What the Road Makes Possible
Why walk this road at all? Once the foundations are in place, one team can help a business know its customers, keep them, price fairly, understand big risks and work faster.
# Leadership
Modernising a data science capability from the inside: inheriting a function with capable people and an incomplete system around them, deciding what to own and what to federate, and the belief that carried it, which is to build systems that outlive the people who built them.
8 posts
Stage 5: Trust Your People, Then Let Go
The last step is the hardest: stop being the person everything depends on. How trust moved us from waiting for things to break to stopping problems early, with a 90 day plan.
Stage 4: When People Push Back, Show Them
The technology was not the hardest part. People were. How we got through pushback and red tape, by showing value in action, and how a crisis became our best argument.
Stage 3: Give the Work a Clear Path
It used to take about a year to get a model into use. Now it fits in a two week sprint. The change came from a shared workspace and one agreed path, not from cleverer people.
Stage 2: Give Your Data One Safe Home
A model can only be as good as the data it learns from. How we brought scattered data into one checked and controlled home, and made security part of normal work.
Stage 1: Find Out What Is True
The first stage of building a data function is not technology. Write down what you have and create a rhythm. When we did, we found models that were simply wrong.
Who You Need on the Team
Who belongs on a data team, who to hire first, what to do when the budget runs out, why graduates matter, and where the team should report.
How to Build a Data Science Function From Scratch: A Roadmap
A five stage roadmap for building a data science, data engineering or AI function from scratch: what you will find, what to do first, what pushback to expect, and how to know when to move on.