Category: Organisational Design
-
Effective ML Teams on Thoughtworks Tech Podcast
I recently recorded an episode of the Thoughtworks Technology Podcast with my Effective Machine Learning Teams co-authors Ada Leung and David Tan, hosted by Scott Shaw and Ken Mugrage. The episode is number 146 – Building at the intersection of machine learning and software engineering. It was great to chat about the book and share…
-
EMLT Q&A
A fun Q&A with Thoughtworks on the drivers, key messages and writing process for Effective Machine Learning Teams (EMLT) with my fellow authors Ada and David. It’s neat to be featured alongside all the other many great books from Thoughtworks authors. Find the book, trial and purchase options at O’Reilly, and find yourself a nice…
-
Dealing with data inventory
Data held by businesses is often described as an asset. This can be misleading or even incorrect. In either case, data managed inappropriately leaves value on the table, inflates cost, reduces responsiveness, and creates risk. Some data held by businesses would better be described as inventory. It might one day be a true asset, but…
-
Effective Machine Learning Teams in print
My book Effective Machine Learning Teams is now in print! Building ML solutions requires multi-disciplinary collaboration. EMLT shows how to use design practices to identify the right products, how to apply good data science and software engineering practices to build products right, and how to structure ML teams and organisations so that they are right…
-
7 wastes of data production – when pipelines become sewers
I recently had the chance to present an updated version of my 7 wastes of data production talk at DataEngBytes Melbourne 2023. I think the talk was stronger this time around and I really appreciated all the great feedback from the audience. Thanks to Peter Hanssens and the DEB crew for having me as part…
-
Perspectives edition #27
I was thrilled to contribute to Thoughtworks Perspectives edition #27: Power squared: How human capabilities will supercharge AI’s business impact. There are a lot of great quotes from my colleagues Barton Friedland and Ossi Syd in the article, and here’s one from me: The ability to build or consume solutions isn’t necessarily going to be…
-
22 rules of generative AI
Thinking about adopting, incorporating or building generative AI products? Here are some things to think about, depending on your role or roles. I assume you’re bringing your own product idea(s) based on an understanding of an opportunity or problems for customers. These rules therefore focus on the solution space. Solutions with generative AI typically involve…
-
Data Mesh Radio
I joined Scott Hirleman for an episode (#95) of the Data Mesh Radio podcast. Scott does great work connecting and educating the data mesh community, and we had fun talking about: Fitness functions to define “what good looks like” for data mesh and guide the evolution of analytic data architecture and operating model Team topologies…
-
Data mesh: a lean perspective
Data mesh can be understood as a response to lean wastes identified in data organisations. I paired with Ned Letcher to present this perspective at the LAST Conference 2021, which was much delayed due to COVID restrictions. Lean wastes including overproduction, inventory, etc, may be concealed and made more difficult to address by centralised data…
-
The Business Case for Data Mesh
I collaborated with with some colleagues to share our experiences with data mesh and how to frame the benefits for an executive audience, written up in an article titled The Business Case for Data Mesh.