{"id":5074,"date":"2025-11-03T13:28:46","date_gmt":"2025-11-03T18:28:46","guid":{"rendered":"https:\/\/staging.dcamportal.org\/?p=5074"},"modified":"2026-02-24T15:12:42","modified_gmt":"2026-02-24T20:12:42","slug":"dcam-v3-framework-beta-6-0-governance-data-data-management-program","status":"publish","type":"post","link":"https:\/\/staging.dcamportal.org\/fr_ca\/dcam-v3-framework-6-governance-data-data-management-program\/","title":{"rendered":"DCAM v3 Framework – 6.0 Governance \u2013 Data & Data Management Program"},"content":{"rendered":"
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\"DCAM<\/p>\r\n\r\n

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Upper Matter<\/h3>\r\n<\/div>\r\n
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Introduction<\/h3>\nGovernance is essential for a successful Data Management Program. It involves formalizing oversight and coordination of all DCAM<\/span> capabilities by setting control guidelines, approval processes, and evaluating adherence to policies, standards, and processes. Accountability is ensured by designating owners and stakeholders to uphold this framework. Data Management requires aspects of governance across the full spectrum of the data development life cycle. To fully understand the scope of Data Governance across the data life cycle, key considerations must include (but are not limited to):\n
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  • Data Requirements<\/b> – curating requirements based on the business needs and objectives, with review and approval consistent with the organization’s data governance practices.<\/li>\r\n \t
  • Data Architecture<\/span><\/b> – establishing consistent definition, design, standards, models, terminology, and formatting rules for data.<\/li>\r\n \t
  • Data Ownership and Stewardship<\/b> - clearly defining responsibilities for managing specific data assets within the organization.<\/li>\r\n \t
  • Data Quality<\/span><\/b> – ensuring data is accurate, complete, consistent, reliable, and validated for intended purpose.<\/li>\r\n \t
  • Data Privacy<\/b> – complying with privacy regulations such as GDPR<\/span> and CCPA.<\/li>\r\n \t
  • Data Security<\/span><\/b> – safeguarding data from unauthorized access, breaches, and misuse.<\/li>\r\n \t
  • Data Access and Use<\/b> – regulating data access and usage based on roles and permissions.<\/li>\r\n \t
  • Data Ethics<\/b> – ensuring that data management, usage, and collaboration practices align with the organization's values and ethical standards.<\/li>\r\n \t
  • Data Development Life Cycle<\/b> – managing data throughout its entire life cycle, from creation to disposal, including archiving and retention practices.<\/li>\r\n \t
  • Regulatory Compliance<\/b> – aligning Data Management practices with applicable industry regulations and laws.<\/li>\r\n<\/ul>\nThe goal is to ensure that the data of the organization, based on requirements, is accurate, reliable, accessible, protected, adheres to policies and standards, and is used consistently and appropriately. Appropriate usage is based on an understanding of who is using data and the purpose for which it is being used. The Data Governance Approach and Plan must be aligned with the Data & Data Management Strategies which are established and maintained based on the ongoing needs and objectives of the business.\n

    Definition<\/h3>\r\nThe Governance – Data & Data Management Program<\/b> component is a set of capabilities that codify the structure, lines of authority, roles and responsibilities, escalation protocol, policy<\/span> and standards, compliance, and routines for managing and facilitating processes across Data Management functions. This ensures authoritative decision-making at all levels of the organization.\n

    Scope<\/h3>\r\n
      \r\n \t
    • Establish a Data Governance function<\/span>.<\/li>\r\n \t
    • Design and implement sustainable business-as-usual processes and tools for Data Governance.<\/li>\r\n \t
    • Define clear roles, responsibilities, and accountabilities for Data Governance resources.<\/li>\r\n \t
    • Establish a Data Governance structure with clear authority, decision-making responsibilities, stakeholder<\/span> engagement, oversight, and ethical data use and outcomes.<\/li>\r\n \t
    • Monitor issue management (data issues, Data Governance issues, Data Management issues).<\/li>\r\n \t
    • Create and maintain Data Management policies, standards, rules, and procedures.<\/li>\r\n \t
    • Monitor adherence to policies, standards, rules, and procedures.<\/li>\r\n \t
    • Ensure that Data Governance aligns and collaborates with other relevant Data Management functions.<\/li>\r\n<\/ul>\r\n

      Value Proposition<\/h3>\r\nOrganizations that establish, clearly communicate, and enforce Data Management policies and standards minimize data management risks while ensuring data is effective for business decision-making.\r\n

