We have developed a framework that addresses the data disciplines that collectively address “Data Governance” in large organizations. The assessment phase determines which are relevant to a specific client’s desired future state, and those become the content of a client-bespoken project.
Privacy, Access, & Data Usage
PURPOSE
Establish policies and enact compliance with regulatory and
contractual commitments for data privacy, data access and sharing, and data
usage (e.g. commercial data usage)
Data Sourcing & Curation
PURPOSE
Establish data sourcing standards, schedules, SLAs, and engagement
protocols with external data sources/providers, including policies for data
onboarding, transformation, processing
Data Assets & Lineage
PURPOSE
Inventory data assets (databases, documents, etc.) to understand and
affect how data propagates through business processes & systems
Business Concept Data Governance
PURPOSE
Agree on standards and unique identification methods for core (non-
derived) concepts in the business, including controlled vocabularies and
classifications
Master & Reference Data Mgmt
PURPOSE
Provide authoritative storage, cross-mapping, and enrichment of
master data records representing the complete set of data for a shared
business concept
Data Quality
PURPOSE
Detect and remediate data that is incomplete, untimely, or does not
meet standards
Analytic (Model) Mgmt.
PURPOSE
Establish standards and controls for analytical models that describe
and predict the business; enable syndication of analytical models to other
areas of the business. This includes metadata on both ML and AI models
Metrics and Attribute Governance
PURPOSE
Share and manage the definitions, calculations, and context on
business metrics and derived data attributes