Introduction

The Data Management Program is an organizational function dedicated to the management of data as an asset throughout an organization. It is important to note that the Data Management Program is an organization, or function, within the Data Management Organization in which typical Program/Project Management Office capabilities and skills reside. It illustrates how the management of key data activities and the related data quality support strategic, business, and operational objectives. It also reinforces the necessity of orchestration, active collaboration, and alignment among diverse stakeholders to instill confidence in data as a trusted factor of input into business and operational processes. The purpose of a data management program is to organize and embed the Data Management concepts into the operational framework of an organization on a sustainable basis. The creation and implementation of the Data Management program elevates the importance of Data Management and integrates it as a core aspect of organizational operations. It establishes Data Management as a sustainable activity by ensuring sustainable funding and resources. It reinforces the importance of managing data across the organization via education, training, and communication and prepares for challenges, by committing to activities that drive effective change and alignment with organizational goals. The Funding Model describes the overall funding approach and the expectations for high-level engagement of senior management used to ensure that the objectives and processes of Data Management become a sustainably funded activity within the organization.

Definition

The Data Management Program & Funding component is a set of capabilities to manage the Data Management Organization. These organizational structures include resource requirements and a full range of Program Management Office activities such as execution of program management, stakeholder management, funding management, change activities, communications, training, and performance measurement. The Funding Model within the Data Management Program is designed to provide the mechanism to ensure the allocation of sufficient capital needed for program implementation, its long-term success and sustained and adoption. It also defines and describes the methodologies used to measure both the costs and the organization-wide benefits derived from the Data Management initiative.

Scope

• Establish a Data Management Program function to implement the Program Management Office capabilities within the Data Management Organization.
• Facilitate the design and implementation of sustainable business-as-usual Data Management processes and tools across the components and their capabilities.
• Establish roles and responsibilities related to the Data Management capabilities that align with the organizational structure and execute in a Data Management Organization.
• Define the Funding Model, secure and monitor funding, and institute cost and benefits tracking aligned to the Business Case.
• Establish the Data Management execution roadmap with supporting project plans to build upon the high-level Data Management Strategy roadmap.
• Engage each stakeholder across the data ecosystem as appropriate to their roles in resource alignment, funding, communications, training and skill development.
• Manage the Data Management initiative by monitoring and socializing Data Management performance metrics.
• Ensure that the Data Management Program governance is integrated into Data Governance structure and process.
• Identify organizational roles and responsibilities and develop engagement activities to support data management strategies/objectives.
• Develop Data Management communication and change management abilities to affect organization-wide behavior and cultural change.

Value Proposition

Organizations that ensure the right people are involved in the Data Management initiative demonstrate the ability to eliminate the replication and misuse of data and improve their ability to integrate data based on enterprise standards. Organizations that define and follow set processes and standard operating procedures for requesting, sharing, defining, producing, and using data, demonstrate the ability to ensure the sharing of data aligns with enterprise standards. A Funding Model with an appropriate, sustainable funding framework is required to ensure the success of the objectives and processes of the Data Management initiative. Organizations that effectively implement the Data Management Program and achieve an accountable Funding Model can improve return on investment in several areas:

• Establishes an organization-wide engaged data culture
• Boosting operational efficiencies through repeatable and sustainable processes
• Enhancing productivity by aligning resources and skills
• Ensuring continuity of funding over time
• Sustainability for the Data Management initiative
• Maintaining ongoing engagement from essential stakeholders

Overview

The Data Management Program is one of the two foundational components of the DCAM Framework that are necessary to provide the business with the data required to support and achieve their business objectives. It should be established as a formal, independent and sustainable part of the organization within the Data Management Organization. An effectively designed Data Management Program, that is flexible enough to accommodate changing circumstances, will help embed the importance of Data Management into the culture of the organization. A key goal is to instill a sense of collective respect for the role of data among all stakeholders. The Data Management Program must ensure the Data Management Organization has access to the appropriate staff resources and functional capabilities to deliver the data needed to support organizational objectives. An effective Data Management Program has:

• Strong support from executive management
• Appropriate governance authority to ensure the implementation of a data control environment
• A well-structured model of how stakeholders will engage in data-related issues

The Data Management Program establishes the lines of responsibility and accountability within the Data Management Organization structure and into federated organizational structures (e.g., operating units, regions). The Data Management Program function organizes Data Management activity throughout the organization. The Data Management Program function will operate in a similar way to a typical program or project, but the Data Management Program must be an ongoing, sustainable business-as-usual organization-wide commitment that organizes Data Management for the long term.

Diagram 2.1: Data Management Program in Data Management Organization

Diagram 2.2: Enterprise Data Management Program with Operating Unit Data Management Programs

The Funding Model defines the mechanism used to generate and maintain the capital needed for the Data Management initiative throughout its life cycle. It establishes the methodology used for cost allocation among business lines and can be used to help align stakeholders on funding-related issues. In mature organizations, the Funding Model reflects the individual requirements of the various Data Management initiative components of the organization and is integrated with governance to ensure that appropriate oversight and accountability is applied to Data Management. Verifiable metrics are essential and must be aligned with tangible business objectives. A well-structured Funding Model can help avoid recurring debates over business priorities, mitigate internal competition and facilitate open discussions among stakeholders. Strong consideration should be given to allocating initial funding to the Data Management initiative as an enterprise level expenditure rather than an individual organizational unit approach. This type of grassroots funding can become mired in competition among organizational units, is often aligned with a tactical view of Data Management and frequently reinforces short-term evaluation cycles. An organization can expect its Funding Model to evolve along with the maturity of its Data Management initiative. There is no single model for funding Data Management. The specific model implemented will depend on the dynamics and operational culture of the individual firm. Some organizations will fund centrally, others will fund through the organizational units, while still others may take a hybrid approach. There are pros and cons to all these approaches. Whichever is selected, the core aspects of the Funding Model, such as investment criteria/priorities, budget management, delivered-versus-expected benefits, allocation methodology and capital needed for ongoing management of the initiative should always be included. Most importantly, the Funding Model must reflect a multi-year journey, incorporating both initial implementation costs, as well as sustainable ongoing funding. Data Management must become a day-to-day operation and must be funded accordingly to ensure it becomes part of the fabric of an organization’s operation.

