Listen Now | Recording of AURA - Chris Gambill & Shane Gibson on 11th September 2026
Agile Data N’ InfoShow Notes
Raw and unedited Top 3 things:
How to migrate legacy data platforms safelyaudit first, choose greenfield vs. lift-and-shift early, define the target architecture, and validate with a thin-slice POC rather than a big-bang migration.
Using AI in migration work, with human oversightLLMs can help translate code, create plans, surface outliers, and review alternatives, but edge cases, over-engineering, and validation mean people remain essential.
Preserving business context and governanceretain raw migration conversations, document why decisions were made, separate logic/context from the execution platform, and establish governance, cost monitoring, and FinOps from day one.
https://agiledata.info/p/watch-now-recording-of-aura-chris-b41?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
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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