The Multiplier Effect
Proprietary data isn't the AI advantage most organizations think it is. What that data connects to is.
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
Proprietary data provides facts. Reference data provides relationships. Relationships enable computation that creates insight.
The Takeaway: Reference data multiplies the value of proprietary data by making it connectable and computable. The mapping work is where value gets created.