Core Questions

• Is the Data Management Program aligned with the Data Management strategy and business objectives?
• Does the Data Management Program have the right mixture of skills, resources, and capabilities to effectively implement and govern the Data Management function?
• Does the Data Management Program have the appropriate support from executive management?
• Is the Funding Model sufficient to sustainably support the Data Management program?
• Have the funding requirements been translated from the business case and aligned with the objectives, sequence priorities and high-level implementation roadmap of the Data Management strategy?
• Does the Funding Model cover all aspects of Data Management including tangible, intangible, special requests, urgent/crisis requirements, unique applications?
• Is there a defined process with established criteria for determining and verifying the investment required for Data Management, and is it aligned with the business structure, priorities and governance process organization?
• Has change approach been considered and are incentives in place to help make the change stick?
• Is a plan in place to continuously check, monitor and react to measures of success?

Core Artifacts

The following are the core artifacts required to execute an effective Data Management Program and Funding Model capability. Items with an ‘*’ link to published best practice guidelines.

• Communications Plan
• Change & Enablement Plan
• Data Management Metrics & Dashboards
• Data Management Organizational Structure*
• Funding Model
• Stakeholder Engagement Plan

The Data Management Organization comprises the team responsible for promoting the Data Management initiative at the various operating levels of the organization. A senior executive data officer at each organization's operating level must be appointed, given authority, and allowed to appropriately staff the Data Management Organization.

Description

The Data Management Organization includes the organizational team responsible for promoting the Data Management initiative at the various operating levels of the organization.

Objectives

• Design and plan the Data Management Organization.
• Charter and approve the Data Management Organization.
• Create the Data Management Organization.

Advice

The Data Management Organization formalizes and runs the Data Management Program. The Data Management Organization needs a visible and strong commitment from executive management. A formal office is necessary to create policy, implement standards, coordinate governance, and manage organizational collaboration across control functions. The data management organization can be evolutionary and grow as the business needs change and expand and the organization matures in data management practices. The approach for the data management organization should reflect this evolutionary process.

Questions

• Is there a formal and sanctioned Data Management Organization?
• Is it recognized as part of the official corporate structure?
• Does it have a clear mission and charter?
• Does the Data Management Organization have strong and visible executive support?
• Does the Data Management Organization have sufficient funding and the skill sets needed to accomplish the Data Management objective?
• Is there a near-term and long-term plan for the growth of the data management organization?
• How will the data management organization manage resources needs during the ebbs and flows of business cycles?

Artifacts

• Data Management charter and approvals
• Specific and identifiable organizational structure and plans for implementation and management
• Formal communication from executive management such as any notifications to stakeholders
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

A senior executive data officer, for example a Chief Data Officer or an operating unit data officer must be appointed and be given full authority to run the Data Management Organization. The role and scope of responsibility for this position must be clearly defined and communicated to the organization.

Objectives

• Establish an understanding of the need for an authoritative data officer.
• Define the role and responsibility of the data officer.
• Communicate the duties and authority of the data officer to stakeholders.

Advice

It is essential that a single, high-level executive be responsible for the Data Management initiative. A committee cannot run the Data Management initiative. To ensure that the Data Management initiative is sustainable, a senior executive with authority must be appointed and must have broad support from other executives. The executive in charge needs to be the primary advocate for Data Management, communicating with vision and passion. This executive is the chief diplomat for collaboration as well as the person who runs the Data Management initiative. Simply appointing the executive is not sufficient – the role and authority necessary to implement a change of this magnitude must be communicated to all stakeholders.

Questions

• Has the remit of the data officer been defined, socialized and documented?
• Has a senior executive been hired to run the Data Management Program?
• Has the executive been empowered with the authority necessary to implement the Data Management initiative?
• Have the lines of authority for the data officer been defined and established?
• Has the role of the Data Management Program and the data officer been sanctioned and communicated to stakeholders?

Artifacts

• Data officer job definition including skills and expectations
• Named individual performing the Data Management executive function
• Executive management communication to stakeholders
• List of stakeholders for communication about data officer

Scoring

Description

The Data Management Organization must be appropriately funded and staffed with the required Data Management skill sets.

Objectives

• Establish the Funding Model to support the Data Management Organization.
• Gain approval to hire skilled personnel as required by the Data Management Organization model.

Advice

Be wary of Data Management Programs that are approved but not given the authority to hire or acquire essential operational talent. It is not necessary for all the tasks associated with Data Management to be the sole tasks of the personnel responsible for them. Some staff may be shared with other business units where they have unrelated functions and responsibilities. Whether it exists as a stand-alone group or whether many of the operational functions are embedded in the business is dependent on the strategy and culture of the organization. Regardless, the Data Management Program needs dedicated resources with appropriate skill sets and at least a small, dedicated, central coordination function. Finding the right people requires ramp-up time and contingency plans. Capabilities to manage inevitable turnover should be designed to avoid single points of failure and prevent operational bottlenecks are essential. The scope of the Data Management initiative continues to evolve. As new issues, opportunities or regulations emerge be mindful of the potential need for additional skills across the stakeholder teams. An example of this would be the skills needed to support Artificial Intelligence, the advanced analytics tools and methodologies and the inherent challenges of integrating data ethics into the governance and operations of the data.

Questions

• Is the operating model for the Data Management Organization established?
• Are the resources needed to support the Data Management initiative defined and acquired?
• Does the Data Management Organization have the authority to hire, or approval to acquire, the skill sets needed for implementation?
• Are all the required skills represented across the stakeholder teams?
• Has ramp-up time for staff onboarding and funding commitment been anticipated?

Artifacts

• Operating model with a resource plan for the Data Management Organization
• Job descriptions for the defined organizational structure
• Gap analysis of skills needed
• List of required skills
• Confirmation of approved budget and authorization to hire

Scoring

Description

The enforceability of the Funding Model is achieved by aligning the resources of the Data Management Organization and program management with the organization's commitment, support and involvement in the ongoing data management activities. Aligning current available resources for Data Management execution will also help uncover additional resource requirements and opportunities.

