Introduction

Le Data Strategy, Data Management Strategy & Data Management Business Case 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. The 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 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 processus. This methodology fosters clear objectives, mutual agreement, and backing from senior executives and various stakeholders.

Diagram 1.1: Business Strategy supported by Data Management

Definitions

Data Strategy 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. Data Management Business Case 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

Champ d'application

• Établir et maintenir une stratégie de données documentée, spécifiant le contenu et l'utilisation des données, ainsi que la stratégie de gestion des données nécessaire pour soutenir l'entreprise, en s'appuyant sur les éléments suivants DCAM d'articuler les capacités de gestion des données.
• Aligner la stratégie des données et la stratégie de gestion des données sur la stratégie, les objectifs et les priorités de l'entreprise, y compris la hiérarchisation des données en fonction de leur criticité pour l'entreprise.
• S'engager avec les principales parties prenantes à articuler et à définir l'état actuel et l'objectif futur des capacités de gestion des données à l'aide d'un outil d'évaluation tel que DCAM.
• Hiérarchiser les objectifs de la stratégie de gestion des données et établir une feuille de route et un calendrier de mise en œuvre.
• Établir la justification et la logique commerciale de la gestion des données par le biais de l'initiative globale de gestion des données de l'organisation. Les analyses de rentabilité doivent mettre en évidence la valeur des données et préconiser l'investissement dans les capacités de gestion des données, qu'il s'agisse de projets d'amélioration ou d'opérations et de pratiques permanentes.

Proposition de valeur

Organizations that have entreprise et unité opérationnelle 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:

• Revenue growth and client satisfaction
• Improve operations and efficiency
• Stronger risk management and regulatory compliance
• Faster and easier digital transformation and innovation

In 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 terme, rather than focusing only on near terme 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.

Vue d'ensemble

Data Strategy, Data Management Strategy and Data Management Business Case are foundational components of the DCAM 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. Using 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. The Data and Data Management Strategies should align with the organization's modèle de fonctionnement and include a roadmap and timeline to reach the target state. If the modèle de fonctionnement changes, the strategies must be updated accordingly. Effective 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. Each operational unit must have a documented Data and Data Management Strategy that aligns with the enterprise-wide modèle opérationnel cible. 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 modèle opérationnel cible. Because 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. The 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 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. The importance of physical documentation is essential for effective data management and offers numerous benefits to an organization. Here are some key advantages:

1. Enhanced Communication: 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.
2. Executive Endorsement: Effective documentation is visibly endorsed by executive management and stakeholders, which fosters a culture of support and commitment towards the strategy.
3. Sustainability: 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.
4. Multi-Channel Presence: 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.
5. Politique Support: Successful strategies are often supported by mandatory organizational policies which are clearly outlined in high-quality documentation.

Questions fondamentales

• Are the Data Strategy, Data Management Strategy and Data Management Business Case documented and aligned with the organization’s business strategy and priorities?
• Does the Data Management Strategy clearly articulate the reason and the importance of implementing data management capabilities at various, appropriate levels of the organization?
• Is there executive buy-in across operations, and technology?
• Do stakeholders agree to support and sustain data management functions?
• 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?
• 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?
• Has the Data Management Strategy identified the modèle de fonctionnement and resources required to establish, maintain, and improve data management both through initiatives and ongoing operations?
• Does the organization review the Data Strategy, Data Management Strategy, and Data Management Business Case on a routine basis, especially as the business changes?

Artefacts de base

Les éléments suivants sont les suivants artefacts de base required to execute an effective capability. Items with an * link to the EDM Association’s published practice guidelines.

• Data Strategy
• Data Management Strategy, including approach for each DCAM component
• Data Management Business Case
• Data Management Accountability Matrix*

In 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.

Description

Business 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.

Objectifs

• Document the key business and data needs across the organization and its critical operating units.
• Validate these inputs to ensure alignment and exhaustivité.
• Understand how different areas of the organization expect data to support their objectives.