      Overview<\/h3>\nData Governance is the key to successful, sustained data management. The approach, and its effective implementation, establishes lines of authority and ensures that the principles of Data Management can and will be implemented. It establishes the mechanisms for stakeholder<\/span> collaboration and defines the organizational structure by which the Data Management initiative will be governed.\n\nThe governance structure determines where the initiative resides in the corporate hierarchy, helps manage stakeholder<\/span> expectations, aligns policies and standards to the organization’s mission and values, ensures the adoption of policies and standards, articulates the mechanism for conflict resolution, provides for governance over allocated funding, and sets the methodology for measuring Data Management progress.\n\nGovernance over the Data Management initiative is multidimensional and includes activities related to each DCAM<\/span> component. While the most appropriate structure for any individual organization will vary, a clear mission meeting business needs is essential for long-term success. For example, domain<\/span> councils might exist to oversee the intersection of business, data, and technology functions. Governing boards might be created to establish business data priorities and resolve conflicts. Tactical groups might exist to manage workflow, perform data reconciliation, address the quality of critical data elements, perform business analysis, and provide triage to resolve issues with defective data or outcomes that violate the organization’s ethical standards. Some organizations may elicit all these types of structures as part of their overall governance framework to successfully embed Data Management concepts into the organization's culture.\n\nAfter the initial implementation, the governance framework must be periodically evaluated, monitored, and adjusted based on business reality to ensure it is effectively integrated into business-as-usual processes.\n

      Core Questions<\/h3>\r\n
        \r\n \t
      • Has the Data Governance Approach and operating model<\/span> been defined and adopted?<\/li>\r\n \t
      • Have the Data Governance Structure, policies and standards, roles and responsibilities, and processes been defined and implemented?<\/li>\r\n \t
      • Have Data Issues and Exception Processes been defined, implemented, and managed?<\/li>\r\n \t
      • Are data domains governed?<\/li>\r\n \t
      • Are the appropriate executives identified and engaged?<\/li>\r\n \t
      • Has the methodology to ensure compliance with established policies, standards and processes across the full data development life cycle been defined?<\/li>\r\n \t
      • Have Data Governance metrics been validated by stakeholder<\/span> criteria, aligned with business objectives and are they being collected in a timely manner?<\/li>\r\n<\/ul>\r\n

        Core Artifacts<\/h3>\r\nThe following are the core artifacts required to execute an effective Data Governance capability:\r\n
          \r\n \t
        • Data Governance Approach & Operating Model<\/span><\/li>\r\n \t
        • Charter(s) for required governing bodies<\/li>\r\n \t
        • Data Management Policy<\/span><\/li>\r\n \t
        • Data Management Principles<\/li>\r\n \t
        • Data Management Standards<\/li>\r\n \t
        • Data Governance Standards and Processes<\/li>\r\n<\/ul>\r\n\r\n\r\n<\/div>\r\n<\/div>\r\n\r\n
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          6.1 Data Governance<\/h3>\r\n<\/div>\r\n
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          \n\nThe Data Governance model<\/span> is inclusive of the Approach. The governance organization structure must be defined and approved by stakeholders and executive management. Roles and responsibilities across the stakeholders must be established with operational processes in place.\n\n\n\n<\/div>\r\n<\/div>\r\n\r\n
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          6.1.1 Data Governance Approach and Plan<\/h3>\r\n<\/div>\r\n
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          Description<\/h5>\r\nThe approach to Data Governance must be defined and reflect the related vision and objectives of the Data Management Strategy.\r\n
          Objectives<\/h5>\r\n
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          • Formally establish the Data Governance Approach within the organization.<\/li>\r\n \t
          • Gain approval and authority for the Data Governance Approach from stakeholders and Executive Management.<\/li>\r\n \t
          • Ensure alignment of stakeholder<\/span> plans and roadmaps with the Data Governance Approach.<\/li>\r\n \t
          • Communicate the role of Data Governance across the organization through formal organizational channels.<\/li>\r\n \t
          • Confirm the Data Governance authority is supported through policy<\/span>.<\/li>\r\n<\/ul>\r\n
            Advice<\/h5>\nData Governance emphasizes the organizational requirements necessary to successfully implement the objectives of the Data Management Strategy. It is essential for the organization to understand the concept of governance and the practical considerations involved in preparing stakeholders to adopt required practices for establishing a sustainable governance model<\/span>.\n\nLeading with governance concepts, not details, is recommended to ensure leadership understands the vision. Data Governance begins with the establishment of the Data Management Strategy, considering the organization’s business priorities and data objectives. Strategically, the primary goal is to ensure that executive leadership acknowledges that Data Governance is mandatory for effective Data Management. This will change and challenge current operational practices. Early and interactive engagement with stakeholders will help reinforce buy-in. Think of this as crafting the governance deal with an appropriate balance between the concepts of governance (clarity on need), the value of governance (coordination and predictability) and the impact of governance (operational and cultural implications).\n\nData ethics activities, if not fully integrated with the Data Management initiative, should at the very least be integrated into the Data Governance function<\/span>. The Data Governance Approach and Plan must be aligned to the Data Management Strategy and regularly reviewed and updated.\n
