Agile Data N’ Info

In this episode, Shane Gibson sits down with anchor modeling co-creator Lars Rönnbäck and data modeling practitioner Christian Kaul. The conversation explores the foundational principles of anchor modeling, an agile, sixth normal form (6NF) ensemble modeling technique developed in 2004 to achieve data warehouse longevity and cope with rapid business changes without degrading into unmaintainable “just a bunch of tables”. The group examines the architecture’s core constructs—anchors (representing immutable entity identities), attributes (isolated in separate tables per field), ties (relationships with flexible arity), and knots (shared finite value sets)—as well as the newly introduced nexus, which translates activity schema concepts into 6NF. They contrast these constructs with other ensemble methods such as Data Vault and Focal, emphasizing how strict separation of immutable surrogate identities from natural business keys provides a consistent, opinionated framework suited for automated schema generation. The discussion also dives deep into anchor modeling’s advanced temporal capabilities, contrasting standard unitemporal progression with bitemporal architectures that use annex tables to track orthogonal historical corrections, as well as complex concurrent reliance temporal models that record conflicting source statements alongside confidence ratings. Lars shares practical deployment insights, including Nikolay Golov’s 500-terabyte implementation for an online marketplace in Russia, and the panel reflects on why ensemble techniques have gained stronger traction in European and Australasian Inmon-aligned environments compared to US Kimball patterns. Looking forward, the panel explores the concept of the model-driven organisation, evaluating how pre-joined perspective views, hybrid operational/analytical databases, and open-source anchor modeling LLM skills enable AI agents to safely generate code and navigate multi-table schemas. Whether you are curious about sixth normal form ensemble modeling, strategies for handling bitemporal and multi-source historical corrections, or how AI agents and LLM skills can interact with highly decomposed data architectures, this episode is packed with valuable insights for data architects and modeling practitioners. If you want want to read a summary generated with GenAI, head over to: https://agiledata.substack.com/i/223482931/google-notebooklm-briefing   If you want to access the transcript for the podcast, head over to: https://agiledata.substack.com/i/223482931/transcript   Listen to more podcasts on applying Agile Data patterns over at https://podcast.agiledata.io/ Read more on the Agile Data Way of Working over at https://AgileDataGuides.com/   If you just want to talk about making magic happen with agile and data you can connect with Shane @shagility on LinkedIn.   Subscribe: Apple Podcast | Spotify | Google Podcast  | Amazon Audible | TuneIn | iHeartRadio | PlayerFM | Listen Notes | Podchaser |  Deezer | Podcast Addict |    <-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  

Raw and unedited Top 3 things: Junior data careers need breadth and hands-on practice. Tim suggested looking beyond tech companies, building small projects or businesses, and learning the fundamentals well enough to explain them. AI-polished answers make genuine understanding harder to assess. Tim described using free-form data-modeling questions to screen applicants; the discussion also covered probing answers more deeply in interviews. AI tools don’t remove the need for clear definitions and human judgment. The discussion returned to disagreements over terms such as “customer,” the limits of generated dashboards, and the risk of tying workflows too closely to one provider. https://agiledata.info/p/watch-now-recording-of-aura-tim-frazer-0ad?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

Raw and unedited Top 3 things: Helsinki Data Week’s format : a speaker-led Tuesday followed by clearly labelled partner sessions kept the event affordable while giving vendors space for substantive talks. An ontology is more than AI-generated descriptions of existing data: Juha distinguished its content, format, and storage, and argued that an organisation’s unique concepts need to be worked out with the business. AI is changing data work, but governance and usability lag: the conversation covered shadow AI, client-data boundaries, and the gap between tools useful to enthusiasts and tools that can scale across an organisation. https://agiledata.info/p/watch-now-recording-of-aura-juha-f55?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

Raw and unedited Top 3 things: Get ahead of quarterly planning. Anna talked about the data team doing discovery before proposals reach the train meetings, so they arrive prepared rather than as surprise data requests. Prototype quickly, then govern what persists. Anna sees value in scrappy, source-connected prototypes, but not in leaving them as lasting, ungoverned dashboards. Keep AI agents behind explicit gates. The discussion highlighted agents missing small but critical instructions—including a child session bypassing Shane’s commit gate—and the need for tests, human checks, and coordination as parallel work scales.   https://agiledata.info/p/watch-now-recording-of-aura-anna-e3b?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

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.   You can read the paper John published that spurred the conversation here: https://www.linkedin.com/feed/update/urn:li:activity:7511570544782356480/   If you want want to read a summary generated with GenAI, head over to: https://agiledata.substack.com/i/219573257/google-notebooklm-briefing   If you want to download the transcript for the podcast, head over to: https://agiledata.substack.com/i/219573257/transcript   Listen to more podcasts on applying Agile Data patterns over at https://podcast.agiledata.io/ Read more on the Agile Data Way of Working over at https://AgileDataGuides.com/   If you just want to talk about making magic happen with agile and data you can connect with Shane @shagility on LinkedIn.   Subscribe: Apple Podcast | Spotify | Google Podcast  | Amazon Audible | TuneIn | iHeartRadio | PlayerFM | Listen Notes | Podchaser |  Deezer | Podcast Addict |    <-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