Introduction

The Data Management Strategy & Business Case determines how data management is defined, organized, funded, governed and embedded into the operations of the organization. It defines the long-term vision including a description of stakeholders or stakeholder functions that must be aligned. Data Management Strategy demonstrates the business value that the program will seek to achieve. It becomes the blueprint for the organization to evaluate, define, plan, measure and execute a successful and mature Data Management (DM) initiative.

The purpose of developing a DM strategy and business case is to articulate the rationale for the DM initiative. The strategy defines why the initiative is needed, as well as the goals, and expected benefits. The strategy also describes how to mobilize the organization in order to implement a successful DM initiative. The DM business case provides the rationale for the investment in the Data Management initiative. Data Management is no different than any other established business process. It needs to be justified, funded, measured and evaluated. It provides clarity of purpose, enabling agreement and support of initiative objectives from senior executives as well as program stakeholders.

Definition

The Data Management Strategy (DMS) & Business Case component is a set of capabilities to define, prioritize, organize, fund and govern data management and how it is embedded into the operations of the organization in alignment with the objectives and priorities of both the Enterprise and Operating Units. The DM business case is the justification for creating and funding a DM initiative. The business case articulates the major data and data related issues facing an organization or operating unit and describes the expected outcomes and benefits that can be achieved through the implementation of a successful DM initiative.

Scope

• Establish a DMS function within the Office of Data Management (ODM).
• Work with Data Management Program Management Office (PMO) to design and implement sustainable BAU processes and tools for the DMS function.
• Align the DMS with the business strategy, objectives, and priorities, including prioritization of data based on its criticality to the business.
• Define the rationale and business case for management of data as an asset through the organization-wide DM initiative.
• Ensure the DMS is aligned with the organization-wide Enterprise Data Management Principles.
• Articulate the DM target and current-state. Then, using DCAM as an assessment tool for gap analysis and prioritized gap closure, create a cohesive execution plan.
• Define the high-level execution roadmap.
• Define strategy execution risks and mitigations.
• Define Data Management performance metrics.
• Document the DMS with a compelling presentation of the value of an organization-wide DM initiative.
• Ensure that the DMS governance is integrated into Data Governance.

Value Proposition

Organizations that have Enterprise and Operating Unit executives who understand, support, and offer direction for the organization-wide DM initiative lower organizational risk and get better acceptance of the DM Initiative at all levels of staff. Staff engagement in the sustainable management of data for the short and long-term success of the organization is essential. Organizations that implement effective data management get a return on investment from several areas:

• Efficiency and effectiveness of data issue resolution, compliance, and auditable demonstration
• Improved enterprise risk management
• Efficiency in business process optimization
• Innovation and differentiation for customers

Overview

The DMS component is one of the three foundational components of the DCAM Framework. The DMS is what integrates the strategies of each of the other components of the Framework into an overall strategy for the execution of the DM initiative. It is important to note that the DMS is a function, within the Office of Data Management in which strategic planning capabilities and skills reside. (You may refer to “Overview” in the “Data Management Program” section of this guide for detail on the structure of the Office of Data Management).

Using the DCAM Framework provides a structure for the DM initiative that includes the core principles of data management. It also helps stakeholders understand the value of data management as it relates to their Operating Units and strategic initiatives.

The strategy should align with the organization’s articulated Target Operating Model for executing data management with a roadmap and timeline to achieve the target. It is important for a strategy to compare the target-state to the current-state in order to show the organizational, functional, operational, and technological gaps and inefficiencies. The Strategy can then define, prioritize, and schedule gap closure. DCAM used as a capability assessment tool is fundamental to the analysis of gaps in each operating level and organizational unit.

The DMS integrates the Framework components at each operating level throughout the organization. For detail on the levels and context at which data management operates, refer to “Data Management Operating Levels” in the introductory section of this guide.

A strategy must be documented for each organizational unit at the various operating levels of the organization and work in concert with the other organizational units across the organization. Within a single organizational unit, each DCAM component has a unique input to the strategy that is then integrated with the other component input and prioritized in the final strategy for that organizational unit. These inputs must align with the Data Management Target Operating Model for the organization. The Target Operating Model defines the expected component and capability requirements for the operating level of the organizational unit.

