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EA Governance Policies in Data Governance

EA Governance Policies in Data Governance

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This curriculum spans the design and operationalization of governance mechanisms at the intersection of enterprise architecture and data governance, comparable in scope to a multi-phase advisory engagement addressing policy integration, role alignment, compliance enforcement, and lifecycle management across complex, federated organizations.

Module 1: Defining the Scope and Authority of EA Governance in Data Governance

  • Determine which enterprise architecture domains (business, data, application, technology) require formal data governance integration based on regulatory exposure and system interdependencies.
  • Establish escalation paths for conflicts between data governance stewards and enterprise architects over data model ownership.
  • Define the threshold for mandatory EA review of data architecture changes, such as new data stores or integration patterns.
  • Negotiate data governance committee representation with enterprise architecture review boards to ensure alignment on standards.
  • Document exceptions to EA data standards for legacy systems, including risk acceptance and sunset timelines.
  • Map data governance roles (e.g., data stewards) to EA roles (e.g., domain architects) to clarify decision rights.
  • Specify whether EA has veto power over data platform procurement decisions that conflict with architectural principles.
  • Develop criteria for when data governance initiatives require formal EA impact assessments.

Module 2: Aligning Data Governance Frameworks with Enterprise Architecture Principles

  • Adapt data governance operating models (centralized, decentralized, hybrid) to match existing EA governance maturity and organizational structure.
  • Integrate data governance controls into EA decision gates within the solution delivery lifecycle.
  • Map data governance policies (e.g., data quality rules) to EA artifacts such as capability models and technology standards.
  • Enforce consistency between data domain definitions in the enterprise data model and business capability maps.
  • Require data governance sign-off on EA principle updates that impact data ownership or lifecycle management.
  • Embed data governance KPIs into EA performance dashboards for cross-functional visibility.
  • Resolve conflicts between data governance mandates (e.g., data localization) and EA-driven cloud migration strategies.
  • Define interface standards between data governance tools (e.g., metadata repositories) and EA management platforms.

Module 3: Governing Data Standards and Metadata Management

  • Mandate the use of canonical data models in integration projects and define enforcement mechanisms through EA reviews.
  • Establish naming conventions for data elements that are enforced in both data governance catalogs and EA documentation.
  • Require metadata lineage to be captured in EA tools for systems designated as source-of-truth for critical data domains.
  • Define ownership of enterprise-wide metadata standards between data governance and EA teams.
  • Implement automated validation of data model compliance against EA standards during CI/CD pipeline execution.
  • Decide whether metadata synchronization between data governance and EA tools occurs in real time or batch mode based on system constraints.
  • Classify metadata types (technical, operational, business) and assign stewardship responsibilities aligned with EA domains.
  • Address inconsistencies in metadata definitions across business units by enforcing EA-approved semantic harmonization rules.

Module 4: Integrating Data Governance into Solution Delivery Lifecycle

  • Embed data governance checkpoints into EA solution review milestones (e.g., conceptual, logical, physical design).
  • Require data impact assessments for all projects involving changes to core data entities, reviewed by both EA and data governance leads.
  • Define minimum data quality thresholds that must be met before a solution can receive EA compliance sign-off.
  • Integrate data governance artifacts (e.g., data dictionaries) into EA-approved solution documentation templates.
  • Enforce the use of approved data integration patterns (e.g., event-driven, ETL) based on EA standards.
  • Track data-related technical debt in EA portfolios and prioritize remediation based on business impact.
  • Coordinate data model versioning across projects to prevent divergence from the enterprise logical model.
  • Require data governance validation for API contracts that expose sensitive or regulated data.

Module 5: Managing Data Ownership and Stewardship Across Enterprise Units

  • Assign data domain owners based on business capability ownership defined in the EA business architecture.
  • Resolve dual ownership claims between business units and IT for enterprise data assets using EA-facilitated RACI models.
  • Define escalation procedures when data stewards and solution architects disagree on data handling practices.
  • Map data stewardship responsibilities to organizational units in the EA organizational model.
  • Enforce data ownership accountability by linking stewardship roles to performance metrics in EA governance reports.
  • Establish data custodianship agreements between IT operations and business data owners based on EA-defined system boundaries.
  • Update stewardship assignments during organizational restructuring using EA impact analysis.
  • Document data handoff points between systems and assign stewardship at integration interfaces.

Module 6: Enforcing Compliance and Risk Management Through EA Controls

  • Embed regulatory data requirements (e.g., GDPR, CCPA) into EA compliance checklists for system certification.
  • Map data sensitivity classifications to EA-defined security zones and network segmentation rules.
  • Require data protection impact assessments for new solutions handling personal data, reviewed by EA security architects.
  • Enforce encryption standards for data at rest and in transit based on EA security baselines.
  • Track non-compliant data flows in the EA landscape and prioritize remediation based on risk exposure.
  • Integrate data retention policies into EA data lifecycle management standards.
  • Conduct EA-led audits of data governance controls in high-risk systems using standardized assessment frameworks.
  • Define data breach response protocols that specify EA’s role in isolating affected systems and data pathways.

Module 7: Governing Data Architecture and Technology Standards

  • Approve or reject data platform technologies (e.g., data lakes, warehouses) based on EA technology reference models.
  • Define data storage tiering policies aligned with EA cost, performance, and availability standards.
  • Enforce use of enterprise messaging standards for data exchange between applications.
  • Establish criteria for adopting new data processing frameworks (e.g., streaming, batch) based on architectural fit.
  • Prohibit shadow data architectures by requiring all data infrastructure changes to undergo EA review.
  • Define data replication rules across environments (development, production) based on EA environment management policies.
  • Standardize data access methods (APIs, views, extracts) to reduce technical sprawl and improve manageability.
  • Manage technical obsolescence by defining sunset timelines for deprecated data technologies in the EA roadmap.

Module 8: Measuring and Reporting Governance Effectiveness

  • Define EA-aligned KPIs for data governance, such as percentage of systems compliant with metadata standards.
  • Integrate data governance metrics into enterprise architecture dashboards for executive reporting.
  • Conduct maturity assessments of data governance practices using EA evaluation frameworks.
  • Track resolution times for data-related architecture review findings to assess governance responsiveness.
  • Report on data standard adoption rates across business units using EA change management data.
  • Identify data governance bottlenecks in project delivery timelines and recommend process adjustments.
  • Measure data quality improvement trends in systems post-implementation of EA and governance controls.
  • Use heat maps to visualize data governance risk exposure across the EA application portfolio.

Module 9: Sustaining Governance Through Organizational Change and Innovation

  • Assess the impact of mergers and acquisitions on existing data governance and EA policies.
  • Adapt governance processes to accommodate agile and DevOps delivery models without compromising control.
  • Introduce data governance sandboxes for innovation projects with temporary waivers from standard EA rules.
  • Update EA principles to reflect emerging data technologies (e.g., AI/ML, blockchain) and their governance implications.
  • Facilitate governance alignment when adopting third-party SaaS platforms with embedded data models.
  • Reconcile decentralized data mesh implementations with centralized EA governance expectations.
  • Manage cultural resistance to governance by aligning enforcement with business outcome tracking in EA value streams.
  • Establish innovation review boards to evaluate experimental data architectures against long-term EA sustainability.
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