The Multiplier Effect
Engine Room Article 6: How Reference Data Transforms Proprietary Data
The Data Advantage Assumption
Many organizations assume their proprietary data is their AI advantage. “We have twenty years of customer data.” “Our operational data is unique.” This assumption is understandable - and often incomplete.
Proprietary data frequently has challenges: inconsistent formats, implicit assumptions that made sense to creators but aren’t documented, gaps that weren’t problems for original use cases but matter for AI applications.
Proprietary data is often an advantage in potential. Reference data is what converts that potential into something usable.
What Reference Data Provides
Taxonomies and ontologies provide standard categorization schemes that enable comparison across different data sources.
Entity registries disambiguate references - connecting ‘IBM’ and ‘International Business Machines’ to the same entity.
Relationship schemas define standard ways of expressing connections between entities.
The Multiplier Mechanism
The pattern: proprietary data provides isolated facts. Reference data provides relationships. Relationships enable computation. Computation creates insight.
Building a knowledge graph from public domain data taught me something counterintuitive: the most valuable work wasn’t creating proprietary data - it was connecting that data to reference standards that made it computable.
Proprietary data provides facts. Reference data provides relationships. Relationships enable computation that creates insight.
Reference data multiplies the value of proprietary data by making it connectable and computable. The mapping work is where value gets created.
Related: 07-source—engine-room-series