Conseil

Business and data needs—including goals, challenges, and priorities—are gathered through discussions with stakeholders at all levels. This discovery processus should be iterative and balanced against real-world constraints such as budget and operational capacity. The aim is not only to capture objectives but also to prioritize them based on dependencies, resource availability, and feasibility. Active partie prenante 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. The analytics fonction 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. Building 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.

Questions

• Are the business and data requirements documented?
• Are the business and data requirements captured from all applicable levels of the organization?

Artéfacts

• Verified documentation of the high-level objectives, requirements, data requirements
• Liste des parties prenantes et preuves d'une communication bidirectionnelle

Notation

Description

The prioritized entreprise and operating units’ business and data requirements will inform the priorities of data initiatives.

Objectifs

• Secure partie prenante review, prioritization and approval of the business and data requirements.
• Establish regular requirements and review cycles to reflect changing business needs into data initiatives.

Conseil

Defining 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 entreprise and operating units. It is recommended that a formal prioritization and approval processus be established to provide input for developing the roadmap of data initiatives. Additionally, 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 fonction(s). This groupe 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. In today’s business and regulatory environment, it is equally important that organization consider the lens of legal and ethical use of data to the processus of prioritizing the business requirements. This early review will avoid investment in opportunities that carry legal and ethical risk.

Questions

• Est-ce que le processus to prioritize and approve high-level business and data requirements been approved?
• Do the priorities include dependencies?
• Are the business priorities verified and aligned with data management initiatives, priorities, budget, technology, and operations?

Artéfacts

• Prioritized list of data requirements
• Defined and approved requirements processes
• Data requirements verification and approval

Notation

Establishing 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.

Description

Defining 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

Objectifs

• Define the data that is required to achieve the prioritized business objectives.
• 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.
• Define a conceptual target state of data for the organization.

Conseil

The 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. The 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. An 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.

Questions

• Have you defined the data required to meet the prioritized business objectives?
• Have you evaluated the potential for externally sourcing the required data?
• Have the application and value of using advanced analytics been evaluated by the organization?
• Have the methods and tools of advanced analytics been evaluated for supporting the processus execution of the data management initiative?
• Is it ethical to use the defined data?
• Is the way the data is being used producing an ethical outcome?
• Has an assessment of the current state been completed?
• Is the target-state of the data initiative defined?
• Have current-state-to-target-state gaps been quantified and prioritized?
• Is there a defined current-state gap identified?

Artéfacts

• Data Strategy
• List of business objectives aligned with associated data
• Evidence of review of external data sources
• Current-state gap analysis

Notation

Description

The Data Management Strategy is a collaboration with the full spectrum of business, technology and operations management stakeholders. Together they document the Data Management Strategy. The Data Management Strategy focuses on how to best apply the data management functions to deliver data and information to the organization in support of business objectives.

Objectifs

• Align the Data Management Strategy with business, technology, and operations leveraging the Data Strategy.
• Document, review and gain approval for the Data Management Strategy from all stakeholders.
• Core strategic concepts from each DCAM Framework Component should be identified in the Data Management Strategy.

Conseil

The Data Management Strategy is a collaborative result of business, data and technology stakeholders. The Data Management Strategy should be informed by elements identified and prioritized in the business requirements and the Data Strategy. The Data Management Strategy should include the objectives and core concepts from each of the components in the DCAM Framework and the priorities from the Data Strategy that result in an integrated implementation roadmap. As business objectives evolve and require adjustment to the Data Strategy, the Data Management Strategy should also incorporate necessary updates. The Data Management Strategy, along with the Data Strategy, is a statement of approach and a guiding document of how an organization will build out their Data Management capabilities. The goal of a formally defined and collaborative statement of Data Strategy and Data Management Strategy is to support the current prioritized business strategic objectives. The high-level objectives need to be translated into requirements for data and data management. A Data Management Strategy that is not synchronized with the high-level objectives of the organization through the Data Strategy can result in a misalignment of data priorities. Critically, this misalignment can lead to a perception of the Data Management Strategy as irrelevant among executive management. Achieving executive buy-in is critical for the organization to adopt the data management initiative. With effective executive support, the time needed for the organization to cultivate a data-driven culture and implement the data management initiative will decrease significantly. The Data Management Strategy should represent the business objectives of the current planning period against the data management modèle opérationnel cible. It should prioritize outcomes and resources across each of the DCAM Framework Components.

