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Level 1: Performed
- Data management resourcing and oversight are event-driven.
Data management sta may be in a central organization or allocated to projects. Their roles are not typically distinct (e.g., position descriptions may refer to data analysts, data architects, etc.), and their activities may di er from project to project.
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Level 2: Managed
An approved interaction and engagement model ensures that stakeholders engage with the data management organization.
Refer to Data Management Strategy, which provides input to organizational model in the form of strategic objectives, priorities, and identified stake- holders.
2.2 Principles are defined and followed to guide the consistency of practices related to data management.
Rarely can all activities and expectations be explicitly defined for every eventuality. To help ensure consistency of practice when explicit guidance
is not available, guidelines and principles are used to help shape decisions. Documented principles help to ensure alignment between stakeholder activ- ities and those of data management sta across the business unit.
2.3 Roles and responsibilities are specified to support the governance of data management and the interaction between governance and the data management function.
Data governance roles are established in a form that clearly communicates to those with responsibilities, and their stakeholders, what those responsibilities are and who has them. Typical examples include a RACI matrix, position descriptions, and organizational charts indicating who has what types of responsibilities relative to data management activities.
Refer to Governance Management, which describes organization-wide bodies of responsibility and authority for corporate data assets, to which the data management organization provides sustaining leadership and support.
2.4 Agreements are in place that provide explicit expectation for the use of shared staff resources with responsibilities for data management.
Two common problems often surface when the management of human resources is shared: the resource is conflicted by the di erences in goals and objectives between the two managers, leading to the resource being placed in the untenable position of making decisions that should be made by management. Having agreement on a clear set of expectations helps to mitigate these issues.
Refer to Data Lifecycle Management, which helps to identify the components of the organization that have a need for, or manage, data. Knowledge of this information is important to ensure that the data management function fully encompasses business needs.
2.5 A mechanism exists and is followed to identify and apply needed changes to enhance or redesign the data management function.
Reviews of the data management function should be performed periodically to assess its relevance and allocation against the changing needs of the business. Other triggers for change typically result from events such as objective measures, results of internal process audits, or suggestions for process improvement.
Rarely does a business unit or organization remain static; business objectives, technology, and sta ng resources change. These tend to impact the needs of the data management function and its governance. It is important to make adjustments to ensure that the existing data management function continues to be aligned with the needs and resources of the business.
In lower maturity organizations, changes related to the data management function are generally event-driven and often made after something has exposed a weakness in the model. By contrast, higher maturity organizations establish objective measures and regularly schedule reviews of the data management function to ensure that the model is meeting the current and future needs of the organization, and initiate changes as necessary.
Refer to Governance Management, which describes organization-wide bodies of responsibility and authority for corporate data assets, to which the data management organization provides sustaining leadership and support.
Example Work Products
- Policies
- Process documents
- Program guides
- Defined roles and responsibilities
- Metrics related to the data management function
- Function review notes or report
- Lessons learned documents
[tabby title=»Defined»]
Level 3: Defined
3.1 A data management function is established with responsibility for managing activities that support data management objectives.
Data management resources, roles, and responsibilities are approved at the executive management level of the organization. Furthermore, policy-making authority establishes principles of operation, standards, and objectives of data management operations across the organization. Data management must be equipped with su cient authority and resources to guide the program. It must also be formed carefully to ensure that it has the appro- priate stakeholder perspectives to ensure successful implementation and institutionalization of its published guidance, and to ensure that its principles encompass all of the stakeholder needs.
Executive management should be involved in data management oversight and help to facilitate adoption and institutionalization. Executive sponsorship establishes shared expectations, promotes confidence in the program, and approves methods for measuring progress. Active engagement of executive sponsors is important to ensure continued alignment with goals and objec- tives, and continued alignment with business, as well as to monitor milestone achievement. Executive sponsors are also important in managing expecta- tions and prioritizing data management resources. Typically, the function of data management reports to a senior executive, often the Chief Data O cer.