Objectives

• Confirm resource alignments for data management initiatives with business stakeholders.
• Identify resource needs and opportunities.
• Empower the Data Management organization to effectively align and support the enterprise and operating units in accordance with its objectives based upon funding plan.

Advice

Funding is required to support the Data Management Organization and the associated resource needs. It is important to have alignment among the stakeholders across the organization to fully resource Data Management initiatives. Alignment on funding and resources can come from a centralized seed funding approach or it can come from enterprise and/or operating units. Regardless of the source – there needs to be evidence of sustainable financial support to properly resource a Data Management Program.

Questions

• Is the funding plan and associate resource needs aligned, documented and verified across stakeholders and Data Management?
• Are resource needs, including gaps & opportunities, identified and confirmed?
• How will the Data Management Organization handle resource management in case of budget reductions or other funding challenges?
• What is the process for prioritizing both discretionary and non-discretionary resource decisions?

Artifacts

• Data Management Resource Plan
• Communication with stakeholders about resource alignment
• Escalation procedures to resolve budget/resource shortfalls or diversions

Scoring

The Data Management Program provides the required program and project management for the Data Management Organization. The approach and plan must be defined and approved by stakeholders. Roles and responsibilities across the stakeholders must be established with operational processes in place.

Description

The approach and plan must be defined for the Data Management Program. Once established, it must be formally empowered by senior management and its role communicated to all stakeholders.

Objectives

• Formally establish the Data Management Program approach within the organization.
• Communicate the role of the Data Management Program function across the organization through approved organizational channels.
• Secure authority to enforce Data Management Program compliance through policy and documented procedure.

Advice

The Data Management Organization, and the Data Management Program within it, should be established as independent entities. Formalization is essential. Be careful about embedding the Data Management Organization in the technology function as it is essential to keep the Data Management focus on business priorities with oversight of data-related IT investments. The creation of a new control function needs broad-based support and a clear announcement from executive management to minimize inevitable disruption. Creating the Data Management Organization without empowerment is useless. As a change function, the Data Management Program needs authority to enforce behavioral change. The authority granted must be formal. The success of a Data Management Program heavily relies on the visible and active support of executive leadership. This support must be clearly communicated and demonstrated through the alignment of resources to address recognized business challenges. It is important the Data Management Organization/Data Management Program is seen as a key contributor to solving business challenges, rather than merely adding compliance overhead. Effective communication of executive support helps to position the Data Management Organization and Data Management Program as integral parts of the solution. Additionally, the approach and plan must be tailored to the organizational culture to ensure alignment and acceptance.

Questions

• Has the Data Management Program been formally established?
• Is there a Data Management Program approach and plan in place?
• Has the Data Management Organization and Data Management Program been formally communicated to business, technology, operations, finance and other risk stakeholders?
• How has executive management demonstrated its support?
• Has authority been granted to the Data Management Program function to implement and enforce best practice via policy and standards?
• Has authority been communicated to stakeholders?
• Is there a functional partnership in place with Internal Audit?

Artifacts

• The Data Management Program plan
• Description of roles and responsibilities of the Data Management Program
• Communication of specific support from executive management with distribution list
• Policies and procedures associated with executing and enforcing Data Management Program
• Bi-directional engagement with stakeholders on the Data Management Program authority

Scoring

Description

The Data Management Organization will require the coordination of many projects across the organization or division. Resources may be shared. It is important that a Program Management Office is established and appropriately staffed with adequate resources to manage the required workload of the Data Management Organization and related activities. The authority and responsibility of the Program Management Office must be defined and communicated to all stakeholders.

Objectives

• Establish, charter and fund the Program Management Office.
• Define and communicate the roles and responsibilities of the Data Management Program function.
• Ensure and enforce alignment of activities and projects to policy and standards through the authority of the Program Management Office function.

Advice

Data Management is no different from any other organizational function. It requires coordination. Program coordination must be formalized, appropriately staffed, funded and empowered to ensure alignment among the stakeholders and adherence to program deliverables. Without the function of the Program Management Office, Data Management is just another good idea that doesn’t get properly implemented on time and within budget. Management of the details associated with the implementation of the Data Management initiative is one of the real measures of implementation success. In larger organizations, there may be an enterprise program management team or office. If this is the case, it is recommended that the data management team secures support and partnership of the enterprise team to leverage and adapt existing processes, standards and policies in support of the Data Management Program Management Office.

Questions

• Has the Program Management Office been established?
• Is the Program Management Office appropriately staffed and funded?
• Does the Program Management Office have the authority needed to be effective?
• Have the roles and responsibilities of the Program Management Office been defined, documented and socialized?
• Have milestones and metrics associated with program delivery been established?

Artifacts

• Evidence of Program Management Office formation such as its charter and approvals
• Evidence of stakeholder identification
• RACI matrix or other evidence of accountability assignment
• Description of the roles and responsibilities of the Program Management Office
• Staff qualifications and assignments
• Evidence of accountability linked to performance reviews and compensation
• Gap analysis of skills needed and in place
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

Formal processes must be established for the activities of the Data Management Program. These processes must align with the Data Management policy and standards of the organization and include procedures, tools and routines. The routines are required for steady-state operations and include, but are not limited to, regular stakeholder meetings, planning sessions, and status reporting.

Objectives

• Establish formal Data Management Program processes in alignment with the Data Management policy and standards.
• Integrate the Data Management Program processes into the overall end-to-end processes of the Data Management initiative.
• Identify, schedule and maintain Data Management Program routines, meetings and working sessions required for operational support.

Advice

Plans and presentations are important, but unless there is evidence of activity being done on a routine basis, the likelihood of a sustained Data Management Organization is at risk. Routines in the form of standing meetings with high repeatable attendance, planning sessions, and regular communications help ensure that Data Management objectives are met.