Because not all organizational units will be at the same maturity in the design and execution of their DM initiative these strategies are specific to their business objectives, priorities, and identified data management inefficiencies and gaps.

The importance of the physical documentation should not be underestimated because the document is the primary internal marketing tool to drive understanding and support from all stakeholders at all levels of the organization.

The DMS includes the business case that describes how value will be realized from the data assets of an organization, through the collaboration of business, data, and technology.

Diagram 1.1: Data Asset Value Model

The DM business case is the cost-benefit realization of the set of activities and deliverables expected from the DM initiative. The DM business case answers the question: Why the firm is focusing on data management? This helps achieve alignment across the stakeholders. The business case helps management understand the costs, benefits, and risks associated with the evolution of the DM initiative. It is essential to link the business case with realistic strategic and tactical measurement criteria and align them with the long-term sequence plan for the DM initiative. This enables the organization to understand the total costs associated with implementation as well as maintenance of the DM initiative and helps ensure that it is sufficiently funded to meet both near and long-term objectives.

The DM business case articulates the benefits of data management, in alignment with the objectives defined, communicated and agreed upon in the DMS. It discusses the defensive benefits of the initiative including operational cost reduction, improved regulatory reporting, streamlined risk management, controlled data governance, improved data quality. It also highlights the offensive benefits of the initiative which include advanced analytics, improved customer service, innovative product development, increased revenues, improved market penetration.

In some cases, the best way to build support for the business case is through a demonstrative proof of concept or pilot project. In these instances, a specific pain point or high-profile business objective would be selected and used to demonstrate the benefits of implementing effective data management. If this approach is used, it is important to select a project that is achievable and can provide quick wins. This approach builds confidence among stakeholders on the foundational benefits of data management to ensure sustainability. Regardless of whether you define the business case with or without a proof of concept, all activities must align to the strategic business objectives of the organization.

The DM strategy and the business case are not static and must be able to evolve as the priorities and needs of the organization change. The most effective and successful data management strategies are living artifacts that are visibly endorsed by executive management and are supported by mandatory organizational policy.

Core Questions

• Does the DMS clearly articulate the reason and the importance of implementing the DM initiative at each level of the organization?
• Is there executive buy-in across business, operations and technology?
• Do stakeholders agree to support and sustain a DMS function?
• Has the DMS sufficiently defined the immediate, medium and long-term goals and objectives of the organization-wide DM initiative?
• Is the DMS in line with organizational priorities?
• Has the DMS effectively identified the critical areas of focus, including how priorities are established and verified?
• Has the DMS identified the operating model and required staffing resources needed to establish, lead and maintain the Data Management initiative?
• Is the Data Management Business Case aligned with the strategic goals of the organization?

Core Artifacts

The following are the core artifacts required to execute an effective Data Management Strategy & Business Case capability. Items with an ‘*’ link to published best practice guidelines

• Data Management and Business Strategy Alignment Matrix
• Data Management Accountability Matrix*
• Data Management Business Case
• DCAM Business Glossary*
• Data Management Framework
• Data Management Operating Model
• Data Management Strategy
• Data Risk Appetite Statement
• Stakeholder Analysis

First Capability

The DMS is a collaboration of business, data and technology stakeholders. The strategy must identify the high-level organizational objectives identified by the organization’s senior executives. The strategy will include the objectives of each of the components in the DCAM Framework that results in an integrated implementation roadmap. The authority for the DMS is from approval by the defined governance structure of the DM initiative. The authority is further enhanced by Internal Audit review of the enforceability of the DMS.

First Sub-Capability

Description

The DMS is a collaboration with the full spectrum of business, technology and operations management stakeholders. Together they document the DMS.

Objectives

• Align DMS with business, technology, and operations.
• Document DMS.
• Publish DMS to all stakeholders.

Advice

The DMS is both a statement of approach and a marketing document for presentation to stakeholders. Without a formally defined and collaborative statement of its strategy, the organization’s approach to DM can become a series of short-term, defensive reactions. Without collaboration, the strategy can be viewed as irrelevant to stakeholders.