            Questions<\/h5>\r\n
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            • Is there a Data Governance Approach and Plan in place?<\/li>\r\n \t
            • Is the Data Governance Approach and Plan aligned to the Data Management Strategy?<\/li>\r\n \t
            • Have the Data Governance Approach & Plan been formally communicated to business, technology, operations, finance and risk?<\/li>\r\n \t
            • Has the Data Governance function<\/span> been granted authority to implement the Approach?<\/li>\r\n \t
            • Has authority been communicated to stakeholders?<\/li>\r\n<\/ul>\r\n
              Artifacts<\/h5>\r\n
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              • Data Governance Approach & Plan<\/li>\r\n<\/ul>\r\n
                Scoring<\/h5>\r\n
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                \r\n\r\nNot Initiated\r\n\r\n<\/div>\r\n\r\n
                \n\nNo formal Data Governance Approach & Plan exists.\n\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
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                \r\n\r\nConceptual\r\n\r\n<\/div>\r\n\r\n
                \n\nNo formal Data Governance Approach & Plan exists, but the need is recognized, and the development is being discussed.\n\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
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                \r\n\r\nDevelopmental\r\n\r\n<\/div>\r\n\r\n
                \n\nFormal Data Governance Approach & Plan are being developed.\n\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
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                \r\n\r\nDefined\r\n\r\n<\/div>\r\n\r\n
                \n\nThe formal Data Governance Approach & Plan are defined and validated and approved by the stakeholders.\n\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
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                \r\n\r\nAchieved\r\n\r\n<\/div>\r\n\r\n
                \n\nThe formal Data Governance Approach & Plan are established, understood and adopted across the organization.\n\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
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                \r\n\r\nEnhanced\r\n\r\n<\/div>\r\n\r\n
                \n\nThe formal Data Governance Approach & Plan is established as part of business-as-usual practice and is regularly reviewed for relevance and effectiveness.\n\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n\r\n\r\n<\/div>\r\n<\/div>\r\n\r\n
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                6.1.2 Data Governance Structure<\/h3>\r\n<\/div>\r\n\r\n
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                Description<\/h5>\nThe Data Governance Structure must align with the operating levels of the organization. In addition to defining governance at the enterprise<\/span> level, individuals must be appointed within the various operating units and given responsibility for Data Governance within those verticals. The Structure must be endorsed by senior management and communicated to all stakeholders.\n
                Objectives<\/h5>\r\n
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                • Document the Data Governance structure and share with relevant stakeholders.<\/li>\r\n \t
                • Implement the Data Governance Structure.<\/li>\r\n \t
                • Establish the Data Governance Committee(s), defined within the Governance Structure, with written and approved charters.<\/li>\r\n<\/ul>\r\n
                  Advice<\/h5>\nThere is no single correct way to define a Data Governance Structure. Amongst other factors, it must consider the size and complexity of the organization, the scope of the organization’s activities, the skill of the staff, the degree of regulation in the industry, and the culture of the organization.\n\nDeveloping a new Data Governance program will likely require new skill sets. Collaboration with HR and senior business stakeholders to define, recruit, and hire individuals with these skills will help facilitate implementation. Structured training in data management and data governance will facilitate a clear understanding of responsibilities associated with assigned roles and can have a positive effect on sourcing the roles internally.\n\nThe Data Governance Structure establishes a system for making authoritative decisions regarding data and Data Management initiatives. Designated stakeholders should be appointed at the enterprise<\/span> level to lead and coordinate these efforts across the organization. It's crucial to ensure that decisions are made at the appropriate level while also incorporating the necessary subject matter expertise. When forming governing bodies, their oversight should be confined to the areas of expertise of the participants.\n\nAvoid overcomplicating the process<\/span> and ensure that governance is viewed as a control function<\/span> rather than a judicial one.\n
                  Questions<\/h5>\r\n
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                  • Has the governance structure been defined and socialized to make sure it is appropriate for the organization?<\/li>\r\n \t
                  • Have the key roles been identified?<\/li>\r\n \t
                  • Has a data education curriculum been established to encourage Data Governance understanding for all impacted stakeholders and participants?<\/li>\r\n \t
                  • Has a Data Governance structure been formally established?<\/li>\r\n<\/ul>\r\n
                    Artifacts<\/h5>\r\n
