The obvious answer would be part II, which I intend to consist mainly of case studies and smaller real-world examples of Lean-Agile change in action. Before that though, I’ve been experimenting with some machine learning tools, in particular in the area of dimension reduction. Broadly, this allows the 43-dimensional data of the Agendashift survey results to be boiled down to just a few key features (factors, traits, tendencies, etc).
This has considerable potential:
- I am already able to demonstrate the automatic identification of survey scores that don’t fit the model – areas in which the organisation in question has unusually strong areas to celebrate or weak areas for further investigation – all driven by the data, not the biases of the person facilitating the debrief
- Cluster analysis could allow representatives of organisations exhibiting similar traits to be introduced and their experiences compared
- A combination of 1 & 2 could lead to some kind of recommendation system – machine-assistance for the coach, perhaps
Expect an announcement very soon on point 1, a new commercial offering and new functionality available soon to paid users (ie Agendashift partners).
Point 2 is in fact my plan for part II. Technicalities aside: bring people together, get them talking, write it up 🙂
Point 3 is for the longer term. A “living” version of the book, if you like.
Technical footnote: The machine learning (ML) tools available in Python are truly awesome. Book recommendation: Python Machine Learning by Sebastian Raschka. I’ve made good use of his online tutorials too.
Questions? Go to #next-steps in our Slack.