Questions

• Have all the aspects of the DMS been defined and presented in terms meaningful to each operating level?
• Are the business, regulatory and operational rationales for the DM initiative defined and verified?
• Is the DMS aligned with business requirements, implementation plans, technical capabilities, and operational processes?
• Has the DMS been documented and published?
• Is the approach to DM clearly defined?
• Are stakeholders aligned on the specified approach?

Artifacts

• Vision statement of the target-state DM initiative and what will be achieved
• Definition of the foundational principles and illustration of why they are essential
• Business requirements and priorities; process for establishing and approving each
• Benefits – answers why we are doing this; articulates the value proposition and how it aligns to organizational principles
• Mapping of strategy to technical and operational capabilities
• List of stakeholders and evidence of bi-directional communication
• Distribution lists and approvals from stakeholders
• Evidence that the strategy published

Scoring

Next Sub-Capability

Description

High-level organizational objectives are those identified by executive management as organizational goals (e.g., the organizational objective is to improve customer support and services).

Objectives

• Fully map DMS and align it with the high-level organizational objectives.
• Secure approval of DMS by the executive committee and stakeholders.
• Define a process to ensure the future alignment of the DMS to organizational objectives.

Advice

The goal is to ensure that the DMS supports the current objectives of executive management. These high-level objectives need to be translated into requirements for data or DM and evaluated against gaps and inefficiencies, often called pain points, that currently exist within the organization. A DM initiative that is not synchronized with the high-level objectives of the organization can result in a misalignment of data priorities. Critically, this misalignment can lead to a perception of the DMS as irrelevant among executive management. Achieving executive buy-in is critical for the organization to adopt the DM initiative. With the right executive support the time required for the organization to embrace a data culture and adopt the DM initiative will reduce exponentially.

Questions

• Has the DMS been aligned and mapped to organizational objectives?
• Has the alignment been verified and approved by stakeholders and executive management?

Artifacts

• Mapping of organizational objectives to data concepts and from there to strategy, with verification from executive committee and stakeholders
• High-level roadmap on how the strategy will be implemented in alignment with business objectives
• List of stakeholders and evidence of bi-directional communication
• Distribution lists and approval documents from stakeholders

Scoring

Next Sub-Capability

Description

The DMS is a high-level aggregation of the strategies for implementation of the other Components of the DCAM Framework. It represents a prioritized balancing of business objectives and resources that results in an integrated implementation roadmap.

Objectives

• Include core concepts from each of the DCAM Framework Components in the DMS.
• Prioritize the core DCAM components based on their alignment to the business objectives.
• The defined outcomes reflect advancement of the current-state toward the target operating model.

Advice

A DMS may exist at each of the operating levels of an organization: enterprise; Region; operating units; and Domain. The DMS integrates the strategy for each DCAM Framework Component based on the business objectives of the operating level implementing it. Often, implementation may require alignment with the business objectives of a higher operating level or control function of the organization.

Core strategic concepts from each DCAM Framework Component should be identified in the DMS. The DMS should represent the business objectives of the current planning period against the DM target operating model. It should prioritize outcomes and resources across each of the DCAM Framework Components.

Questions

• Have the core strategic concepts of the DM Program & Funding Model been integrated into the DMS?
• Have the core strategic concepts of the Business & Technology Integration been integrated into the DMS?
• Have the core strategic concepts of the Business & Data Architecture been integrated into the DMS?
• Have the core strategic concepts of the Data Quality Management been integrated into the DMS?
• Have the core strategic concepts of the Data Governance been integrated into the DMS?
• Have the core strategic concepts of the Data Control Environment been integrated into the DMS?

Artifacts

• Evidence of content management with authorized data domains, critical data elements, taxonomies and ontology, identifiers, systems of record
• Verification of DM measured by program, outcome, quality and usage
• Evidence of implementation of DM for architectural principles, cross-functional collaboration, operational capabilities, incremental strategy
• Communication and training materials used for stakeholder education and socialization
• Evidence of governance Including organizational structure, policy, controls, stewardship, accountability, audit and enforcement
• List of stakeholders and evidence of bi-directional communication
• Verified mapping of strategy to technical and operational capabilities
• Evidence that the strategy was approved and published

Scoring

Next Sub-Capability

Description

Approval of the DMS requires a mechanism and a governance structure that provides authority for the Strategy.

