EKG Principles
At agnos.ai we have adopted these ten guiding principles for building sustainable enterprise knowledge graphs.
Our Design Principles
We define an EKG as meeting these criteria. They guide our thinking and the vision of connecting data across silos within and beyond your organization.
01. Identity
Objects are identified with unique, permanent and resolvable identifiers.
02. Meaning
Meaning is directly resolvable to a machine-readable mathematical statement.
03. Distributed
Facts about any given object may be provided by multiple sources.
04. Open World
Information from various sources can vary over time. These “multiple versions of the truth” must be reconciled on access by context.
05. Self-Describing
All datasets are completely self-describing with information about lineage, provenance, source, quality and governance.
06. Measurement
The quality of fit-for-purpose data must be measured and actionable.
07. Use Cases
Information in the EKG exists with a known business justification and prioritized purpose.
08. Control
Entitlement, privacy and business policies will be automatically executed, enforced and audited at the data point level.
09. Ecosystems
All components of the data ecosystem will be subject to service level agreements.
10. Standards
The EKG platform and content should be based on open standards.