【正文】 Data management 。 Periodic review and adjustment of data management priorities to ensure alignment with business objectives.Typical Work Products1. Catalog of data management projects by business unit/function2. Guidelines for ranking data management projects3. Report on data management project implementation as measured against priorities 4. Enterprisewide data management projects plan (for verification against defined priorities)5. Enterprisewide data management priorities list6. Enterprisewide library/repository of data management projects by priority7. Data management priorities review procedures and scheduleLevel 4: Measured Data management activities are clearly aligned with data management priorities. The documentation and review process for prioritization is standardized and repeatable A formal process exists for making tradeoffs on data management project priorities among all relevant stakeholders. A formal project prioritization methodology exists within business units。 Data management priorities are defined based on groups of projects at a program level or in line with business functions. Specific projectbased priorities are not integrated with the priorities of other projects. Priorities of data management are not defined.Level 1: Performed The organization can provide clear evidence of continuous improvement based on formal business process analysis. Metrics are used to continuously refine and enhance business objectives throughout the organization. Organizational business objectives are linked to data management objectives throughout the entire business process (endtoend links established). Formal processes exist to quantify both incremental and longterm outes of data management, and the metrics are used to measure achievement of objectives. All programs across the organization are measured and evaluated from a data management perspective and aligned with corporate data management and business objectives. Performance of data management is clearly translated into business benefits. All relevant stakeholders are involved in and aligned with data management. Data management program objectives are municated broadly across the organization Periodic and formal evaluation of data management objectives against business drivers and goals takes place at the organizational level with stakeholders. Ad hoc data management metrics exist and are loosely aligned with management objectives.Typical Work Products1. Localized statements of data management objectives in key process areas (pliance, customer centricity, financial reporting)2. Project plans include data management objectives and tasks3. Project plans, tasks, and other statements are connected to ponents of localized data management strategy4. A data management strategy document exists to address objectives, priorities, benefits, resources, responsibilities, and an action plan Level 3: Defined Data management objectives are defined and documented within the context of a project, group of projects, region, business area or function. Adherence to informal policies for data management is done on a project basis and loosely monitored.Typical Work Products1. Documented data management objectives2. Documented data management policies3. Project documentation (showing adherence to data management policies)Level 2: Managed No formalized data management objectives defined.Level 1: Performed All business rules pertaining to the use and validity of data must be validated by the business stakeholders and documented. Business value associated with data management must be measurable in business terms. to create and sustain an effective data management program ensuring the appropriate creating therefore the statements of objectives will need to be customized to the specific institution. The result will be a set of basic principles/core data values and data architecture blueprint of the organization that are acpanied by an assessment of current capabilities and aligned with the governance mechanism for each of the core data management areas. Examples of concepts that are incorporated into the statement of shared goals include: the objective of valuing Draft copy for review by EDM Council members onlyTable of ContentsAbout iiIntroduction iiiBackground iiiAbout The DMM, What it is iiiAudience ivDMM Framework ivOrganization of the DMM Text viCapability and Maturity Levels viiiCapability Measurement ixMaturity Measurement xData Management Strategy 15Data Management Goals 16Data Management Objectives 16Data Management Priorities 20Scope of Data Management Program 23Corporate Culture 27Alignment 27Communications Strategy 31Governance Model 35Governance Structure 35Organizational Model 39Oversight 43Governance Implementation and Management 46Human Capital Requirements 50Measurement 54Data Management Funding 58Total Lifecycle Cost of Ownership 58Business Case 62Funding Model 66Data Requirements Lifecycle 70Data Requirements Definition 70Operational Impact 74Data Lifecycle Management 77Data Management Operations 80Standards and Procedures 81Areas 81Promulgation 85Business Process and Data Flows 88Data Dependencies Lifecycle 91Ontology and Business Semantics 95Data Change Management 99Data Sourcing 105Sourcing Requirements 105Procurement amp。 and David Williams, Data Governance Director, Citi who all have invested a significant amount of their time and intellectual capital into the development of this draft of the Data Management Maturity Model.Michael AtkinManaging Director, EDM Council 156169。 Richard White, Data Governance Director, Citi。 Melanie Mecca, Senior Associate, Enterprise Data Architect, Booz, Allen, Hamilton。 John Housen, Enterprise Data Management, Governance a