Objectives

• Create a mechanism to capture feedback from stakeholders.
• Incorporate stakeholder feedback into the DMS.
• Have stakeholders review and approve the DMS.

Advice

An effective DMS needs buy-in from all the stakeholders within the organization because it has significant implications for the stakeholders responsible for operations. Stakeholders are more likely to buy into the DM initiative if they see evidence of their influence over the finished DMS. This buy-in typically makes implementation easier. DMS approval is best managed as an iterative process focused on the needs of business and balanced against the requirements for implementation.

Questions

• Is there a mechanism to obtain and verify feedback from stakeholders on the aspects of the DMS?
• Is there a mechanism to obtain and verify feedback from stakeholders on the implementation strategy?

Artifacts

• Approval process documentation that defines the mechanism of the process of approval
• List of stakeholders by function
• List of stakeholders and evidence of bi-directional communication
• Distribution lists and approvals from stakeholders

Scoring

Next Sub-Capability

Description

Enforceability of the DMS should be evaluated by the Internal Audit function of the organization.

Objectives

• Have Internal Audit review and approve the DMS.
• Have Internal Audit determine that implementation of the DMS can be enforced via their existing examinations.

Advice

Engagement with Internal Audit is an important way to ensure that the DM initiative is viable from an organizational point of view. This will typically require education of Internal Audit about DM concepts and principles. This early stage activity and the development of a partnership with Internal Audit can also help ensure their engagement in oversight as a priority. If the DMS can be audited, it becomes real in the eyes of both the organization and industry regulators. Internal Audit can be an important supporter of the implementation of a sustainable DM initiative.

Questions

• Is Internal Audit familiar with the concepts associated with DM?
• Has Internal Audit reviewed the DM initiative and determined that it can be audited via scheduled exams?

Artifacts

• Evidence of communication with Internal Audit about the concepts in the DMS
• Evidence of internal reviews and approval of the DMS
• Verification that the DMS can be enforced and audited

Scoring

Next Sub-Capability

The DMS must reflect the high-level business requirements of the organization. These requirements will inform the priorities of the DM initiative and the financial return on investment from managing data. The business stakeholders must validate the quantifiable business outcomes of the business case.

First Sub-Capability

Description

High-level business requirements are those identified by the operating units, often reflecting the high-level enterprise requirements identified by executive management. It is important that the DMS reflect both the organizational requirements as well as operating units’ requirements.

Objectives

• Document the high-level business requirements for the enterprise and critical operating units.
• Verify high-level business requirements for the enterprise and critical operating units and incorporate them into the DMS.

Advice

High-level business requirements such as objectives, goals, pain points and priorities are derived based on discussions with representatives from the operating units at all levels of the organization. The discovery process and verification of these requirements are done on an iterative basis and need to be balanced against operational reality as well as budgetary requirements. The goal is not only to define objectives and requirements but to prioritize them based on dependency, budget, and implementation reality. The objective of the business case is to achieve stakeholder buy-in for how the initiative will operate and how it will deliver against the objectives of the business. It drives home the fact that the benefits outweigh the costs.

A unique consumer of data that cuts across all operating units is the data analytic or data science team. It will be critical to obtain input for business requirements from the practitioners and executives of these teams. The methodologies and tools they deploy and the speed at which they are evolving may call for an ongoing routine more frequent than the annual planning cycle. Ensure that the scope of discussion covers both internal activities and data analytics that may be acquired externally.

The investment in a shared understanding of the business objectives that drives the DMS is essential for stakeholders to buy into the long-term view of the DM initiative. A DMS cannot be built in a vacuum. Instead, it must reflect the requirements of the enterprise and individual operating units.

Questions

• Have the business requirements been documented?
• Have the business requirements been captured from all applicable levels of the organization?

Artifacts

• Documentation of the high-level objectives, requirements and verification
• List of stakeholders and evidence of bi-directional communication

Scoring

Next Sub-Capability

Description

The prioritized enterprise and operating units’ business requirements will inform the priorities of the DM initiative.

