I was part of a panel on the Impact of GenAI on Modern Data Platforms recently, hosted by the Data Engineering Melbourne meetup. It was great to chat with MC Ryan Collingwood and fellow panellists Rahul Trikha, Peter Barnes and Tony Nicol in front of a large and curious crowd.
Like the crowd, I felt the topic was super relevant to the work I and my team are doing right now. Ryan posed questions on:
- “Wow!” and “Whoops!” in first-hand experience
- Impact on leadership and communication
- Managing hype vs reality as data professionals
- True cost & hidden complexities
- Hot takes and hot tips
In prior thinking about this topic, I boiled our current approach down to “trust vs convenience” in the data & AI supply chain, considering impact on:
- enablement perspective on specialisation in a large technology business
- basic supply chain activities – source, transform, serve, govern
- data mesh perspectives – domain ownership, product mindset, platform definition, shared and automated governance
- waste perspectives – how to reduce rather than exacerbate waste
- unstructured data management and governance, including LLMs
- interdependence of operational and analytic planes in an AI world
- lifecycle of low-code solutions
- modular and extensible design in a rapidly evolving vendor ecosystem
I’m hoping to write up some of these thoughts soon. Thanks to Shivendra Vishal for organising the panel and sharing some photos!
And my delicious hot take was of course GenAI Stone Soup.