https://drive.google.com/file/d/1WTOONdaVL3v9J55_MDYh1l971P2jgAwg/view?usp=share_link

Knowledge graph teams routinely demonstrate strong bottom-up success: rich semantic models, reasoning, and compelling use cases. Yet many of these “hero graphs” stall when organizations attempt to scale them enterprise-wide. At the same time, Chief Data Officers invest heavily in data catalogs, governance, and operating models that often fail to engage practitioners or reflect how knowledge graphs are built.

This talk addresses a structural gap experienced practitioners recognize but rarely articulate clearly: knowledge graph initiatives and data management programs tend to operate as parallel worlds, driven by different incentives, timelines, and definitions of success. One proves value locally and quickly; the other optimizes for enterprise consistency, accountability, and reuse.

The key insight is that this is not an either/or problem. Knowledge graphs scale sustainably when bottom-up delivery is anchored in shared standards and enterprise intent—so that each use case strengthens, rather than fragments, the broader data management ecosystem.

We present a pragmatic reference model showing how graph practitioners can design use cases that feed enterprise visibility, governance, and stewardship automatically, while data management principles can accelerate semantic innovation. Attendees will leave with a clear mental model and practical language for engaging CDO-led organizations and turning individual graph wins into durable enterprise capability.