                      \r\n \t
                    • Data Governance Structure (organization charts and roles)<\/li>\r\n \t
                    • Charter(s) for required governing bodies<\/li>\r\n \t
                    • Bi-directional communication such as stakeholder<\/span> rosters, internal memos, and meeting minutes<\/li>\r\n<\/ul>\r\n
                      Scoring<\/h5>\r\n
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                      \r\n\r\nNot Initiated\r\n\r\n<\/div>\r\n\r\n
                      \r\n\r\nNo formal Data Governance Structure exists.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                      \r\n
                      \r\n\r\nConceptual\r\n\r\n<\/div>\r\n\r\n
                      \r\n\r\nThe concepts associated with establishing a formal Data Governance Structure are being discussed.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                      \r\n
                      \r\n\r\nDevelopmental\r\n\r\n<\/div>\r\n\r\n
                      \n\nA formal Data Governance Structure is being developed.\n\nRepresentatives from involved business lines and control functions are participating in the planning process<\/span>.\n\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                      \r\n
                      \r\n\r\nDefined\r\n\r\n<\/div>\r\n\r\n
                      \r\n\r\nA formal Data Governance Structure has been documented and approved. Key roles have been identified.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                      \r\n
                      \r\n\r\nAchieved\r\n\r\n<\/div>\r\n\r\n
                      \r\n\r\nThe approved Data Governance Structure is implemented.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                      \r\n
                      \r\n\r\nEnhanced\r\n\r\n<\/div>\r\n\r\n
                      \r\n\r\nThe approved Data Governance structure is operational and assessed for efficacy on a regular basis.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n\r\n\r\n<\/div>\r\n<\/div>\r\n\r\n
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                      6.1.3 Data Governance Roles and Responsibilities<\/h3>\r\n<\/div>\r\n\r\n
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                      Description<\/h5>\r\nData Governance roles and responsibilities must be established. The responsibilities of the key roles are defined and adopted by stakeholders (e.g., data executives, data stewards, data custodians).\r\n
                      Objectives<\/h5>\r\n
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                      • Define and communicate a comprehensive list of Data Governance roles and associated responsibilities.<\/li>\r\n \t
                      • Ensure that the Data Governance roles and responsibilities are properly reflected in governing documents (e.g., charter, policy<\/span> and standards).<\/li>\r\n \t
                      • Hold individuals accountable for their assigned responsibilities.<\/li>\r\n<\/ul>\r\n
                        Advice<\/h5>\nThink carefully about how the governance process<\/span> will work in the real world. It is important to evaluate roles and responsibilities from all perspectives including that of sponsors with executive authority, owners and other accountable parties. Both business stewards who manage data content and technology stewards who handle technical implementation have roles and responsibilities in data governance that need to be defined. In addition, clarity on responsibilities can be supported with the use of RACI or RASCI charts (Responsible, Accountable, Supported, Consulted, Informed). In some cases, there may be people already fulfilling some aspects of the role. Identifying these key subject matter experts may decrease adoption time.\n
                        Questions<\/h5>\r\n
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                        • Have the roles and responsibilities of Data Governance been fully defined, documented, in collaboration with the impacted business?<\/li>\r\n \t
                        • Do Data Governance roles have proper responsibilities and authority to be effective?<\/li>\r\n \t
                        • Is Data Governance appropriately staffed?<\/li>\r\n \t
                        • Have the assigned stakeholder<\/span> responsibilities been socialized with impacted business stakeholders?<\/li>\r\n \t
                        • Is there a succession plan in place for key stakeholders?<\/li>\r\n \t
                        • Are the assigned roles regularly evaluated to determine if additional training\/skills are required for the stakeholders to be successful?<\/li>\r\n<\/ul>\r\n
                          Artifacts<\/h5>\r\n
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                          • Evidence of stakeholder<\/span> role assignment<\/li>\r\n \t
                          • RASCI matrix or other evidence of accountability assignment<\/li>\r\n \t
                          • Documentation of the roles and responsibilities in key governing documents<\/li>\r\n \t
                          • Assessment of staff qualifications and identification of skill gaps<\/li>\r\n<\/ul>\r\n
                            Scoring<\/h5>\r\n
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                            \r\n\r\nNot Initiated\r\n\r\n<\/div>\r\n\r\n
                            \n\nNo formal Data Governance roles & responsibilities exist.\n\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                            \r\n
                            \r\n\r\nConceptual\r\n\r\n<\/div>\r\n\r\n