Questions

• Have formal processes been defined and implemented?
• Are the procedures, tools and routines in place for implementing the processes?
• Are Data Management Program activities part of the normal operational routine of stakeholders?
• Are there standing meetings, planning sessions, and regular communications about Data Management Program initiatives?

Artifacts

• Process design artifacts, procedure guides and published routines
• Process performance metrics reports
• Meeting minutes, status reports and Data Management Program announcements

Scoring

Description

The Data Management Program has the authority to enforce adherence and compliance to the established program, policies, procedures, processes, and standards. The Data Management Program must be formally empowered by senior management and communicated to all stakeholders.

Objectives

• Operate the Data Management Program collaboratively with Data Management stakeholders.
• Endow the Data Management Program with the authority to enforce adherence and compliance through policy and documented procedure.

Advice

Creating the Data Management Organization without empowerment is useless. As a change function, the Data Management Program needs authority to enforce behavioral and operational change. The authority granted must be formal. Support from Internal Audit is very useful to ensure compliance with policy and standards

Questions

• Has the Data Management Program been established as mandatory?
• Has authority been granted to implement and enforce best practice via policy and standards?
• Has authority been communicated?
• Is there a functional partnership in place with Internal Audit?

Artifacts

• Communication from executive management with distribution lists
• Policies and procedures associated with making Data Management mandatory
• List of stakeholders and evidence of bi-directional communication

Scoring

The Funding Model must include resources required for stakeholders to execute the Data Management projects and initiative. The model must align with the organization-wide funding process and be enforced through the Data Management governance process.

Description

The resource funding process for data management must align with the overall funding process of the organization to support specific data management initiatives or projects. The Funding Model must be defined, reviewed and approved by the organizational stakeholders supporting the data management initiatives or projects. As data management initiatives and projects change and evolve over time to support the ever-changing business needs for data and information, the Funding Model also needs to evolve for additional support. The Funding Model must be aligned with the organizational funding processes reflected in the annual and multi-year appropriations processes.

Objectives

• Define and socialize the Funding Model and approach for data management initiatives and projects with stakeholders.
• Revisit and revise funding according to ongoing changing business needs.

Advice

The goal is to ensure that the Funding Model for data management initiatives and projects is synchronized with the overall funding approach of the organization (i.e., budget processes, cycles, escalations, approvals). Successful Data Management Programs leverage existing mechanisms because they are already established and enforceable. As the business needs and stakeholders for data and information evolve over time, it is important for the Data Management Program to continue to revisit and appropriately update the Funding Model design for the changing aspects of the program.

Questions

• Is the funding process being debated with stakeholders at the appropriate level of authority?
• Is the Funding Model reflective on the current year requirements and does it account for anticipated multiple year funding as required?
• Is the Data Management initiative participating in the standard organization funding and budgeting processes?
• Have current stakeholders approved the Funding Model approach and methods?

Artifacts

• Alignment with budget processes and organizational cycles
• Stakeholder alignment and approval of Funding Model & approach
• A process to review, modify, and validate the Funding Model to support changing business needs
• Data Management initiative processes mapping to current and multi-year implementation organizational planning activities

Scoring

Description

The funding plan provides a clear specification of the required resources, including personnel, materials, technology, and other relevant costs, to support the defined business objectives and deliverables. The funding plan must be aligned with the Funding Model and all resources that are required for the execution of the Data Management Strategy. The plan should establish a clear roadmap of financial management, ensure adequate resources are available, and facilitate effective communication with responsible stakeholders.

Objectives

• Define and socialize the funding plan with stakeholders.
• Align funding plan to the business requirements in support of the associated delivery dates of key Data Management initiatives.
• Confirm organizational alignment of funding plan that will sustain data operations

Advice

The funding plan is the reality check of the Data Management initiative. The goal is to ensure that the resources needed to deliver against objectives are available – and to ensure that the business requirements can be satisfied. It is important that all stakeholders evaluate the proposed funding plan and that feedback is captured.

Questions

• Can the technology infrastructure deliver against requirements?
• Can the operations function sustain and support the objectives of the Data Management initiative?
• Is the Funding Plan appropriate for the initiative?
• Has the Funding Plan been socialized and approved?

Artifacts

• Alignment of the budget with business requirements and delivery schedules
• Alignment of Data Management goals with technology and operational capability
• Funding plans and budget allocation
• List of stakeholders and evidence of bi-directional communication

Scoring

Data Management Program roadmaps must be documented and describe the steps required to attain the Data Management initiative’s target-state for its organizational structure and function. A broad set of stakeholders are required to effectively manage the data and must be engaged and participate to create and validate the roadmaps. Accountable engagement should be reinforced through active monitoring and review. Once roadmaps are approved, they must be translated into detailed project plans.

Description

Program roadmaps must describe the steps required to attain the Data Management initiative’s target-state for its organizational structure and function. Roadmap topics include, but are not limited to, governance structure, content management strategy, infrastructure design, and data architecture.

Objectives

• Establish Data Management Program roadmaps.
• Data Management Program roadmaps are driven by and aligned with the Data Management Strategy and operating unit roadmaps.
• Formalize stakeholder accountability to the program deliverables through job description modification and compensation modification.

