{"id":5038,"date":"2025-10-15T10:52:14","date_gmt":"2025-10-15T14:52:14","guid":{"rendered":"https:\/\/staging.dcamportal.org\/?p=5038"},"modified":"2026-02-24T15:07:33","modified_gmt":"2026-02-24T20:07:33","slug":"dcam-v3-framework-beta-1-0-data-strategy-data-management-strategy-amp-data-management-business-case","status":"publish","type":"post","link":"https:\/\/staging.dcamportal.org\/fr\/dcam-v3-framework-1-data-strategy-data-management-strategy-data-management-business-case\/","title":{"rendered":"Cadre DCAM v3 \u2013 1.0 Strat\u00e9gie de donn\u00e9es, strat\u00e9gie de gestion des donn\u00e9es et analyse de rentabilit\u00e9 de la gestion des donn\u00e9es"},"content":{"rendered":"
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\"DCRAM<\/p>\r\n\r\n

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Upper Matter<\/h3>\r\n<\/div>\r\n\r\n
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Introduction<\/h3>\r\nThe Data Strategy, Data Management Strategy & Data Management Business Case<\/b> component defines how data and data management capabilities are linked to top level business objectives and embedded into the operations of the organization. It articulates the long-term value and vision for data, the data management capabilities, and identifies the stakeholders that must be aligned to achieve the organization’s business objectives with data.\n\nThe Data Strategy focuses on connecting strategic business objectives and priorities with the necessary data scope needed to achieve those goals while ensuring alignment with stakeholders. The Data Management Strategy builds on this by outlining the data management capabilities essential for attaining these objectives and delivering business value. It clarifies the rationale behind the required capabilities, along with their associated goals and anticipated benefits. The Data Management Strategy serves as a blueprint for the organization to assess, prioritize, plan, measure, implement, and enhance data management capabilities across the DCAM<\/span> components. Additionally, the Data Management Strategy outlines the steps for effectively mobilizing the organization to successfully implement these capabilities. The Data Management Business Case evaluates the investment required and the benefits derived from data management capabilities, also serving as a contribution to component 2.0, Program Management & Funding. Like other business capabilities, Data Management needs to be justified, funded, measured, and assessed as part of the organization’s strategic planning process<\/span>. This methodology fosters clear objectives, mutual agreement, and backing from senior executives and various stakeholders.\n

\"diagram<\/p>\r\n

Diagram 1.1: Business Strategy supported by Data Management<\/p>\r\n\r\n

Definitions<\/h3>\r\nData Strategy<\/b> is the formal specification of an organization’s business objectives, priorities, and scope of data required to achieve the objectives (particularly content and planned usage). Its purpose is to identify, align, and prioritize data requirements for the organization and specific stakeholders. It is derived from the overall (non-data specific) business objectives and strategic priorities of the organization.\r\n\r\nData Management Business Case<\/b> justifies creating and funding data management capabilities as defined in the Data Management Strategy. It articulates major data needs of the organization, quantifies the expected outcomes and benefits achieved through implementation of data management capabilities, and describes how value is realized from the data assets of an organization, through the collaboration of business, data, and technology\r\n

Scope<\/h3>\r\n
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  • Establish and maintain a documented Data Strategy, specifying the data content and usage, and Data Management Strategy needed to support the business, leveraging DCAM<\/span> to articulate data management capabilities.<\/li>\r\n \t
  • Align the Data Strategy and Data Management Strategy with the business strategy, objectives, and priorities, including prioritization of data based on its criticality to the business.<\/li>\r\n \t
  • Engage with key stakeholders to articulate and define the current state and future state target for data management capabilities using an assessment tool such as DCAM<\/span>.<\/li>\r\n \t
  • Prioritize the goals of the Data Management Strategy and establish a deployment roadmap and timeline for implementation.<\/li>\r\n \t
  • Establish the justification and business rationale for managing data through the organization's comprehensive data management initiative. The business cases should highlight the value of data and advocate investment in data management capabilities, whether for improvement projects or ongoing operations and practices.<\/li>\r\n<\/ul>\r\n

    Value Proposition<\/h3>\nOrganizations that have enterprise<\/span> and operating unit<\/span> executives and stakeholders who understand, support, and provide direction for Data Management initiatives have better acceptance of data management at all levels. It is important to engage staff at all levels across the organization to create sustainable Data Management success. Organizations that adopt effective data management practices experience a return on investment from multiple perspectives:\n
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    • Revenue growth and customer<\/span> satisfaction<\/li>\r\n \t
    • Improve operations and efficiency<\/li>\r\n \t
    • Stronger risk management and regulatory compliance<\/li>\r\n \t
    • Faster and easier digital transformation and innovation<\/li>\r\n<\/ul>\nIn developing the Data Strategy, it is important to consider both “offense”-related priorities (e.g., revenue growth) and “defense”-related priorities (e.g., regulatory compliance). Priorities often shift over time, and it is essential to develop capabilities that are sustainable across the long term<\/span>, rather than focusing only on near term<\/span> urgencies. Ideally, it should be possible to develop a balanced approach to leverage efforts for both offense and defense. The Data Strategy should be periodically reviewed, such as on an annual basis, in collaboration with the business to ensure it continues to align with business goals and objectives.\n

