Data Management Procedures represent formalized workflows and guidelines necessary for effective governance and management of data as a strategic asset. -------------------------------------- The Data Management Procedures proposed below provide a comprehensive framework for supporting a Data-Centric Governance Capability, ensuring a structured, repeatable, and scalable approach to managing data as a strategic asset. By implementing these procedures, organizations can improve data quality, enhance operational efficiency, and ensure compliance with regulatory and business requirements. 1. Data Governance Procedures: These procedures establish the overarching framework for governing data assets, ensuring accountability, and enforcing compliance across the organization: * Define roles and responsibilities (e.g., Data Owners, Data Stewards, Governance Committees). * Establish governance frameworks, policies, and decision-making mechanisms. * Manage data governance policies related to compliance, risk, and usage. * Develop and enforce data stewardship practices and accountability measures. 2. Data Quality Management Procedures: Focus on maintaining and improving the quality of data assets by aligning them with organizational requirements: * Develop processes for data profiling, quality measurement, and root cause analysis. * Define acceptable data quality thresholds and monitor against them. * Create procedures for identifying, resolving, and preventing data quality issues. * Develop data quality reporting mechanisms for stakeholders. 3. Data Lifecycle Management Procedures: Ensure proper management of data throughout its lifecycle, from creation to deletion: * Define retention polici*es and data archival processes. * Establish procedures for data versioning and tracking changes over time. * Develop disposal and secure destruction policies for obsolete data. * Ensure compliance with legal, regulatory, and business requirements during data lifecycle management. 4. Reference-, Master- and Metadata Management Procedures: Ensure consistency and reliability of shared, critical data entities across the organization to ensure interoperability, discoverability, traceability, and data understanding: * Develop processes for defining and managing reference-, meta- and master data. * Implement procedures for data harmonization, standardization, and integration. * Define workflows for managing hierarchies, relationships, and mappings within data sets. * Establish procedures for resolving discrepancies and ensuring data integrity. * Develop procedures for integrating metadata with tools like data catalogs. * Monitor metadata quality and its alignment with governance policies. 5. Data Integration and Interoperability Procedures: Enable seamless integration of data across disparate systems and ensure interoperability: * Define processes for integrating data from diverse sources, ensuring consistency and alignment. * Develop conflict resolution mechanisms for overlapping or duplicate data. * Establish data transformation and mapping procedures to align with organizational needs. * Define integration testing and validation procedures to ensure data integrity. 6. Data Quality Monitoring and Reporting Procedures: Provide continuous oversight of data assets and their alignment with governance frameworks: * Establish monitoring procedures for data quality, access, and compliance. * Define processes for creating and distributing dashboards, reports, and metrics. * Develop escalation workflows for handling deviations and anomalies. * Monitor the effectiveness of governance policies and update them as necessary. 7. Data Sharing and Usage Procedures: Facilitate appropriate data access and sharing within and outside the organization: * Define policies and processes for data access requests and approvals. * Establish procedures for creating and maintaining data-sharing agreements. * Develop guidelines for tracking data usage and ensuring compliance with sharing policies.