Advice

Data Management is a collaborative activity with implications across the organization affecting multiple stakeholders. Performance commitments from stakeholders are an essential aspect of a successful Data Management Program and come in many forms. It involves a financial commitment as well as an operational commitment that is often a daily necessity and requires accountability for responsible participants. Ensuring that data is properly curated, secure, and accessible is a shared responsibility. Stakeholders should be accountable as part of the risk framework of the organization to evaluate the appropriate and ethical use of data. All these accountabilities should be formally included in performance review and compensation. The expanding importance of advanced analytics and artificial intelligence within an organization brings with it a diverse set of stakeholders and unique requirements for data. Make sure the stakeholder analysis aggressively identifies all data stakeholders. Strategies should include high-level roadmaps. Those roadmaps should be driven by the Data Management Strategy and supported by detailed project plans that may include roadmaps for each project plan. Therefore, roadmaps may exist at multiple levels within the overall planning structure. Defined and detailed roadmaps are needed to establish and communicate the required actions to reach the Data Management initiative’s target-state across all the Data Management framework components. Roadmaps must be consistent with the strategy, as they illustrate the path for implementation. These plans don’t have to be fully fleshed out, but they do need a clear, real-world definition of what will be done and when. Short-term roadmaps (i.e., 30/60/90-day) do need to be comprehensive. More flexibility is acceptable for long-term plans. Questions should be raised about the scope, practicality, and achievability of each plan. Identify what dependencies exist within or between plans as dependencies can add risk. It is essential that roadmaps be developed in coordination with the appropriate operating units and shared, reviewed and approved by stakeholders. Collaboratively working with operating units and stakeholders during the roadmap planning and development phases promotes direct feedback and buy-in. Sharing the program plans with stakeholders helps ensure support. This will require discussion and likely a modification of plans. The back-and-forth collaboration is essential to ensure stakeholders own the outcomes, deliverables, and commitments.

Questions

• Have clearly defined Data Management Program roadmaps been developed?
• Have stakeholders been identified and verified?
• Are roadmaps and plans tangible (i.e., can they be measured)?
• Have the dependencies across the program and roadmaps been defined, documented, and verified?
• Are any/all dependencies included in their respective budgets?
• Have the roadmaps been developed in coordination with operating units?
• Have the roadmaps been shared with key stakeholders?
• Has feedback, including suggestions and concerns, been captured and addressed?
• Have final roadmaps been developed, documented, and approved by stakeholders?
• Have stakeholders demonstrated a commitment to the objectives of the Data Management Program?
• Is funding in place to verify commitment to Data Management Program deliverables?
• Is there a mechanism to ensure accountability such as alignment with performance review and compensation?

Artifacts

• Data Management Program roadmaps, including evidence on how they align to data the management strategy
• Maps of dependencies across roadmaps associated with implementation
• Outcomes and projected deliverables
• Budget alignment with approach, roadmaps, and dependencies
• Alignment of roadmaps with appropriate organizational stakeholders
• List of stakeholders and evidence of bi-directional communication with organizational units
• Verification and approval of roadmaps

Scoring

Description

Once roadmaps are agreed to and approved, they must be translated into tangible mechanisms for delivery. The Data Management Program Office is responsible for the creation, coordination and management of Data Management project plans, detailing deliverables and establishing timelines, milestones and resources. Data Management requires participation and cooperation from staff and resources outside the Data Management Program organizational structure, as well as staff and resources from other organization-wide control functions. Those identified as stakeholders must be held accountable for on-time and on-budget program delivery. To strengthen that commitment, individual performance in support of the Data Management Program should be reflected in stakeholder reviews and compensation.

Objectives

• Develop project plans, including deliverables, timelines, resources and milestones aligned with programs’ roadmaps.
• Manage program project plans with standard organizational project management methods and processes.
• Document stakeholder agreement with program deliverables.

Advice

Program roadmaps need to be translated into detailed project plans. The management of these project plans should be centralized via the established Program Management Office to ensure adherence and delivery. The project plans need to contain practical deliverables and reflect the priorities that were negotiated with stakeholders. It is important to ensure that resource plans are sufficient to support the plan's commitments and that strong stakeholder commitment is aligned with approved budgets.

Questions

• Do practical project plans exist?
• Are they aligned with program roadmaps and budgets?
• Is there a centralized mechanism, Program Management Office, in place to oversee implementation?
• Are routine project review procedures in place to track progress?
• Have stakeholders pledged sufficient resources to implement project plans and meet program roadmaps?
• Do the resources exist, or do they need to be acquired?
• If they need to be acquired, has sufficient ramp-up time been incorporated into deliverable timeframes?
• Does the Data Management Organization have authority to review and modify resource plans of stakeholders?

Artifacts

• Evidence of completed project plans with defined deliverables
• Timeframes, resource loading and milestones in line with implementation roadmaps
• Evidence of centralized Program Management Office review processes, including progress tracking, review feedback, reconciliation, and approval procedures.

Scoring

Description

Sufficient funding dedicated to the Data Management Program must be allocated and aligned with committed business, technology and operations organizations. In a mature Data Management Program, the Data Management Organization is granted authority to review and approve committed budgets.

Objectives

• Align funding to the program roadmaps, plans and workstreams.
• Review, approve and allocate funding levels accordingly.

Advice

Funding plans have dependencies and interrelationships. The goal is to ensure that all stakeholder plans are approved and aligned with the objectives of the Data Management Program. There is no single strategy for funding Data Management initiatives. The strategy will depend on the culture of the organization. Some fund centrally, some require operating unit allocations, some provide seed funding for early-stage activity and some mix and match. Regardless of the funding mechanism, accountability and predictability are required. as the Data Management Organization needs a reliable and realistic mechanism to ensure funding commitment.

Questions

• Have budgets been prioritized to ensure adequate funding for the Data Management Program?
• Are budgets aligned to Data Management initiative deliverables?
• Does the Data Management Organization have the authority to challenge stakeholders about budget commitments?

Artifacts

• Funding plans and budget allocation
• Funding approval and authorization to spend
• Escalation procedures for budget shortfalls

Scoring

The success of the Data Management Program requires organization-wide standard Data Management processes that are repeatable, sustainable, and measurable. The organization should leverage existing industry standards and best practices The use of these standards must be required by policy.

Description

Standard data management processes are documented and informed by industry standards and best practices. Engagement with industry trade groups, research organizations and standards bodies ensure that the organization is aware of and aligned with the latest Data Management trends and new developments related to best practices.

Objectives

• Create a formal function with dedicated resources to maintain participation in Data Management education, industry activities and events
• Keep stakeholders informed about changes and events in the Data Management industry

Advice

The organization should participate in industry trade groups to stay informed of new developments in Data Management. Likewise, engagement with standards bodies needs to be formalized. Internal owners and facilitation agents are a good way of ensuring the flow of information across the organization. Embedding engagement with external organizations and standards bodies into job descriptions is useful to ensure participation. As new issues emerge – like advanced analytics and data ethics – leveraging early best practices can be extremely valuable to the organization for increased effectiveness and adoption speed.