Objectives

• Secure stakeholders review, prioritization and approval of the business requirements incorporated into the DMS.
• Establish regular requirements for review cycles.

Advice

Incorporation of business requirements into the DMS is based on defining the priorities of the high-level organizational objectives. Regulators, auditors and stakeholders will want to understand how the organization is addressing the correlation of priorities to both funding and operational realities. Discussions about funding can help define enterprise and operating units’ priorities. This prioritization and approval process will clarify what will and will not be done as part of the DM initiative. It is also important to define and illustrate how the organization will deal with new issues as they arise.

While the DM executive is not usually directly accountable for the advanced analytics function of the organization, they can bring valuable insight to the prioritization of the business requirements. This insight is based on highlighting opportunities where the data and the methods and tools of advanced analytics can present a competitive advantage. Tools such as Artificial Intelligence (AI) and Machine Learning (ML) can drive improvements in efficiency, client experience and innovative products and services.

In today’s marketplace and regulatory environment, it is equally important that the DM executive bring a lens of the legal and ethical use of data to the process of prioritizing the business requirements. This early review will avoid investment in opportunities that ultimately carry ethical risk.

Questions

• Has the process to prioritize and approve high-level business requirements been approved?
• Do the priorities include links and dependencies?
• Are the priorities verified and aligned with DM initiative priorities, budget, technology and operations?
• Is there a process for review and prioritization of new requirements?

Artifacts

• Prioritization process documentation
• Examples of requirements verification and approval

Scoring

Next Sub-Capability

Description

The DM business case must align with and reflect the enterprise and operating units level objectives, drivers and requirements as detailed in the DMS.

Objectives

• Map the DM Business Case and align it with organizational priorities and objectives.
• Align the DM Business Case with the strategic enterprise and operating units’ priorities and objectives.
• Include a review of the DM Business Case business requirements to ensure they follow ethical data practices.

Advice

The DM Business Case is the justification for creating and funding a DM initiative. The DM Business Case answers the why questions and addresses the so what challenges. It articulates the major data and data related issues facing the organization and describes the expected outcomes and benefits that can be achieved through the implementation of a successful DM initiative.

The business case can incorporate defensive objectives related to regulation, risk and compliance. This defensive objective must include an ethical review of data practices. The risk of unethical data practices should be evaluated as part of establishing the business case that the potential of reputational risk may outweigh the business value of the proposed use of data. The practices employed to manage data that must hold up to ethical scrutiny.

It should also address offensive objectives such as business enablement, analytics and operational efficiencies. Core objectives must be defined. Implementation approaches must be articulated. The value propositions need to be clearly stated in ways that are meaningful to stakeholders.

Questions

• Does the justification of the business case align with the DMS?
• Are the objectives defined and verified?
• Are the value propositions clearly specified and addressed to the stakeholders?
• Is the data in scope to the business requirements and ethical use of the data?

Artifacts

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

Scoring

Next Sub-Capability

Description

A primary function of the business case is to define the challenges of the current-state and to define the roadmap to improvement.

Objectives

• Define and sequence expected outcomes.
• Define and articulate the current-state to target-state.

Advice

In order to develop the roadmap for current-state to target-state advancement, the dependencies between intermediate steps need to be identified so the right sequence can be defined. Because of the scope of DM, issues need to be prioritized and sequenced. This process needs to be formal and transparent to avoid confusion and manage expectations. Strong communication about priorities, sequences, and dependencies is essential.

Questions

• Have data access and delivery dependencies been defined and verified?
• Have critical DM concepts been prioritized, sequenced and verified to align with business outcomes?
• Is there a communication strategy in place to provide visibility and transparency to stakeholders?

Artifacts

• Definitions of business outcomes
• Documentation of sequence plans and schedules
• Evidence of stakeholder communication through feedback on priorities

Scoring

Next Sub-Capability

Description

Buy-in to the business case is affirmed by stakeholder validation of the quantifiable business outcomes.

Objectives

• Socialize the DM Business Case to stakeholders.
• Review and validate the business objectives.
• Review and approve outcomes, benefits, timelines and target thresholds.

