【正文】 nd Process Executive, Chartis Insurance。 Jeff Gorball, Managing Director, Kingland Systems。 John Carroll, Managing Consultant, element22。 Roy BenHur, Senior Manager, Deloitte and Touche。 IntegrationData Management PlatformApplication IntegrationRelease ManagementHistorical DataCategoryComponentProcess AreaData QualityData Quality FrameworkData Quality Strategy DevelopmentData Quality Measurement amp。 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 amp。 Draft copy for review by EDM Council members onlyOrganization of the DMM TextThe organization of categories and their associated ponents and process areas provide a logical business framework of related activities. The text presents these ponents and process areas consolidated under the top four categories. Figure 2 shows how these toplevel 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 mon practices used across all categories.Figure 2. DMM CategoriesTable 1 shows the organization of the categories, ponents, 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 amp。The Data Management Maturity (DMM) model’s overall goal is to help organizations bee more proficient in their management of critical data and to provide a consistent and parable 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 munity, 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 20plus years of experience related to CMMI. The DMM defines what is required to achieve alignment on strategy, implement governance mechanisms, manage operational ponents, 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 acpanying standardized methodology for conducting objective appraisals of capability and maturity levels within the organization’s 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 output—should use this model. The DMM provides a framework and acpanying 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 peertopeer 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 prised of 37 process areas which serve as the principle means to municate the goals, practices, and typical work products of the model. It is through the execution of the practices (for acplishment of the goals) that the purpose of each process area is achieved. Acplishment 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 ponents, 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 Constructvii169。 The ponents and incremental capability measurement criteria are based in practical reality of what is required to ac