Questions

• Do you have a strategy for engagement with the Data Management industry outside of your organization?
• Are the appropriate owners and facilitation agents identified and engaged?
• Does executive management understand the value proposition and buy into the engagement activity?

Artifacts

• Evidence of participation in industry trade groups
• Evidence of attending training and Data Management education classes and forums

Scoring

Description

The Data Management policy must require the adoption of standard Data Management processes and tools across the organization. Standard routines should be established that perform and confirm the use of standard Data Management processes across the organization.

Objectives

• Establish routines that support the review and confirmed use of standard Data Management processes.
• Empower the Data Management Organization to require remediation of gaps found in operational processes.
• Charge Internal Audit to perform routine examinations of the Data Management processes and report findings as appropriate.

Advice

Data Management processes should be routinely monitored. Monitoring occurs on three levels. 1) Quality Ccontrol self-attestation where the stakeholders evaluate and assert, they are following the Data Management processes. 2) Quality Assurance by the Data Management Organization, where the Data Management initiative works with stakeholders to validate compliance. 3) Internal review where Internal Audit has formally validated that processes are being followed.

Questions

• What are the mechanisms to ensure quality control and quality assurance of the management processes?
• What is the schedule for Internal Audit review of the Data Management processes?

Artifacts

• Evidence of self-attestation and enterprise Data Management Organization review
• Evidence of Internal Audit engagement and review

Scoring

Change and enablement focus on identifying team duties, defining organizational data roles, and developing engagement activities to support data management strategies. The keys to successful change management are clear, consistent, and transparent communication coupled with well-defined roles and responsibilities. If an organization has a formal change management function, the data management team should partner and engage with the team and leverage the standards and learnings they can provide to the data management organization.

Description

The scope and approach of the Data Management Change & Enablement function is documented. This includes role and responsibilities, activities related to Data Management Change and Enablement and how these activities align with the activities of the other data management components. The shared activities that support Data Management Change and Enablement have been identified, scoped, communicated, and launched across the Data Management program.

Objectives

• Data Management functions that support enablement have been identified.
• Change and Enablement duties and responsibilities have been documented.
• Change and Enablement functions, or integration into existing change and enablement processes, have been created.

Advice

For a successful data management plan within an enterprise, pinpointing and fostering the necessary skills and knowledge is essential during the transition and for future operations. Assessing the current capabilities to implement change and prepare for any foreseeable challenges, is crucial. Commitment from the project sponsor, particularly in supporting the workforce during change, is vital. Additionally, active and sustained engagement from the highest level of leadership is key to the long-term success of the plan.

Questions

• Does the change initiative have a primary sponsor with the necessary authority over the people, processes, and systems to authorize and fund the change?
• Does the change initiative have clearly defined goals and implementation approach?
• Have all change-related resources (people, materials, budget) for the effort been identified and secured?
• Are the resources required to execute the change plan prepared to realize the plan?
• Is there a clear roadmap developed that depicts what is planned to be completed by when?

Artifacts

• Data Management Change and Enablement Approach
• Change and Enablement Roadmap
• Change & Enablement roles & responsibilities
• Change & Enablement Plan
• Responsibility Assignment Matrix (RAM)

Scoring

Description

It is important to define, document, and establish the organizational stakeholders that will interact with the Data Management Change and Enablement engagement roadmap. The roadmap must be aligned appropriately with the organization. The processes to define and document the knowledge and awareness required for the impacts of change have been established. As organizational impacts are identified, the processes to develop and execute communications and engagement plans are established considering how the organization will be impacted, as part of the Data Management Program.

Objectives

• Identify and establish the required knowledge.
• Develop an appropriate engagement roadmap and plan for stakeholders.
• Align Stakeholders to support the engagement plans.

Advice

To drive stakeholder, buy-in for a data management change, it’s essential to highlight both the positive outcomes and potential challenges. A strong case for change management is crucial, as it ensures a smoother transition and better alignment with organizational goals. Identifying stakeholders, understanding their stance, and customizing plans for each group are key steps. Clear, continuous communication about the rationale and benefits is necessary to maintain stakeholder engagement.

Questions

• Is the primary sponsor encouraging leaders to participate and support the change?
• Is it clearly defined who is impacted and how they are impacted?
• Have you made a compelling case for why change is important?
• Are incentives in place to help make the change stick?

Artifacts

• Stakeholder analysis
• Engagement Approach Document
• Communication Records
• Leadership Endorsement
• Impact Assessment Report

Scoring

Description

Data Management Change and Enablement activities in line with the data management function have been established and integrated. Realistic goals for continual engagement have been set during data literacy development across the organization and appropriately captured and reported. Change resistance risks have been identified and monitored with appropriate mitigation activities fully deployed. The Data Management Program has established a sustainable engagement presence supporting activities for the Data Management Strategy roadmap.

Objectives

• Established and adopted the ability to measure and track development performance metrics and targets.
• Identified and addressed the resistance and risks to data management activities.
• Visibility of in-progress work across business has been embedded in the engagement plan.

Advice

For effective change management, set SMART goals that enhance awareness, desire, knowledge, ability, and reinforcement. Successes should be measured and celebrated by the primary sponsor to reinforce change and address resistance. This ensures a supportive environment for the change initiative.

Questions

• Have measurable goals with targets been documented and data collected to easily present progress?
• Is a plan in place to continuously check, monitor and react to measures of success?
• Are owners in place for each goal to clarify who is responsible for monitoring and driving action?
• Have adoption objectives been set?

Artifacts

• Scorecard of measurable goals and their targets
• Performance Dashboard
• Monitoring Plan

Scoring

Communication ensures the development of a data management communications approach, creation of promotional materials, and ongoing engagement to keep the organization informed about data management progress. If an organization has a formal communication function, the data management team should partner and engage with the team and leverage the standards and learnings they can provide to the data management organization.