      Overview<\/h3>\nData Strategy, Data Management Strategy and Data Management Business Case are foundational components of the DCAM<\/span> Framework. It is important to note that the Data Management team should have strategic planning capabilities and skills available to support strategy and approach development and ongoing management activities.\n\nUsing the Framework provides structure and core principles for the data management initiative. It also helps stakeholders understand the value of data management in relation to their operating units and strategic goals.\n\nThe Data and Data Management Strategies should align with the organization's operating model<\/span> and include a roadmap and timeline to reach the target state. If the operating model<\/span> changes, the strategies must be updated accordingly.\n\nEffective strategies compare the current state to the target state to identify organizational, functional, operational, and technological gaps. A capability assessment tool can support this analysis, helping to define, prioritize, and schedule gap closure.\n\nEach operational unit must have a documented Data and Data Management Strategy that aligns with the enterprise-wide target operating model<\/span>. Within each unit, components contribute unique inputs that should be integrated and prioritized in the final strategy. These inputs must support the broader organizational goals and capability requirements defined by the target operating model<\/span>.\n\nBecause not all operational units will be of the same maturity in the design and execution of their data management initiative, these strategies are specific to their business objectives, priorities, and identified data management inefficiencies and gaps.\n\nThe description above may seem to imply a large and possibly complex organizational business structure that requires coordination to develop a comprehensive strategy for data. However, it is important to note that such structural complexity is not a requirement and the DCAM<\/span> Framework can be applied to an organization of any size that would like to maintain and sustain data management best practices to provide business partners with high quality trusted data and information. Regardless of whether an organization decides to compile these topics in a single or in multiple documents, all are required to develop a cohesive approach for the organization.\n\nThe importance of physical documentation is essential for effective data management and offers numerous benefits to an organization. Here are some key advantages:\n
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      1. Enhanced Communication<\/b>: Good documentation ensures that the strategy is communicated to key stakeholders in an ongoing and consistent manner. This helps in maintaining clarity and alignment across the organization.<\/li>\r\n \t
      2. Executive Endorsement<\/b>: Effective documentation is visibly endorsed by executive management and stakeholders, which fosters a culture of support and commitment towards the strategy.<\/li>\r\n \t
      3. Sustainability<\/b>: High-quality documentation supports the creation of sustainable data management practices by providing a clear and structured approach that can be followed and improved over time.<\/li>\r\n \t
      4. Multi-Channel Presence<\/b>: Documentation may need to exist in multiple channels – formal written frameworks, presentations, and online platforms. This ensures that the strategy is accessible and understood by all relevant parties.<\/li>\r\n \t
      5. Policy<\/span> Support<\/b>: Successful strategies are often supported by mandatory organizational policies which are clearly outlined in high-quality documentation.<\/li>\r\n<\/ol>\r\n\r\n

        Core Questions<\/h3>\r\n
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        • Are the Data Strategy, Data Management Strategy and Data Management Business Case documented and aligned with the organization’s business strategy and priorities?<\/li>\r\n \t
        • Does the Data Management Strategy clearly articulate the reason and the importance of implementing data management capabilities at various, appropriate levels of the organization?<\/li>\r\n \t
        • Is there executive buy-in across operations, and technology?<\/li>\r\n \t
        • Do stakeholders agree to support and sustain data management functions?<\/li>\r\n \t
        • Have the Data Strategy and Data Management Strategy defined the immediate, medium, and long-term goals and objectives of data management capabilities across the organization?<\/li>\r\n \t
        • Have the Data Management Strategy and Data Management Business Case effectively identified the critical areas of focus, including how priorities were established, verified, and measured?<\/li>\r\n \t
        • Has the Data Management Strategy identified the operating model<\/span> and resources required to establish, maintain, and improve data management both through initiatives and ongoing operations?<\/li>\r\n \t
        • Does the organization review the Data Strategy, Data Management Strategy, and Data Management Business Case on a routine basis, especially as the business changes?<\/li>\r\n<\/ul>\r\n\r\n

