DMM数据成熟度模型.docx

Data Management Maturity ModelDMM Definition of the components, business processes and capability areas required for certification of data management effectivenessAboutEnterprise Data Management CouncilThe EDM Council is a non-profit and trade association created by the financial industry to elevate data management as an essential business mandate. The Council is focused on the creation of a standards-based infrastructure for effective data management and the development of best practices associated with data management implementation in operational environments. For more information, visit .Carnegie Mellon University-Software Engineering Institute (SEI)The SEI is a U.S. Department of Defense federally funded research and development center operated by Carnegie Mellon University. The SEI helps organizations make measured improvements in their software engineering capabilities by providing technical leadership to advance the practice of software engineering. For more information about the SEI, visit . For more information about the SEI Partner Network, visit /partners.Booz Allen HamiltonBooz Allen Hamilton is a leading provider of management and technology consulting services to the U.S. government in defense, intelligence, and civil markets, and to major corporations, institutions, and not-for-profit organizations. Booz Allen is headquartered in McLean, Virginia, employs more than 25,000 people, and had revenue of $5.59 billion for the 12 months ended March 31, 2011. For more information, visit .IntroductionBackgroundThe Enterprise Data Management Council has entered into a strategic partnership with the Software Engineering Institute (SEI) of Carnegie Mellon University (CMU) to create a detailed and auditable data management maturity (DMM) model. The DMM defines the components of data management at the specific business-process level so that organizations can assess themselves against documented best practices and improve their management of data resources across functional, business unit and geographic boundaries. The core DMM content model was developed via collaboration among data management practitioners, operational managers, IT professionals and representatives of various lines-of-business. The components and incremental capability measurement criteria are based in practical reality of what is required to achieve alignment on strategy, implement governance mechanisms, manage operational components, define dependencies, align with IT capabilities, ensure data quality and integrate trusted data into downstream applications.The Data Management Maturity (DMM) models overall goal is to help organizations become more proficient in their management of critical data and to provide a consistent and comparable benchmark for regulatory authorities in their efforts to control operational risk. The DMM model is constructed based on the foundational principles of the Capability Maturity Model Integration (CMMI) developed and managed by SEI for more than 20 years. The proven framework of the CMMI has helped guide thousands of organizations worldwide through improvement activities resulting in lowered risk, increased predictability and performance, and increased profitability. About The DMMThe DMM model is a process management and improvement maturity model for the development and management of data and data services. It consists of best practices that address the lifecycle of data management from creation through delivery and maintenance. While the development of the DMM model was rooted in the financial community, the practices related to data management are extensible and applicable to any industry or management objective. The model presents an organized set of practices and goals necessary for the management of data as an asset through increasingly more robust and disciplined practices.The DMM has roots the Capability Maturity Model Integrated (CMMI) and leverages proven process areas from the 20-plus years of experience related to CMMI. The DMM defines what is required to achieve alignment on strategy, implement governance mechanisms, manage operational components, define dependencies, align with IT capabilities, ensure data quality and integrate data into business processes. It does not prescribe how organizations must do something, but rather what they must do in order to achieve high capabilities or maturity of data management. By providing a structured and standard framework of practices, the DMM can be leveraged by organizations to build their own roadmap to data management maturity. The DMM has an accompanying standardized methodology for conducting objective appraisals of capability and maturity levels within the organizations data management practice. AudienceOrganizations and agencies interested in improving or assessing their data management practice from establishing strategic objectives through governance and operational management to quality data outputshould use this model. The DMM provides a framework and accompanying assessment methodology for evaluating the efficiency of data management practices, measuring the maturity of operational integration, and establishing standard best practices that can be adopted by organizations worldwide. A standardized assessment methodology enables organizations to benchmark themselves against best practices for peer-to-peer and internal evaluation. Regulators and business users can also use the DMM to evaluate the capability and maturity of organizations in the production, management and use of data. The model is structured to guide practitioners through the myriad of activities that are necessary to achieve sound data management practices.DMM FrameworkThe core of the model is comprised of 37 process areas which serve as the principle means to communicate the goals, practices, and typical work products of the model. It is through the execution of the practices (for accomplishment of the goals) that the purpose of each process area is achieved. Accomplishment of goals within each of the individual process areas allows an organization to achieve capabilities and maturity of data management. The process areas of the model are aligned under 11 model components, which in turn are organized under 4 broad categories. The structure of the model can be seen in Figure 1 on the following page.Figure 1. DMM ConstructOrganization of the DMM TextThe organization of categories and their associated components and process areas provide a logical business framework of related activities. The text presents these components and process areas consolidated under the top four categories. Figure 2 shows how these top-level categories (strategy, operations, platform/architecture and data quality) work together to achieve fundamental activities. The DMM makes reference (and includes a link) to a set of supporting process areas that contain common practices used across all categories.Figure 2. DMM CategoriesTable 1 shows the organization of the categories, components, and process areas of the DMM.Table 1 - Categories, Components and Process AreasCategoryComponentProcess AreaData Management StrategyData Management GoalsData Management ObjectivesData Management PrioritiesScope of Data Management ProgramCorporate CultureAlignmentCommunications StrategyGovernance ModelGovernance StructureOrganization ModelOversightGovernance Implementation & ManagementHuman Capital RequirementsMeasurement