As AI moves into operational decision‑making, traditional, document‑led data management breaks down. Policies don’t execute. Controls don’t scale. Assurance arrives too late. What used to be inefficient is now operational and regulatory risk.
This paper explains the model shift required to govern data and AI at scale – Automated Data Management (ADM).
What’s in the paper?
A clear, executive‑level view of Automated Data Management — not as tooling, but as a way of working.
You’ll learn:
- Why manual data management has become a bottleneck for AI
- What ADM actually means in practice
- How intent becomes executable, enforceable controls
- How organisations move from retrospective assurance to continuous proof
The path to ADM
We outlined a practical, phased approach to Automated Data Management without breaking your existing architecture:
Design Observability — making governance intent machine‑readable
Design Execution — turning intent into automated controls
Data Observability — continuously proving reality matches design
Mark McQueen
Managing Partner & CEO, Head of North America, Ortecha
Matt McQueen
Senior Consultant, Ortecha