Questions

• Have all the aspects of the Data Management Strategy been defined and presented in terms meaningful to each operating level?
• Is the business, regulatory and operational rationale for the data management initiative defined and verified?
• Is the Data Management Strategy aligned with the Data Strategy, technical capabilities, and operational processes?
• Is the Data Management Strategy documented, approved and published?
• Is the approach to Data Management clearly defined in the strategy?
• Have the core strategic concepts of the DCAM components been integrated into the Data Management Strategy?

Artéfacts

• Statement of the target-state data management initiative and what will be achieved
• Definition of the foundational principles and illustration of why they are essential
• Data Management Strategy reflective of the Data Strategy and business objectives and requirements
• Benefits – answers why we are doing this; articulates the value proposition and how it aligns to organizational principles
• Liste des parties prenantes et preuves d'une communication bidirectionnelle
• Distribution lists and approvals from stakeholders

Notation

The business case for data management must be developed to quantify the financial return on investment and strategic value from managing data. The business case must also reflect the high-level requirements and priorities of the organization necessary to achieve business results. The business case helps to substantiate the value of data management and help facilitate broad cross-organizational adoption. The business stakeholders must validate the quantifiable business outcomes of the business case for data management. As business strategies and objectives evolve, it becomes essential to align the data management program with the shifting business landscape and revise the Data Management Business Case as needed. The updated Data Management Business Case should showcase previous achievements and illustrate how data management will continue to drive business success. For example, consider how the advancements in technology are influencing change and how the data management program can support evolution.

Description

The Data Management Business Case must align with the objectives, drivers, and requirements at both the entreprise et unité opérationnelle levels. It should illustrate how the Data Strategy and Data Management Strategy work together to facilitate the achievement of business goals by clearly outlining the intended outcomes and return-on-investment for the data management capabilities.

Objectifs

• Create the Data Management Business Case based on organizational priorities and objectives.
• Document the financial value (e.g., ROI) for all prioritized and approved business requirements.
• Document the alignment to prioritized business requirements.

Conseil

The Data Management Business Case justifies the creation and funding of a data management initiative by addressing the major data and data-related issues facing the organization. It explains the expected outcomes and benefits of effective data management, integrating both defensive objectives (regulation, risk, compliance, and ethics) and offensive objectives (business enablement, analytics, and operational efficiency). Core objectives and implementation strategies must be clearly defined, and value propositions should resonate with stakeholders. Recognizing and harnessing the value of data is crucial, and understanding data value helps prioritize data initiatives. The EDM Association's Data ROI Working Groupe has published several important documents, including the Data Office ROI Playbook. This publication offers valuable insights into the significance of data and delineates four primary categories of data value business cases: increasing revenues, reducing costs, enhancing cash flows, and managing risk. These factors are crucial when evaluating the value of data within an organization.

Questions

• Is the business case based on the business requirements and aligned with the Data Strategy & Data Management Strategy?
• Are the objectives defined and verified?
• Are the value propositions clearly specified and addressed to the stakeholders?
• Are business case ROI assumptions and calculation methodologies consistent with industry’s best practices?
• Are the business case ROI assumptions and calculation methodologies documented?

Artéfacts

• Business case documentation
• Evidence of alignment between business case, strategy, organizational objectives and priorities
• Evidence of ethical data practices review and related risks

Notation