understanding's free roundtable and introduction programs here: https://landing.datameshunderstanding.com/Please Rate and Review us on your podcast app of choice!If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see hereEpisode list and links to all available episode transcripts here.Provided as a free resource by Data Mesh Understanding / Scott Hirleman. Get in touch with Scott on LinkedIn if you want to chat data mesh.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.Gunjan's LinkedIn: https://www.linkedin.com/in/gunjanaggarwal/Gunjan's Medium: https://gunjan-aggarwal.medium.com/In this episode, Scott interviewed Gunjan Aggarwal, Head, Digital Data Products and MarTech Strategy at Novartis. To be clear, she was only representing her own views on the episode.Some key takeaways/thoughts from Gunjan's point of view:Set your overall data product strategy - for when you are in stage 2, going wider with data mesh - earlier in your journey than many may think. It's easy to focus only on use cases instead of the bigger picture.Make sure to align early on who owns what - what are the clear boundaries between roles. Otherwise, with the amount of change data mesh drives, there will likely be unnecessary chaos. Get specific.Don't fall to the 'Data Field of Dreams' - "if you build it, they will come." Focus on building to actual problem statements. Involve people early, make them accountable, give them skin in the game and they will care."The more you ask why, the more clarity you will get." Really dig in deep into the reasoning for creating new data products or ARDs (analytics ready datasets). If we have this data product, what will it unlock for us?It's crucial to avoid the trap of building data products specifically to use cases. You must have the bigger picture in mind and focus on reusability instead of only solving one set of challenges. Can you extend an existing data product?Data people should have domain knowledge where possible. That way, they can push back on requirements that don't make economic sense, that don't...