Robust Data Governance
Establish comprehensive governance practices to ensure data quality, security, and compliance
Data governance is the foundation of a successful data strategy. It encompasses the policies, processes, and standards that ensure data is managed as a valuable asset across your organization.
Data Governance Zachman
Enterprise data governance framework mapping business objectives to implementation
| Why (Business Driven) | What (Business Capabilities) | How (Key Project) | Where | Who | When |
|---|---|---|---|---|---|
Contextual(Motivation) What does it matter? | |||||
| No data yet. Upload an Excel file to populate this section. | |||||
Conceptual(Business Enablement) What is the rule? | |||||
| No data yet. Upload an Excel file to populate this section. | |||||
Logical(Rules) How smart should the system be? | |||||
| No data yet. Upload an Excel file to populate this section. | |||||
Physical(Where rule is implemented) Which tools do we use? | |||||
| No data yet. Upload an Excel file to populate this section. | |||||
Assembly(Design Principles & Standards) How to ensure the quality? | |||||
| No data yet. Upload an Excel file to populate this section. | |||||
Operation(Business Outcomes & KPIs) How to measure the success? | |||||
| No data yet. Upload an Excel file to populate this section. | |||||
Governance Framework
Data Quality
Ensure accuracy, completeness, consistency, and timeliness of your data
Master Data Management
Create a single source of truth for critical business entities
Data Catalog
Discover, understand, and govern data assets across the organization
Data Observability
Monitor data pipelines, detect anomalies, and ensure data reliability
FinOps for Data
Optimize cloud data infrastructure costs while maintaining performance
Data Policy
Establish and enforce comprehensive data policies for compliance and protection
Why Data Governance Matters
Improved Decision Making
Trust your data for critical business decisions
Regulatory Compliance
Meet GDPR, CCPA, and industry regulations
Operational Efficiency
Reduce time spent on data issues
Cost Reduction
Minimize data-related errors and rework