                            \n\nNo formal Data Governance roles & responsibilities exist, but the need is recognized, and their development is being discussed.\n\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                            \r\n
                            \r\n\r\nDevelopmental\r\n\r\n<\/div>\r\n\r\n
                            \r\n\r\nFormal Data Governance roles and responsibilities are being developed.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                            \r\n
                            \r\n\r\nDefined\r\n\r\n<\/div>\r\n\r\n
                            \r\n\r\nThe Data Governance roles are responsibilities are defined and have been validated and approved by the directly involved stakeholders.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                            \r\n
                            \r\n\r\nAchieved\r\n\r\n<\/div>\r\n\r\n
                            \r\n\r\nThe Data Governance roles are filled, and responsibilities are communicated and recognized by stakeholders.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                            \r\n
                            \r\n\r\nEnhanced\r\n\r\n<\/div>\r\n\r\n
                            \r\n\r\nThe Data Governance roles and responsibilities are established as part of business-as-usual practice and are regularly reviewed for clarity and relevance.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n\r\n\r\n<\/div>\r\n<\/div>\r\n\r\n
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                            6.1.4 Data Governance Standard Processes<\/h3>\r\n<\/div>\r\n\r\n
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                            Description<\/h5>\nFormal processes must be established for the key Data Governance activities. These Data Governance processes align with the Data Management policy<\/span> and standards of the organization. The processes are required for steady-state operations.\n
                            Objectives<\/h5>\r\n
                              \r\n \t
                            • Establish formal Data Governance processes in alignment with the Data Management policy<\/span> and standards.<\/li>\r\n \t
                            • Integrate the Data Governance processes into the overall end-to-end processes of the Data Management initiative and align them with the data development life cycle.<\/li>\r\n \t
                            • Identify, schedule, and maintain Data Governance routines, meetings, and working sessions required for operational support.<\/li>\r\n<\/ul>\r\n
                              Advice<\/h5>\nData Governance subject matter experts should work with relevant teams to create and standardize Data Governance processes, ensuring they align with the entire Data Management initiative. Consistency<\/span> in practices related to authority, policy<\/span>, and control is crucial.\n\nIt is important to address challenging organizational issues related to how data governance will affect change in the organization. Don’t underestimate the difficulties associated with, or minimize the importance of, getting agreement on essential concepts like authority, policy<\/span>, and control. The Data Governance team should work with the organizations change management experts to manage change impact in the organization.\n\nData Governance is an ongoing effort vital for achieving the organization's data and business objectives. The goal is to ensure that Data Governance becomes adopted as business-as-usual across the organization.\n
                              Questions<\/h5>\r\n
                                \r\n \t
                              • Are formal Data Governance processes defined and operational?<\/li>\r\n \t
                              • Are specific procedures, tools and routines in place for support processes?<\/li>\r\n \t
                              • Are standard<\/span> processes being followed?<\/li>\r\n \t
                              • Are there meetings, planning sessions and regular communications about Data Governance initiatives?<\/li>\r\n<\/ul>\r\n
                                Artifacts<\/h5>\r\n
                                  \r\n \t
                                • Data Governance Process<\/span> documentation<\/li>\r\n \t
                                • Process<\/span> performance and compliance metric reports<\/li>\r\n \t
                                • Meeting minutes, status reports, and Data Governance communications<\/li>\r\n<\/ul>\r\n
                                  Scoring<\/h5>\r\n
                                  \r\n
                                  \r\n
                                  \r\n
                                  \r\n\r\nNot Initiated\r\n\r\n<\/div>\r\n\r\n
                                  \r\n\r\nNo formal Data Governance operational processes exist.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                                  \r\n
                                  \r\n\r\nConceptual\r\n\r\n<\/div>\r\n\r\n
                                  \r\n\r\nNo formal Data Governance operational processes exist, but the need is recognized, and their development is being discussed.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                                  \r\n
                                  \r\n\r\nDevelopmental\r\n\r\n<\/div>\r\n\r\n
                                  \r\n\r\nFormal Data Governance operational processes are being developed.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                                  \r\n
                                  \r\n\r\nDefined\r\n\r\n<\/div>\r\n\r\n