Clear roles and responsibilities, along with accountability mechanisms, are defined, documented, and standardized. Performance goals for members of the data management team should include data management program goals. This helps to ensure that alignment between data management needs and the organization’s strategy and priorities is maintained.
3.2 The interaction model for the data management function ensures the involvement of data governance for projects that use shared data.
The organization should understand which projects and data management activities are in direct support of its organizational goals and strategy. Shared data sets are critical to the accomplishment of the organization’s goals; and projects which may create, manage, or have an impact on this data should be monitored through the data management function. The monitoring of these projects and activities should be accomplished via regular communications between the execution teams, senior management, and the positions and roles established as part of the data management function. Criteria are generally established through which the monitoring occurs, including a standard set of core metrics that allows consistent evaluation among projects and activities across the organization.
Refer to Data Lifecycle Management for identifying the components of the organization that have need for, or manage, data across projects and other initiatives and operations.
3.3 A data management organization and specified structure are defined and periodically reviewed to ensure that they meet the needs of the organi- zation.
The data management organization is formed to conduct the following: Promote guiding principles of data management
Clarify and communicate responsibilities
– Foster a common understanding of objectives
– Address and synthesize the expectations of various business functions
– Facilitate approved criteria for measuring progress
This organizational model should be regularly reviewed to ensure that it evolves as the needs of the organization change and governance activities are institutionalized.
To ensure that the data management function meets the needs of the organization, data management sta ng should be managed from a strategic viewpoint. Regular review of organizational objectives and evaluation of existing skills and capabilities should be conducted to ensure that sta ng will consistently meet the needs of the organization, with hiring or sta training plans initiated as needed.
The Data Management Strategy process area provides guidance to the organizational model in the form of strategic business objectives, priorities, and stakeholders. These form the basis of the objectives that the data management function executes and oversees.
3.4 Data management processes are established and maintained by the data management function with governance approval.
3.5 Data management is an explicitly recognized business function and is leveraged across the organization.
Strong data management practices often require subject matter experts for execution. These experts should be trained and, where appropriate, certified in their specific discipline. Career paths and professional growth plans should be established to ensure that these sta members have the means to increase and hone their skills for the benefit of the organization. These resources are valuable assets of the organization, and their skills and competency should
be formally recognized so that they can be leveraged across the organization. Doing so will help to guide others with less training, will help to support consistency of practices across the organization, and will facilitate institution- alization of sound data management practices.
Some organizations further articulate this orientation, developing centers of excellence for data management disciplines (for example, for data quality, data integration, or data modeling). While centralization is usually accom- panied by challenges, this approach can provide e ciency gains, consistency in process execution, and increased product or service quality.
Example Work Products
- Data management function documentation Data management structure
- Training records
- Compliance or audit reports
- Project reports
- Governance oversight plan
- Communication plans and schedules
- Definition of roles, responsibilities
- Performance measures
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Performance measures Level 4: Measured
4.1 The data management function has defined tasks that are measured and assessed using statistical and other quantitative techniques.
4.2 Modifications of the data management function and its practices are based on an analysis of indicators using statistical and other quantitative techniques.
Example Work Products
- Data management structure
- Performance measures
- Record of changes to data management structure
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Level 5: Optimized
5.1 The operational plan for the continuous improvement of data management activities must be prioritized.
5.2 Analysis using statistical and other quantitative techniques as well as the use of process performance models leverages data to improve operational efficiency.
At a mature level of data management capability, it is recognized that not only are data assets of the organization, but they are directly supportive of the organization’s ability to achieve its strategic objectives. The operational e ciencies of all activities critical for achievement of organizational goals are quantitatively measured, and these measures are the preeminent source for decisions on what changes should be made. Continuous improvements based on predictive analytics are expected as part of the portfolio of activities used by the organization to initiate or support change.
Example Work Products
- Resource plans
- Priorities aligned with strategy
- Strategic decisions and supporting metrics
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