Advice

Stakeholders in enterprise and operating units level management must review the business case and validate both objectives and approaches. This process needs to be formalized and ongoing, as new priorities are introduced, and existing ones are completed.

Questions

• Has the business case been socialized to the stakeholders?
• Have the target objectives been reviewed and verified?
• Have outcomes, benefits, timelines, and thresholds been approved?

Artifacts

• Show mechanism for verification and validation by stakeholders
• List of stakeholders and evidence of bi-directional communication
• Distribution lists and approvals from stakeholders

Scoring

Next Sub-Capability

Defining the data management vision includes: 1) a data content strategy identifying what data is required; 2) a data usage strategy for how the data will be used; and 3) a DM deployment strategy that aligns the business objectives to a prioritized execution road-map.

First Sub-Capability

Description

The data content strategy identifies the sources and types of data that are required to meet the prioritized objectives of the business. Sources include both internal and external data.

Objectives

• Define the data that is required to achieve the prioritized business objectives.
• Maintain a backlog of lower priority business objectives and required data for future consideration.

Advice

The data content strategy answers the question of what data is required to support the objectives of the business. The data in scope cannot exceed the capacity of the DM initiative responsible for managing the data. This becomes an exercise of right sizing the priorities of the business to the resources that are provided to the DM initiative.

The types of data include reference, transactional, derived and alternative. Types of alternative data may include geospatial, satellite, mobile, sensor, weather, and social media. All data must be viewed through the lens of ethical access and use.

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 you captured a backlog of business objectives and required data for future consideration?

Artifacts

• Data content strategy included in the DM initiative strategy document
• Definition of the in-scope data aligned to business objectives
• Evidence of review of external data sources

Scoring

Next Sub-Capability

Description

The data usage strategy defines how the organization intends to use data. The strategy includes the vision for the adoption and use of advanced analytics methods and tools like Machine Learning (ML), Artificial Intelligence (AI) and cognitive thinking. The strategy must identify the appropriate use of innovation within the constraints of achieving ethical data outcomes.

Objectives

• Develop a data usage strategy that addresses the vision for the use of advanced analytics methods and tools.
• Ensure that the data usage strategy addresses the vision of the organization in the innovative use and maintenance of data.
• Create a data usage strategy that includes a review of the ethical use and outcome of the data being used.

Advice

The data usage strategy is an extension of the data content strategy. The challenge is to combine the basic requirements for data from the business objectives to explore the organization’s vision for using advanced analytics methods and tools. Advanced analytics can lead to the discovery of opportunities for innovation or efficiencies not previously possible. However, applying the automation and efficiency from these innovative tools to the analysis of data exposes an organization to increased risk from the misuse of data. The data access and use are not simply a legal question but also an ethical question.

The DM executive should provide insight to how these evolving methods and tools can be used to advance the objectives of the business. As part of evaluating these new capabilities a review of the ethical use and–the ethical outcome of the use–of the data is required.

Questions

• 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 process execution of the DM initiative?
• Is it ethical to use the defined data?
• Is the way the data is being used producing an ethical outcome?

Artifacts

• Data usage strategy included in the DM initiative strategy document
• Evidence of the ethical review of the in-scope data

Scoring

Next Sub-Capability

Description

The Data Management Deployment Strategy defines the prioritized business objectives and their requirements for DM. The strategy includes a vision of the DM initiative target-state, current-state gap analysis, prioritized roadmap and execution resource requirements.

Objectives

Define the business requirements for data and DM capability.

Evaluate the DM initiative current-state against the target-state with gap analysis and a gap closure roadmap.

Define the required execution resources.

Define the business case for the DM initiative.

Advice

The DM deployment strategy answers the question of what DM is going to be applied to the in-scope data defined in the data content strategy. A solid business case is a critical part of the deployment strategy to validate the value delivered from the investment in the DM initiative.

Questions

• Is the target-state vision of the DM initiative defined?
• Has an assessment of the current-state been completed?
• Have current-state-to-target-state gaps been quantified and prioritized?
• Is there a defined current-state gap closure roadmap in place?

Artifacts

• Data management strategy included in the DM initiative strategy document
• Current-state gap closure roadmap
• Funding and resource analysis
• Presentation of the business case

Scoring