"Data transformation" gets used to mean almost anything, which is exactly the problem he sets out to fix. In this Q&A, he lays out what the phrase should actually mean, and the four phases that separate a programme that works from one that stalls.
It starts with a thesis, not a technology: a clear statement of the value you're chasing, against which every later decision gets measured. Then comes an honest map of where your data actually lives and flows, followed by the least glamorous and most decisive step of all, agreeing what your data actually means. Skip that step and every report, product and AI agent you build on top inherits the confusion.
Will's take on where this is heading is blunt: AI won't close the gap between organisations with clean data and those without it. It will widen it, fast.
Read Will's full answer, and his three top tips, in issue #1 on InPublishing.com
Want to know where your own data stands before AI raises the stakes? Talk to 67 Bricks.