                                  \r\n\r\nFormal Data Governance operational processes are defined and approved by appropriate stakeholders.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                                  \r\n
                                  \r\n\r\nAchieved\r\n\r\n<\/div>\r\n\r\n
                                  \r\n\r\nThe approved Data Governance operational processes are established, owned, recognized and used by stakeholders.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                                  \r\n
                                  \r\n\r\nEnhanced\r\n\r\n<\/div>\r\n\r\n
                                  \r\n\r\nThe Data Governance operational processes are established as part of business-as-usual practice and are regularly reviewed for efficacy and relevance.\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n\r\n\r\n\r\n<\/div>\r\n\r\n\r\n<\/div>\r\n\r\n
                                  \r\n\r\n

                                  6.2 Govern the Data Management Program<\/h3>\r\n<\/div>\r\n
                                  \r\n\r\n\r\n
                                  \r\n\r\nGoverning the Data Management program includes the administration of data management policies and standards, funding, program and project management, and issue management.\r\n\r\n\r\n\r\n<\/div>\r\n<\/div>\r\n\r\n
                                  \r\n\r\n

                                  6.2.1 Data and Data Management Policies and Standards<\/h3>\r\n<\/div>\r\n\r\n
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                                  Description<\/h5>\nPolicy<\/span> and standards must reflect the basic principles of Data Management. Data Management policies and standards must be established for the organization and adopted by stakeholders and executive governing bodies. The policies and standards must align with relevant cross-organizational policies and standards, be clearly documented, and align with the Data Management Strategy and business priorities. They must address how data is acquired, managed, maintained and delivered throughout an organization.\n
                                  Objectives<\/h5>\r\n
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                                  • Develop policies and standards in collaboration with business, technology, and operations stakeholders.<\/li>\r\n \t
                                  • Align policies and standards with the Data Management Strategy.<\/li>\r\n \t
                                  • Coordinate, reference and align with cross-organizational policies and standards.<\/li>\r\n \t
                                  • Engage with stakeholders to communicate policies and standards.<\/li>\r\n<\/ul>\r\n
                                    Advice<\/h5>\nDeveloping and implementing policies and standards turn the Data Management initiative from a good idea into a functioning reality. They constitute the rules for how data will be managed to ensure that data is trusted and meaningful. They must be based on core principles, linked to the Data Management Strategy, and integrated into normal business processes. Policies and standards must be objectively actionable and measured consistently to monitor progress against Data Management Strategy & Business Case.\n\nAlthough they can vary, most policies and standards will contain rules and guidelines pertaining to data ownership, data definition, data lineage<\/span>, metadata<\/span>, data quality<\/span>, data access, permissible use, data sourcing, and controls.\n\nPolicies and standards drive business procedures and behaviors. Ensuring adequate communication with all stakeholders is paramount to ensure that all understand how the policies and standards impact their business and functional roles. Consider validating that coverage<\/span> and adherence are adequately measured to meet business priorities.\n\nData Governance leaders should work with all cross-organizational control functions to identify the intersection of data-related mandates that may impact other control objectives. Likewise, other policies and standards should reference the Data Governance policies and standards, when appropriate.\n\nEstablish the necessary review and approval processes along the data life cycle to ensure that decisions about the acquisition, use and distribution of data adhere to the appropriate policy<\/span> and standards.\n
                                    Questions<\/h5>\r\n
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                                    • Have the Data Management policies and standards been created, published, and aligned with the Data Management Strategy?<\/li>\r\n \t
                                    • Have they been developed and verified in collaboration with stakeholders, including Internal Audit and executive management?<\/li>\r\n \t
                                    • Are the policies and standards complete and harmonized with cross-organization control functions (e.g., cross-border issues, security, privacy, ethics), data acquisition processes (e.g., legal contracts, entitlements), data usage (e.g., authorizations, redistribution), data retention (e.g., records, archiving, minimization, historical), quality control (e.g., business rules, logic checks, transformations), data meaning (e.g., identifiers, definitions, classifications), formats and messaging (e.g., schemas, metadata<\/span>, ISO standards)?<\/li>\r\n \t
                                    • If required, are the mechanisms to support coordination with regulators defined and operational?<\/li>\r\n<\/ul>\r\n
                                      Artifacts<\/h5>\r\n