Data Governance
A strong Data Governance program builds trust in data, enabling informed decision-making and efficient operations through clear management processes and roles.
We work with client teams to develop scalable data governance practices, enforcing standards across the data lifecycle (collection, maintenance, sharing). We create tailored frameworks that align with the client’s goals, ensuring privacy and compliance while making data valuable and usable for stakeholders.
A Data Governance Operating Model makes data useful and valuable for its consumers. To build a model, we assess which disciplines within Data Governance are relevant to the client’s desired future-state, and those become the content of a client-bespoke 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
Data Quality
PURPOSE
Detect and remediate data that is incomplete, untimely, or does not
meet standards
Master & Reference Data Management
PURPOSE
Detect and remediate data that is incomplete, untimely, or does not
meet standards
Business Concept Governance
PURPOSE
Agree on standards and unique identification methods for core (non-
derived) concepts in the business, including controlled vocabularies and
classifications
Metrics and Attribute governance
PURPOSE
Share and manage the definitions, calculations, and context on
business metrics and derived data attributes
Analytic (Model) Management
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