          Core Artifacts<\/h3>\r\nThe following are the core artifacts<\/b> required to execute an effective capability. Items with an * link to the EDM Association’s published practice guidelines.\r\n
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          • Data Strategy<\/li>\r\n \t
          • Data Management Strategy, including approach for each DCAM<\/span> component<\/li>\r\n \t
          • Data Management Business Case<\/li>\r\n \t
          • Data Management Accountability Matrix*<\/li>\r\n<\/ul>\r\n\r\n<\/div>\r\n<\/div> \r\n\r\n
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            1.1 Business Requirements for Data<\/h3>\r\n<\/div>\r\n
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            \r\n\r\nIn most organizations, ad-hoc data initiatives will not be enough to help the business achieve success. To achieve success, the data management organization needs to understand the high-level business requirements and associated data requirements. These requirements are critical to establish and prioritize data management initiatives. The data management initiatives must be aligned to the needs, priorities, and desired outcomes of the business stakeholders.\r\n\r\n\r\n<\/div>\r\n<\/div> \r\n\r\n
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            1.1.1 High-level Business and Data Requirements<\/h3>\r\n<\/div>\r\n\r\n
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            Description<\/h5>\r\nBusiness needs are identified by operating units and often reflect broader organizational priorities established by executive leadership. The Data Strategy and Data Management Strategy must address both enterprise-wide goals and the specific needs of operational teams.\r\n
            Objectives<\/h5>\r\n
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            • Document the key business and data needs across the organization and its critical operating units.<\/li>\r\n \t
            • Validate these inputs to ensure alignment and completeness<\/span>.<\/li>\r\n \t
            • Understand how different areas of the organization expect data to support their objectives.<\/li>\r\n<\/ul>\r\n\r\n
              Advice<\/h5>\nBusiness and data needs—including goals, challenges, and priorities—are gathered through discussions with stakeholders at all levels. This discovery process<\/span> should be iterative and balanced against real-world constraints such as budget and operational capacity.\n\nThe aim is not only to capture objectives but also to prioritize them based on dependencies, resource availability, and feasibility. Active stakeholder<\/span> involvement is essential to building commitment and ensuring that data management efforts directly support business outcomes. It reinforces the idea that defining data needs is a shared responsibility between business and data teams.\n\nThe analytics function<\/span> brings a distinct and valuable perspective. Their evolving tools and rapid pace of change may warrant more frequent updates than the traditional planning cycle. Be sure to consider both internally driven analytics and insights derived from external data sources.\n\nBuilding a shared understanding of business goals and data expectations is critical for long-term success. The Data Strategy and Data Management Strategy must be living documents—continuously informed by the evolving needs of both the organization and its operational units.\n\n
              Questions<\/h5>\r\n
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              • Are the business and data requirements documented?<\/li>\r\n \t
              • Are the business and data requirements captured from all applicable levels of the organization?<\/li>\r\n<\/ul>\r\n
                Artifacts<\/h5>\r\n
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                • Verified documentation of the high-level objectives, requirements, data requirements<\/li>\r\n \t
                • List of stakeholders and evidence of bi-directional communication<\/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 definition of business and data requirements exists.\r\n\r\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
                  \r\n\r\nNo formal definition of business and data requirements exists, but the need is recognized, and the development is being discussed.\r\n\r\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
                  \r\n\r\nThe formal business and data requirements are being developed.\r\n\r\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
                  \r\n\r\nThe formal business and data requirements definition is established and documented at the appropriate levels of detail in support of the various levels of an organization.\r\n\r\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
                  \r\n\r\nThe formal business and data requirements definition is validated and supported by appropriate stakeholders.\r\n\r\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
                  \r\n\r\nThe formal business and data requirements definition is established, reviewed, and updated regularly-\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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                  1.1.2 Prioritized Business and Data Requirements<\/h3>\r\n<\/div>\r\n\r\n
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                  Description<\/h5>\nThe prioritized enterprise<\/span> and operating units’ business and data requirements will inform the priorities of data initiatives.\n
                  Objectives<\/h5>\r\n
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                  • Secure stakeholder<\/span> review, prioritization and approval of the business and data requirements.<\/li>\r\n \t
                  • Establish regular requirements and review cycles to reflect changing business needs into data initiatives.<\/li>\r\n<\/ul>\r\n
                    Advice<\/h5>\nDefining the priorities of the business and data requirements is crucial for supporting the organization in achieving success with high-quality data and analytics. Stakeholders need to understand how the organization is addressing the correlation between priorities, funding, and operational realities. Discussions about funding can help define the priorities of both enterprise<\/span> and operating units. It is recommended that a formal prioritization and approval process<\/span> be established to provide input for developing the roadmap of data initiatives.\n\nAdditionally, it is important to define and illustrate how the organization will address new requirements or challenges as they arise. Collaborating with the business is essential for determining priorities. Additional insights can be garnered by involving all stakeholders such as the organization's Analytics function<\/span>(s). This group<\/span> can provide valuable perspectives on prioritizing business and data requirements by highlighting opportunities where data and advanced analytics methods and tools can offer a competitive advantage. Tools such as Artificial Intelligence and Machine Learning can drive improvements in efficiency, client experience, and the development of innovative products and services.\n\nIn today’s business and regulatory environment, it is equally important that organization consider the lens of legal and ethical use of data to the process<\/span> of prioritizing the business requirements. This early review will avoid investment in opportunities that carry legal and ethical risk.\n
