Skip to main content
You went from an empty environment to governed wealth & asset-management analytics — and, more importantly, to a model that grows itself:
  • North Star set: Ana looked at your data and drafted what the ontology is for — a written north_star.md with the questions that matter — before touching the schema
  • Connected: ontology repo (live, git-backed) + warehouse (read-only) + your documents
  • Validated: the model diffed against your actual schema, fixed via PR — grain and identity resolved first
  • Classification live: asset-class / GICS / FIGI groupers joining in-sandbox, zero warehouse writes
  • Governed answers: AUM, net flows, return, allocation, concentration, fee yield — SQL shown, decisions recorded
  • Compliant by default: PII roles, small-cell suppression, MNPI / information barriers, suitability / GIPS
  • Self-maintaining: not-yet-governed questions answered on the frontier and proposed back as PRs — discovery paid once, then reused
  • Yours: definitions adapted in your repo, pinned with golden-query tests

​
Where to go from here

  • Operate it: the Ontology Operations workshop covers the production loop — git workflows, access control, accuracy testing, scaling across teams.
  • Understand the build: Build Your Ontology, End to End teaches the from-scratch method the starter packages up.
  • Roll it out: point your analysts and business users at Self-Service Analytics — the ontology you just stood up is what makes their answers trustworthy.
  • Licensing: before production, review LICENSING.md (CUSIP / per-security GICS structural-only; FIGI via OpenFIGI; source the security master from your licensed feed).
How was this workshop? One click helps us improve — optional comment after. Thanks — feedback recorded!**’})();return false;”>😕 Not usefulThanks — feedback recorded!**’})();return false;”>🙂 OKThanks — feedback recorded!**’})();return false;”>🤩 Great