#85 The Move from Legacy to Leader in Data and Analytics - Interview w/ Immanuel Schweizer
#85 The Move from Legacy to Leader in Data and Analytics - Interview w/ Immanuel Schweizer

#85 The Move from Legacy to Leader in Data and Analytics - Interview w/ Immanuel Schweizer

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68 min
Success & Inspiration
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https://www.patreon.com/datameshradio (Data Mesh Radio Patreon) - get access to interviews well before they are released Episode list and links to all available episode transcripts (most interviews from #32 on) https://docs.google.com/spreadsheets/d/1ZmCIinVgIm0xjIVFpL9jMtCiOlBQ7LbvLmtmb0FKcQc/edit?usp=sharing (here) Provided as a free resource by DataStax https://www.datastax.com/products/datastax-astra?utm_source=DataMeshRadio (AstraDB) Transcript for this episode (https://docs.google.com/document/d/1WRNYqsgfM-S4jt4ExOxnW8kuhvcaX1sh2CIGJFzvlHE/edit?usp=sharing (link)) provided by Starburst. See their Data Mesh Summit recordings https://www.starburst.io/learn/events-webinars/datanova-on-demand/?datameshradio (here) and their great data mesh resource center https://www.starburst.io/info/distributed-data-mesh-resource-center/?datameshradio (here) In this episode, Scott interviewed Immanuel Schweizer, the Data Officer for EMD Electronics. Some interesting thoughts and questions from the conversation: Good governance starts at data collection - what are ethical and compliant ways to collect data from the beginning? This points to intentionality around data use stretching into the application - what should you collect that might not be part of the day-to-day application function but that might might lead to generating insights that will be used to generate a better user experience? And what are the ethical concerns? Should we initially create data products to serve specific use cases or should we focus on sharing data first and then shaping what people consume most into data products? EMD is approaching data products from a different angle than most, using the second approach. When looking at data mesh, should you start with the high data maturity teams or work to pull everyone up to at least a decent baseline maturity level? If you work with the most mature teams, will their challenges really be applicable to the not-so-mature domains? Can you find good reuse patterns to scale your mesh implementation? Doma

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#85 The Move from Legacy to Leader in Data and Analytics - Interview w/ Immanuel Schweizer - Listen Free | WowFM