                    Questions<\/h5>\r\n
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                    • Has the process<\/span> to prioritize and approve high-level business and data requirements been approved?<\/li>\r\n \t
                    • Do the priorities include dependencies?<\/li>\r\n \t
                    • Are the business priorities verified and aligned with data management initiatives, priorities, budget, technology, and operations?<\/li>\r\n<\/ul>\r\n
                      Artifacts<\/h5>\r\n
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                      • Prioritized list of data requirements<\/li>\r\n \t
                      • Defined and approved requirements processes<\/li>\r\n \t
                      • Data requirements verification and approval<\/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 definition of prioritized requirements exists.\r\n\r\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
                        \r\n\r\nNo formal definition of prioritized business requirements exists, but the need is recognized, and the development is being discussed.\r\n\r\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
                        \r\n\r\nThe formal prioritized business requirements are being developed.\r\n\r\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
                        \r\n\r\nThe formal prioritized business requirements are documented and validated by stakeholders.\r\n\r\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
                        \r\n\r\nThe formal prioritized business requirements are established and available for use by the organization. For example, used to drive the Data Strategy, Data Management Strategy and Data Management Business Cases.\r\n\r\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
                        \r\n\r\nThe formal prioritized business requirements are reviewed and updated regularly, (e.g. annually).\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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                        1.2 Strategy for Data<\/h3>\r\n<\/div>\r\n
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                        \n\nEstablishing the Data Strategy & Data Management Strategy is critical for developing Data Management initiatives. A robust Data Strategy & Data Management Strategy encompasses the entire organization and outlines the necessary elements for the data management initiative such as data requirements, data capabilities, and resources, to achieve the organization's strategy and objectives. These incorporate input from all stakeholders, both business and technical, and prioritizes their needs to enhance the overall benefits to the organization.\n\n\n\n<\/div>\r\n<\/div>\r\n\r\n
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                        1.2.1 Data Strategy<\/h3>\r\n<\/div>\r\n\r\n
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                        Description<\/h5>\r\nDefining the Data Strategy is a fundamental component of the Data Management Program. A comprehensive Data Strategy is established across the organization, outlining the data, capabilities, and resources required to support the organization’s strategic objectives. It incorporates input from all business and functional stakeholders, ensuring priorities are aligned to maximize organizational benefits. The Data Strategy should present a view into the future state of data for the organization\r\n
                        Objectives<\/h5>\r\n
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                        • Define the data that is required to achieve the prioritized business objectives.<\/li>\r\n \t
                        • Develop a data usage approach that addresses the innovative use of data and technologies and ensures that over-arching strategy of the organization is a primary focus.<\/li>\r\n \t
                        • Define a conceptual target state of data for the organization.<\/li>\r\n<\/ul>\r\n
                          Advice<\/h5>\r\nThe Data Strategy identifies the data content, sources and types of data, that are required to meet the prioritized objectives of the business. Sources can include both internal, external, structured and unstructured formats. The Data Strategy addresses the fundamental question of what data is needed to support business goals. However, the scope of the data must align with the capacity of the data management initiative responsible for handling it. This requires balancing business priorities with available resources. To support future needs, it is recommended that the data management organization maintains a backlog of lower-priority business objectives and associated data requirements.\r\n\r\nThe Data Strategy also defines how the organization intends to utilize data, emphasizing the adoption and integration of innovation to achieve business objectives. This includes leveraging existing data in new ways, incorporating new data sources, and utilizing advanced analytics technologies such as Artificial Intelligence. Additionally, the strategy must establish guidelines for the responsible and ethical use of data and new technologies, ensuring alignment with business goals while maintaining ethical data practices.\r\n\r\nAn organization must clearly understand how the future state of data management will align with and support its business objectives. Defining the future state of the data environment requires a comprehensive assessment of the current state to identify gaps that must be addressed. The Data Strategy should acknowledge these gaps and outline a high-level approach—based on business priorities and resource requirements (people, processes, and technology)—to achieve the desired future state. This establishes a long-term vision that ensures the organization understands how data management will contribute to its strategic goals and establishes a baseline for the data management strategy.\r\n
                          Questions<\/h5>\r\n