Two PDF playbooks, one on standing up data governance and one on governing AI, plus the maturity assessment framework as a document. Free, no email.
A playbook is not a methodology. A methodology tells you what the ideal end state looks like. A playbook tells you what to do on Monday, when you have no budget, no mandate, and one sympathetic director who might return your call.
Both of these are written for that Monday.
From Theory to Action: A practical guide to transforming information chaos into strategic value using the DAMA framework. Includes the Golden Square (People, Processes, Technology & Data) and financial impact strategies.
From Risk to Trust: A strategy for ethical, transparent, and governed AI. Includes the 5W2H Canvas for AI projects.
A practical diagnostic template to evaluate your organization's current data maturity level across People, Process, and Technology.
Automatic results
Prefer to assess online, with nothing to score by hand?
Fill in the Maturity Scorecard and get your DAMA level, your radar chart and an executive report automatically on completion.
Fill in the Scorecard onlineFree · a few minutes · instant result
This is the sequence I use to get a program from nothing to one functioning domain: how to pick the domain, how to find the critical data elements inside it, how to get ownership accepted rather than assigned — which is the whole game — and what the first council meeting needs on the agenda so people leave having actually decided something.
It's deliberately narrow. It doesn't try to cover eleven knowledge areas, because a first-year program that tries to cover eleven knowledge areas produces a framework document and no change in behavior. One domain, done visibly, is what buys you the second one. Nobody has ever been given a second domain on the strength of a roadmap.
Most AI governance conversations start at the model and work backward, which is exactly why they stall. The interesting questions turn out to be data questions almost every time: what was this trained on, who was allowed to approve that, what happens when somebody asks for their record to be deleted, and whether you can say — in writing, to a regulator, without a week of archaeology — where a given output came from.
So this playbook works forward from the data instead: classification before ingestion, lineage as a precondition rather than a nice-to-have, and the specific decision rights an AI governance body needs that a data governance council doesn't already have. It assumes you have some governance in place. If you don't, read the other one first — starting here would be building the roof.
The framework behind the scorecard, as a document you can adapt. Useful if you'd rather run the assessment yourself, in your own wording, than send your team to a form on somebody else's website. I'd probably do the same.
Both playbooks work as reading for a governance council, and both work better when the reading is followed by an argument. If you want that argument staged rather than hoped for, the scenario simulators put a room inside the trade-offs, and the workshop format runs them on your own systems with a facilitator report afterward.