Description

A communications and messaging approach that provides visibility and information for identified audiences, both internal and external, to the organization has been created. The appropriate assessment, utilization, and sourcing of existing channels which require additional tools has been undertaken. Existing organizational communication processes, and the coordination of Data Management Strategy have been identified.

Objectives

• Establish standard Data Management communication channels and operational communications program.
• Document standard communications processes and standards.
• Incorporate the communication approach goals and objectives into the Data Management Strategy.
• Integrate with existing organizational communication practices.

Advice

The goal of the communications approach is to establish an organization-wide data culture that understands the importance of data and the appropriate and ethical use of the data. Every individual should know their role in meeting the data objectives of the organization. Effective change communication involves leveraging existing resources, using multiple channels to reach the appropriate audience, tailoring messages to different groups, and encouraging feedback through two-way communication to drive engagement. Focus on communication as a two-way street. Maintain an active mechanism for discussions about value derived vs. the inevitability of operational disruption. Take advantage of the existing internal communications infrastructure to develop and implement an organization-wide communications strategy. Consider methods to involve the full spectrum of participants, including Corporate Communications, Human Resources, Executive Management, and Internal Audit in the communications program. There are many subtle but critical concepts (e.g., understanding the difference between correcting bad data and fixing data problems at the source) that these partners may discover that people closer to the solutioning cannot. It is important to evaluate the degree to which executive management is participating in these activities. Having and highlighting their engagement denotes importance and sends a clear, positive message of support for the Data Management initiative. Introduce continual communications to reinforce Data Management concepts and emphasize that it is not a one-and-done process. Think about a variety of communications channels and mechanisms to keep the content fresh. Communications is a critical aspect of success that benefit from a dedicated professional. The full spectrum of communications channels (e.g., websites, access portals, reference libraries, documents, training materials, town hall meetings) can be leveraged to ensure that stakeholders understand the goals, objectives and processes associated with the Data Management Program.

Questions

• Are there tried and true channels/communication means/tools in place in the company that are being used?
• Is there a formal process for receiving feedback and answering questions from stakeholders?
• Are there standard enterprise tools that are being used for communication?
• Does each audience group have a curated communication plan?
• Has the importance of communication and training been defined as part of the Data Management Strategy?
• Does the communications plan include ongoing communications for new employees?
• Does the communications approach define the core goals and objectives of the Data Management initiative?
• Have the plans been created, published, and approved?
• Are the communications channels defined and established?
• Is the internal communications program operational?

Artifacts

• Feedback/two-way communication framework
• Data Management Communication Approach including list of all potential communication means
• Communications plan and documentation of the channels to be used
• Evidence illustrative of content and methods used
• Definition of mechanisms for engagement
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

The development of resources and materials that provide data management professionals with relevant templates and tools to support communication have been developed. Each set of materials should use the correct terminology in their resources and materials, ensuring that the intended audiences can fully understand the information being presented to them. Over the course of the resources’ life cycle, material has been regularly checked and maintained to ensure that it remains in line with the current data management strategy as it matures.

Objectives

• Standard communication tools and templates suitable for data management use.
• The tools and templates are maintained and updated as data management matures and needs change.

Advice

Effective communication is the backbone of data management. Documenting and deploying Data Management Communications standards ensures that all stakeholders are on the same page, leading to consistent understanding and application of data practices. This sub-capability serves to align communication strategies with data management objectives, facilitating clarity and compliance across the enterprise.

Questions

• Are the Data Management Communications standards comprehensively documented and accessible to all relevant stakeholders?
• Have the communication standards been integrated into the daily operations of the data management teams?
• Is there a process in place to regularly review and update the communication standards?
• How are changes to the standards communicated to the stakeholders?
• What measures are in place to ensure adherence to the communication standards?

Artifacts

• Data Management Communications Standards
• Records of stakeholder acknowledgment of the standards
• Update logs showing the history of changes to the communication standards

Scoring

Description

The formation of a communication plan that provides engagement and open communication on the progress of Data Management within the organization is essential. A benchmark on the measurements for sustained engagement success should be set and used to evaluate relevant outcomes from the ongoing management strategy plan. The communication plan aligns with the current trajectory of the organization’s data management maturity and growth and allows for two-way feedback between the various Data Enablement teams and relevant audiences.

Objectives

• Communication roadmap is aligned to data management maturity growth, and provides relevant, actionable, and contextually relevant information to audiences.
• Appropriate two-way feedback communication has been developed.

Advice

An effective communication roadmap is designed to ensure stakeholders are consistently informed about data management efforts. It emphasizes the importance of clear, regular updates and the need for feedback mechanisms to refine communication strategies. The goal is to foster an environment where communication is not only informative but also responsive to stakeholder input.

Questions

• Is there a clear roadmap developed that depicts who, when and what is planned to be completed by when?
• Are you evaluating the effectiveness of your communication?
• Are you acting based on your evaluation and updating approach if communication strategies are not effective?

Artifacts

• Data Management Communications Plan
• Measurable success factors to evaluate effectiveness of communication
• Clear action plans to address gaps in communication and feedback

Scoring

Measuring the Data Management Program includes program metrics, outcome metrics, process metrics, and financial metrics.

Description

Data Management program metrics are documented and used to track program progress. Develop program metrics for Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs) to track the progress of the implementation and adoption of the Data Management initiative. Include other critical metrics that demonstrate the health and wellness of the Data Management initiative. The metrics include program capabilities such as organizational structure rollout, adoption and engagement, skill set hiring, leader appointments, policy adoption, and standards implementation.

Objectives

• Establish program metrics and thresholds aligned with the Data Management Strategy.
• Track and report program metrics to stakeholders, including executive management.
• Use program metrics to inform and drive program decisions and modifications.

Advice

Program metrics are designed to track progress against the implementation plans for the Data Management initiative. This is the implementation of metrics at the end-user level, designed to provide evidence of progress against the Data Management Program roadmap. Validation that you are on track serves as a valuable tool for communication across stakeholders, including executive management.

