【文章內(nèi)容簡(jiǎn)介】 arying levels of goals. For capability measurement, process areas can be viewed as independent and organizations may select any process area or number of process areas as their area of focus for building and improving capabilities. Within each process however, there is an expectation in the model that each increased capability level builds on the levels below it. For example, in order to achieve level four, an organization would have to meet expectations defined for each level up through level four in that process area. Figure 3 below shows an example of selected process areas and capability level measures. Note how the capability level may be different for each process area. Figure 3. Capability Level ExampleMaturity MeasurementMaturity assessment as apart from capability measurement requires that all process areas in the measure ponent or category are at a mon minimum, as well as full implementation of the support processes that are defined separately from any one particular ponent. For example, in order to have a level three maturity rating for the Data Quality category, the organization must be at least level three for all process areas in the Data Quality category plus fully implemented across all four support processes (available in separate document). Figure 4 below shows this example.Figure 4. Maturity Level ExampleAcknowledgementThis document represents three years of effort and thousands of hours work from representatives across a variety of organizations. Led by the Enterprise Data Management Council in partnership with the Software Engineering Institute (SEI) of Carnegie Mellon University, the content was created through a collaboration of numerous data practitioners and represents the best thinking from industry on how to turn the ‘a(chǎn)rt and practice’ into the ‘science and discipline’ of data management. On behalf of the EDM Council, I would like to acknowledge the extraordinary contributions of Funmi Balogun, Director, Enterprise Data Standards, Fannie Mae。 Roy BenHur, Senior Manager, Deloitte and Touche。 John Bottega, Chief Data Officer Enterprise Change, Technology and Operations, Bank of America。 John Carroll, Managing Consultant, element22。 Predrag Dizdarevic, Principal, element22。 Jeff Gorball, Managing Director, Kingland Systems。 Doug Finn, Principal, Deloitte and Touche。 John Housen, Enterprise Data Management, Governance and Process Executive, Chartis Insurance。 Olga Maydanchik, Data Quality, Citi。 Melanie Mecca, Senior Associate, Enterprise Data Architect, Booz, Allen, Hamilton。 Doug Nixon, Financial Services, Ernst and Young。 Richard White, Data Governance Director, Citi。 Gian Wemyss, Senior Member of the Technical Staff, Software Engineering Institute, Carnegie Mellon University。 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。 EDM Council Inc. 2012 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。 Provider Management 109Platform and Architecture 113Architectural Framework 114Architectural Standards 114Architectural Approach 118Platform and Integration 122Data Management Platform 122Application Integration 126Release Management 129Historical Data 132Data Quality 135Data Quality Framework 136Data Quality Strategy Development 136Data Quality Measurement and Analysis 141Data Quality Assurance 145Data Profiling 145Data Quality Assessment 149Data Quality for Integration 153Data Cleansing 1Appendix A: Standards and Procedures 5Quality Control 6Data Access 8Distribution 10Data Access 12Shared Service Utilization 14Technical Metadata 17Data Definitions 19Business Data Definitions 21Archive and Retention 23OnBoarding and KYC 25Entitlement and Permissioning 28Redistribution 30Business Continuity Plan/IT Security 32Record Creation and Change Management 34Vendor Strategy 36Audit and Compliance 38Management of Sensitive Data 40Data Precedence and Business Rules 42Data Transformation 44Appendix B: Glossary 1CATEGORY IData Management StrategyData Management Strategy ponents establish how data is managed, organized, funded, governed and embedded into the operational philosophy of the organization. It defines the longterm plan of action and illustrates how the various ponents are linked. The organization must ensure that all ponents of the data management strategy align.The Data Management Strategy category is prised of a collection of 5 ponents and 17 subordinate process areas as shown in the diagram below. Complete fulfillment of the Data Management Strategy requires execution of