Learn  how we turned scattered and disjointed reference data into a governed golden  source: one centralized tool, clear ownership, standard workflows, and  reusable patterns that improve data quality, integration, and trust.  Organizations rely on reference data—codes, hierarchies, and  classifications—to keep reporting, operations, and analytics in sync. Yet  this data is often scattered across systems, managed manually, and owned  inconsistently creating integration headaches and trust gaps. This session shares  how we are building an enterprise reference data management capability  centered on a centralized data governance tool as a governed "golden  source" and a reusable framework rather than a one-off project. We will  walk through the journey from fragmented local lists to a centralized  environment that models golden entities, steward's changes through workflows,  and delivers certified versions to consumers such as reporting applications,  data warehouses, and finance platforms. Along the way, we'll cover the  governance structures (committees, framework, ownership), integration  patterns (APIs vs. outbound staging), and change management practices that  make the capability sustainable. Attendees will leave with a practical view  of what it takes to stand up reference data as an enterprise capability: how  to prioritize domains, align producers and consumers, balance enterprise  standards with local needs, and measure success through improved  interoperability, reduced manual effort, and gain higher confidence in key  reports.