Questions

• Has the concept of metrics related to the Data Management initiative itself been defined?
• Have the program-related metric plans been socialized and verified?
• Is there a method for monitoring progress against the approach and roadmaps of the Data Management Program?
• Have metrics been defined across the core Data Management capabilities?
• Do stakeholders support the metrics program?
• What reporting mechanisms are being used?

Artifacts

• Definition of program metrics
• Evidence of the program metrics tracking and reporting within the Data Management Strategy
• List of stakeholders and evidence of bi-directional communication
• Approvals and sign-off reports, dashboards, heat maps and other forms of output

Scoring

Description

Data Management Outcomes are documented and used to track progress against business objectives. Outcome metrics are measurements of the net effect of the Data Management initiative. Define metrics that cover the stated business objectives that align to the data, data usage and Data Management deployment strategies in the Data Management strategy. Examples of outcome metrics include lower operational failures, streamlined reporting, reduced number of data reconciliations, improved data discovery, improved access to critical data, and the use of data is appropriate and ethical.

Objectives

• Establish outcome metrics and thresholds aligned with the Data Management Strategy.
• Track and report outcome metrics to stakeholders, including executive management.
• Use outcome metrics to inform and drive program decisions and modifications.

Advice

Stakeholders need to understand the concepts of factor-of-input and data interoperability, harmonization, and process automation. It is important to measure value, but it must be remembered that data is only one input. It is quite possible to have good data but not achieve the desired outcome because of operational deficiency. Measuring areas such as Straight Through Processing, reduction of repairs, improved discovery, consolidation of technology is multidimensional and not always easy to trace back to Data Management.

Questions

• What are the expected operational outcomes associated with Data Management and proper data hygiene?
• How will the organization measure both the defensive and offensive value of Data Management?
• Do stakeholders understand the concepts associated with data as a trusted factor of input?
• Do stakeholders understand the concepts of linked analysis and causality?
• Do stakeholders understand the concepts of appropriate and ethical use of data?

Artifacts

• Definition of outcome metrics
• Evidence of the outcome metrics tracking and reporting within the Data Management Strategy
• Approvals and sign-off reports, dashboards, heat maps and other forms of output
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

Data Management Process Metrics are documented and used to drive continuous improvement. A core aspect of business process optimization is establishing metrics to measure the performance of that process. When the process is not performing as designed, there is an opportunity for process improvement.

Objectives

• Establish process metrics and thresholds aligned with the Data Management Strategy.
• Track and report process metrics to stakeholders, including executive management.
• Use process metrics to inform and drive program decisions and modifications.

Advice

Data Management requires process metrics to demonstrate that the processes are performing as designed. The metrics are intended to be an early alert system to identify process failures. Additionally, metrics often support assessing the impact and priority of an underperforming process.

Questions

• Are there methods of measuring the performance of Data Management processes?
• Have metrics been defined across Data Management processes?
• Do stakeholders support the metrics program?
• What reporting mechanisms are being used?

Artifacts

• Definition of Data Management process metrics
• Evidence of the Data Management process metrics tracking and reporting within the Data Management Strategy
• Approvals and sign-off reports, dashboards, heat maps and other forms of output
• List of stakeholders and evidence of bi-directional communication

Scoring

Description

Data Management Financial Metrics for total program costs and benefits (ROI) are tracked and reported. Data and Data Management expenses occur throughout an organization and need to be assessed in the context of the overall Data Management initiative. Determining the current cost at the enterprise level as well as the operating unit level establishes a benchmark that can be referred to as the organization-wide Data Management initiative is established and deployed. The metrics should include cost-benefit analysis that results in an accountable return-on-investment measurement.

Objectives

• Work with stakeholders to establish a sustainable method for calculating the financial benefits of the Data Management initiative at the enterprise and organizational unit levels. Use an established organizational standard or a newly created method.
• Measure and monitor financial benefits and use these results for enterprise and organizational unit-level decision making.

Advice

It is important to establish a cost baseline for the Data Management initiative. This baseline is an essential metric that will repeatedly prove to be valuable. Prepare to discuss what constitutes data expenses and what is the appropriate methodology to use to capture spending by data category. Capturing the benefits of Data Management is required to ensure continued buy-in to the Data Management initiative. Benefits should be understood in the context of the entire organization. This understanding rests on the evaluation of all the organization-wide dependencies associated with trusted data and the impact of bad data. This usually will not fit into standard criteria for the calculation of project-based ROI. Determine the current methodology for these calculations. Examine the value proposition from four dimensions:

1. 1) operational efficiency (cost)
2. 2) trust (model-based strategies)
3. 3) insight (upselling and predictive analysis)
4. 4) flexibility (ability to adapt to changing circumstances)

Positive cost-benefit analysis of the Data Management initiative is necessary to ensure organizational adoption. Data Management affects many systems and processes across the organization and may need to be evaluated beyond standard, project-based return-on-investment (ROI) methodologies. Determining ROI should include a defensive and offensive view of the benefits. Evaluating the benefits of maintaining ethical usage of data is a defense-based analysis measuring cost avoidance. Whereas the benefits tied to advanced analytics is an offense-based analysis measuring increased opportunity and the resulting revenue. Ensure that captured metrics are being properly used for decision-making, resource allocation, task prioritization, and other similar business objectives. Graphic expressions using tools such as heat maps help put Data Management into context. The ability to identify areas of improvement can be useful in loosening purse strings.

Questions

• What methodology is used to capture total Data Management spend (e.g., acquire, cleanse, store, manipulate, transform, integrate, distribute) and soft metrics (e.g., reconciliation, lack of capability, missed opportunity, capital charges, inefficient operations and collateral calculations)?
• What is the organizational view of the benefits associated with Data Management?
• What are the methodologies used to calculate financial and operational benefits?
• How have these metrics been used to establish and remediate priorities?

Artifacts

• Expense categories with evidence of agreement
• Cost allocation methodology
• Total Cost of Operations calculation with worksheets, approvals, reporting, and ROI criteria
• Documentation of methodologies and illustrations of application
• Evidence of use of metrics to evaluate, adjust and enhance the Data Management initiative
• List of stakeholders and evidence of bi-directional communication

Scoring