Listen now | Recording of AURA - Chris Gambill & Shane Gibson on 9th October 2026
Agile Data N’ InfoShow Notes
Raw and unedited Top 3 things:
How to evaluate new tools:Chris described a three-part approach: check whether a tool solves a real pain point, whether it has a community, and what a time-boxed trial reveals. He used Databricks serverless micro apps as an example of something worth revisiting later, rather than adopting now. How to reduce vendor and migration risk:We discussed keeping business logic and context outside execution platforms, retaining access to data, and testing a fallback plan—not just assuming a migration will work.
AI-generated dashboards and their consequences:Faster creation could multiply duplicate reports. We discussed finding existing reports before making new ones, as well as the adoption, governance, and security challenges of switching BI tools.
https://agiledata.info/p/watch-now-recording-of-aura-chris-0f0?utm_source=youtube
<-OO-> Information Product Canvas Workshop
“That’s not what I wanted.”“But that’s what you asked for.”
Shane runs a one-day workshop on the Information Product Canvas, a repeatable way to gather data requirements in 30 minutes. The approach he actually uses with his stakeholders, not a textbook version that falls apart on contact with messy data and vague stakeholder requirements. Latest dates: Gather data requirements in 30 minutes using a shared language.
Simply Magical Data Ways of Working
More from Agile Data N’ Info
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
Raw and unedited Top 3 things: