Data Governance

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

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

Establish data sourcing standards, schedules, SLAs, and engagement protocols with external data sources/providers, including policies for data onboarding, transformation, processing

Data Assets & Lineage

Inventory data assets (databases, documents, etc.) to understand and affect how data propagates through business processes & systems

Business Concept Data Governance

Agree on standards and unique identification methods for core (non- derived) concepts in the business, including controlled vocabularies and classifications

Master & Reference Data Mgmt

Provide authoritative storage, cross-mapping, and enrichment of master data records representing the complete set of data for a shared business concept

Data Quality

Detect and remediate data that is incomplete, untimely, or does not meet standards

Analytic (Model) Mgmt.

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

Share and manage the definitions, calculations, and context on business metrics and derived data attributes