Twenty years across data, analytics, and the decisions they serve.

The arc

Ian has built business-intelligence functions from zero twice — inside a private-equity real-estate firm and a university facilities organization — and modernized analytics inside a $350M tax-software operator, where he architected the governed knowledge platform behind the data organization's AI program. Semantic models, executive scorecards, data catalogs, cross-system migrations: the unglamorous architecture that makes numbers trustworthy.

The path ran through eight years of selling — pharma, telecom, automotive — then more than a decade building the analytics that decisions run on. That order is why the work starts from how decisions actually get made, not from the toolchain. A mathematics degree and published astronomy research — a systematic-error finding in previously published double-star measurements (Journal of Double Star Observations, Vol. 13, No. 3) that drew a collaboration invitation from a national observatory — shaped the discipline: patterns first, verification always.

Since 2023 the work has centered on AI-native analytics: retrieval systems grounded in governed definitions, evaluation harnesses that score whether an AI's answers are actually correct, and human-in-the-loop workflow design. That doctrine runs in the open — Ian is the author and maintainer of Iris, an open-source MCP-native evaluation server for AI agents, listed in the official MCP Registry. The rule every build follows: if we can't measure that it answers correctly, we don't call it done.