Insight
Research-grounded points of view on the data governance problems that actually cost organizations money — written to be forwarded to the people who hold the budget.
Most AI initiatives don't fail because of the model. They fail because the data underneath them was never trustworthy enough to build on.
This paper makes the case that AI readiness is, in practice, a data governance problem — and lays out the foundation (ownership, quality, metadata, provenance, and responsible-AI oversight) that turns stalled AI pilots into initiatives that ship and pay off. It connects directly to the AI Readiness dimension of our Maturity Survey and the Responsible AI policy in the Starter Kit.
A CFO's guide to data-quality ROI. Poor data quality costs the average organization millions a year — it just never appears as a line item.
Written for the finance leaders who control the budget, this paper reframes data quality as an unbudgeted, recurring cost already embedded in the business — in rework, bad decisions, and failed initiatives. It includes a simple worksheet to estimate your own exposure and shows the return from bringing it under control.
Take the free Maturity Survey to see where you stand, or get the Starter Kit to start closing the gaps.