Data Management FundingTotal Lifecycle Cost of OwnershipBusiness CaseFunding ModelData Requirements LifecycleData Requirements DefinitionOperational ImpactData Lifecycle ManagementData Management OperationsStandards and ProceduresAreasPromulgationBusiness Process and Data FlowsData Dependencies LifecycleOntology and Business SemanticsData Change ManagementData SourcingData Sourcing RequirementsProcurement & Provider ProcessPlatform and ArchitectureArchitectural FrameworkArchitectural StandardsArchitectural ApproachPlatform & IntegrationData Management PlatformApplication IntegrationRelease ManagementHistorical DataCategoryComponentProcess AreaData QualityData Quality FrameworkData Quality Strategy DevelopmentData Quality Measurement & AnalysisData Quality AssuranceData ProfilingData Quality AssessmentData Quality for IntegrationData CleansingSupport Process AreasConfiguration ManagementMeasurement and AnalysisRequirements ManagementRisk ManagementSupplemental information is provided in the appendices. Appendix A presents a review of the types of standards and procedures. These are organized along functional lines and present information that is typically documented by the organization to provide the necessary guidance to the operations of data Management. Appendix B (under development) will present a glossary of key words and terms used within the DMM. Capability and Maturity LevelsThe DMM presents six levels of capability and maturity. Capability is measured according to each process area, whereas maturity is measured by component and rolled up to the category level. Each level is characterized by increasing attributes of practices. Table 2 below provides a synopsis of the definitions and characteristics of each level.Table 2 Capability/Maturity Level DefinitionsLevelDescriptionSummaryLevel 0: IncompleteProcesses are either not performed or partially performed, and any that do exist are typically in a state of dynamic change. Individuals perform data management tasks - no formal goals exist.Not performed or performed on an ad hoc basis. Level 1: PerformedProcesses are ad hoc and localized to projects. There are no unified processes across areas. Process discipline is unlikely to be rigorous with emphasis on data repair. Performance may not be stable and may not meet specific objectives such as quality, cost, and schedule. There may be improvements, but there is no mechanism to ensure that improvements are maintained.Reactive or partial data management performed informally (inconsistent) at the local/business unit levelLevel 2: ManagedProcesses are planned, performed, monitored, and controlled to achieve a given purpose or in reaction to a specific need. There is an infrastructure in place to support the processes at a business unit level. Clear roles and responsibilities have been defined.Processes are defined and documented but performed at the business unit level on a sporadic basisLevel 3: DefinedSets of standard processes have been established and improved over time. Processes to meet specific needs have been tailored from the set of standard processes according to the organizations guidelines and provide a predictable measure of consistency.Processes are defined, managed and orderly with implementation consistently applied at the organizational levelLevel 4: MeasuredManaged and measured process metrics have been established. There are formal processes for managing variances. Quality and process performance is understood in statistical terms and is managed throughout the life of the process.Processes are structured, aligned, adopted and traceable with consistent measurement analytics at the organizational levelLevel 5: OptimizedThe focus is on continually improving process performance through both incremental and innovative improvements. Feedback is used to drive process enhancements and business growth.Processes are managed on a continuous basis and advocated at the executive management levelCapability MeasurementCapabilities are measured within each process area by performing practices to achieve varying 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 component or category are at a common minimum, as well as full implementation of the support processes that are defined separately from any one particular component. 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 ExampleAcknowledgementThis 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 art 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 Ben-Hur, Senior Manager, Deloitte and Touche; John Bottega, Chief Data Officer Enterprise Change, Technology and Operations, Bank of America; John Carroll, Managing Consultant, element-22; Predrag Dizdarevic, Principal, element-22; 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 Table of ContentsAboutiiIntroductioniiiBackgroundiiiAbout The DMM, What it isiiiAudienceivDMM FrameworkivOrganization of the DMM TextviCapability and Maturity LevelsviiiCapability MeasurementixMaturity MeasurementxData Management Strategy15Data Management Goals16Data Management Objectives16Data Management Priorities20Scope of Data Management Program23Corporate Culture27Alignment27Communications Strategy31Governance Model35Governance Structure35Organizational Model39Oversight43Governance Implementation and Management46Human Capital Requirements50Measurement54Data Management Funding58Total Lifecycle Cost of Ownership58Business Case62Funding Model66Data Requirements Lifecycle70Data Requirements Definition70Operational Impact74Data Lifecycle Management77Data Management Operations80Standards and Procedures81Areas81Promulgation85Business Process and Data Flows88Data Dependencies Lifecycle91Ontology and Business Semantics95Data Change Management99Data Sourcing105Sourcing Requirements105Procurement & Provider Management109Platform and Architecture113Architectural Framework114Architectural Standards114Architectural Approach118Platform and Integration122Data Management Platform122Application Integration126Release Management129Historical Data132Data Quality135Data Quality Framework136Data Quality Strategy Development136Data Quality Measurement and Analysis141Data Quality Assurance145Data Profiling145Data Quality Assessment149Data Quality for Integration153Data Cleansing1Appendix A: Standards and Procedures5Quality Control6Data Access8Distribution10Data Access12Shared Service Utilization14Technical Metadata17Data Definitions19Business Data Definitions21Archive and Retention23On-Boarding and KYC25Entitlement and Permissioning28Redistribution30Business Continuity Plan/IT Security32Record Creation and Change Management34Vendor Strategy36Audit and Compliance38Management of Sensitive Data40Data Precedence and Business Rules42Data Transformation44Appendix B: Glossary1CATEGORY IData Management StrategyData Management Strategy components establish how data is managed, organized, funded, governed and embedded into the operational philosophy of the organization. It defines the long-term plan of action and illustrates how the various components are linked. The organization must ensure that all components of the data management strategy align.The Data Management Strategy category is comprised of a collection of 5 components and 17 subordinate process areas as shown in the diagram below. Complete fulfillment of the Data Management Strategy requires execution of all practices (at the appropriate level) across all 22 aspects of this category, shown in Figure