An Experiment - Top Data Trends for 2025 with Coalesce and Google NotebookLM - Episode #55
Agile Data N’ InfoShow Notes
Join two LLM generated guests as they discuss the Top Data Trends of 2025 Whitepaper published by Coalesce. This is a different episode. Instead of a human guest, we have two robot guests. I decided to try and experiment. My experiment was, can I upload a white paper to LLM, have it generate a podcast listen to that podcast in my daily walk and see whether that summary removes the need for me to actually read the white paper. So in this case, I have grabbed a white paper called Top Data Trends for 2025 from Coalesce, uploaded it to the Google Notebook LLM and got it to generate a podcast with two hosts chatting about the white paper. Have a listen, let me know what you think. I'm really keen to understand, do you think this approach is useful, or is it a load of bollocks? You can download the whitepaper from the Coalesce website here:https://coalesce.io/reports/the-top-data-trends-for-2025/If you want to read the transcript for the podcast head over to:https://agiledata.io/podcast/agiledata-podcast/an-experiment-top-data-trends-for-2025-with-coalesce-and-google-notebooklm/
Listen to more podcasts on applying AgileData patterns over at https://agiledata.io/podcasts/ Read more on the AgileData Way of Working over at https://wow.agiledata.io/way-of-working/
If you want to join us on the next podcast, get in touch over at https://agiledata.io/podcasts/#contact
Or if you just want to talk about making magic happen with agile and data you can connect with Shane @shagility or Nigel @nigelvining on LinkedIn.
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In this episode Shane Gibson sits down with data modeling veteran’s John Giles and Remco Broekmans The conversation explores the premise of democratising data modeling through AI, centered around a paper published by John regarding where AI can and cannot add real value. The group highlights the critical importance of conceptual and context data modeling, stressing that business terms and definitions must be established upfront before looking at physical source tables. They strongly critique the anti-pattern of placing humans “at the end of the line” merely to rubber-stamp automated outputs, arguing instead for an “AI-in-the-loop” approach where technology serves strictly as an assistant under human direction. The discussion also dives deep into complex real-world scenarios where automated tools fail—specifically when physical data or source systems do not yet exist. John shares extreme edge cases, such as establishing a brand-new government department from scratch based on a vague Act of Parliament, unifying over 70 distinct healthcare organisations into a single model, and modeling evolving visions like the Australian wool industry. Remco breaks down his multi-agent ELM framework, explaining how introducing deliberate manual friction points between LLM tasks keeps human modelers in control and why model-storming workshops, human trust, and organisational culture are far more vital than push-button automation. Whether you are curious about conceptual modeling techniques, how to effectively integrate “AI in the loop” while avoiding push-button modeling pitfalls, or strategies for aligning business definitions across complex organisations, this episode is packed with valuable insights for data professionals.
Raw and unedited Top 3 things:
Raw and unedited Top 3 things:
Raw and unedited Top 3 things:
Raw and unedited