{"id":3419,"date":"2020-01-20T21:08:41","date_gmt":"2020-01-21T02:08:41","guid":{"rendered":"http:\/\/www.dcamportal.org\/?p=3419"},"modified":"2021-09-27T09:04:31","modified_gmt":"2021-09-27T13:04:31","slug":"dcam-framework-7-data-control-environment","status":"publish","type":"post","link":"https:\/\/staging.dcamportal.org\/fr\/dcam-framework-7-data-control-environment\/","title":{"rendered":"DCAM Framework – 7.0 Data Control Environment"},"content":{"rendered":"
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Upper Matter<\/h2>\r\n <\/div>\r\n\r\n
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Introduction<\/h3>\r\n\r\n

The Data Control Environment<\/b> refers to the state of operation in which the data assets of an organization are holistically managed throughout the organization. There are three elements of a successful data control environment.<\/p>\r\n\r\n

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  1. The data management (DM) objectives and capabilities described within this document have been embraced and adopted throughout the organization.<\/li>\r\n
  2. The data lifecycle<\/span> is fully supported by all stakeholders. These stakeholders ensure understanding, awareness and control of data throughout the data supply chain–from source to consumption to disposition.<\/li>\r\n
  3. DM is part of the organization’s data ecosystem. It is integrated and coordinated with all other control functions organization-wide.<\/li>\r\n\r\n <\/ol>\r\n\r\n

    The purpose of the data control environment is to coordinate the people, process<\/span> and technology of DM into a cohesive operational model<\/span>. The data control environment defines the mechanisms used to capture data requirements, unravel data flows and linked processes and determine how data is to be delivered to the data consumer<\/span>. The data control environment supports the data lifecycle<\/span>. It ensures that proper resources and controls are in place as data moves throughout its journey. Also, the data control environment ensures collaboration and alignment to cross-organizational control functions. Areas such as Information Security, Data Privacy and Change Management must operate in sync with DM to ensure data is properly managed across all business functions.<\/p>\r\n\r\n

    To the extent that the data control environment is not achieved it results in potential data risk. Data risk should be managed in alignment with the overall risk management framework of the organization. Data risk scope includes areas such as data architecture<\/span> risks, metadata<\/span> risks, data quality<\/span> risks, data governance risks and Master Data<\/span> risks.<\/p>\r\n\r\n

    Definition<\/h3>\r\n\r\n

    The Data Control Environment (DCE)<\/b> component is a set of capabilities that together form the fully operational data control environment. Data operations, supply-chain management, cross-control function<\/span> alignment and collaborative technology architecture<\/span> must operate cohesively to ensure the objectives of the DM initiative are realized across the organization.<\/p>\r\n\r\n

    Scope<\/h3>\r\n\r\n
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    • Work with DM Program Management Office<\/span> (PMO<\/span>) to design and implement sustainable business-as-usual processes and routines to enable a successful data control environment.<\/li>\r\n
    • Bring together the DM components as a coherent, end-to-end data ecosystem.<\/li>\r\n
    • Follow current DM best practices by routinely reviewing and auditing the capabilities and their processes.<\/li>\r\n
    • Ensure all facets of DM for business-critical data such as data lifecycle<\/span>, end-to-end data lineage<\/span> and data aggregations are fully operational.<\/li>\r\n
    • Ensure DM is aligned with other control function<\/span> policies, procedures, standards, and governance.<\/li>\r\n\r\n <\/ul>\r\n \r\n\r\n

      Value Proposition<\/h3>\r\n\r\n

      Organizations that improve their ability to reconcile requirements for data and accurately share data across the organization’s ecosystem are able to better respond to changes in business process<\/span>, regulatory, and audit requirements.<\/p>\r\n\r\n

      Organizations that deliver data through a controlled environment respond more rapidly to market opportunities and provide innovation to customers<\/span>.<\/p>\r\n\r\n\r\n

      Overview<\/h3>\r\n\r\n

      The DCE is where the execution components of Business & Data Architecture<\/span>, Data & Technology Architecture<\/span>, Data Quality<\/span> Management and Data Governance are made operational in the data supply chain by the data producer<\/span>. This operationalization brings a defined set of data into control and makes it available to data consumers at a point in time, either real-time or period end.<\/p>\r\n\r\n

      \"Domain<\/p>\r\n\r\n

      Diagram 7.1: Data Supply Chain<\/p>\r\n\r\n

      One of the first functions within the DCE is the orchestration of the DM component disciplines. These disciplines must be aligned to effectively manage data across the organization. The DCE forces the alignment of all the capabilities discussed in this model<\/span> into a consistent operational flow. Each capability must be properly resourced and prioritized as well as supported by business, data and technology senior management.<\/p>\r\n\r\n

      The successful coordination of these components is a determining factor in the success of the DM initiative. It is the responsibility of the DM organization and the senior data officer<\/span> at each level of the organization to structure and coordinate the DM operating model<\/span>. This properly defines data meaning, ensures data quality<\/span> (DQ), and delivers data in a timely and efficient manner. Evidence of the processes must be compiled through demonstration of organizational structures, charters, policies, and senior management directives.<\/p>\r\n\r\n

      Data is a core factor of input into business functions and operational processes. The data lifecycle<\/span> tracks the progress of data from source to storage, to maintenance, to distribution and to consumption. From this point the data may be reused, sent to the archive and finally to defensible destruction. The mechanisms used to identify, align, and validate the data as factors of input into business functions are derived by reverse engineering existing business processes into their individual data elements and by unraveling the data assembly processes used to create the required data sets.<\/p>\r\n\r\n

      This reverse engineering process<\/span> to define data requirements needs to be managed with precision. Only precision will avoid confusion and miscommunication between what the business users truly need for their business function<\/span> and what technology professionals need for technical implementation. Data requirements should be modeled, aligned with business meaning, prioritized in terms of how critical it is to the business process<\/span> and verified by all stakeholders. These steps ensure that essential concepts are not lost in translation. This is particularly critical for data that is shared among multiple data consumers and for core data attributes that are used as a baseline for onward expression in operational calculations or business formulas.<\/p>\r\n\r\n

      For complex applications and for all data aggregation-related processes, it is essential to understand and document how the data moves from system-to-system; how the data is transformed or mapped; and how the data is aligned to business definition and standard<\/span> meaning. Gaining agreement on this data lineage<\/span> process<\/span> is fundamental for ensuring that the results of decentralized or linked processing can be trusted to be consistent and comparable.<\/p>\r\n\r\n

      The final aspect of an effective DCE is the integration of DM into the data ecosystem of an organization. The data ecosystem is a concept that describes how data is managed collaboratively with all cross-organization control functions. Control functions such as information security, storage management, legal and compliance, privacy, and vendor management all have responsibilities on how data is managed. It is imperative that the policies of DM are integrated and aligned with the policies of the cross-organization control functions to ensure data is being managed consistently and holistically organization-wide.<\/p>\r\n\r\n

      Finally, a DCE ensures technology’s alignment with DM policies and best practices. DM capabilities such as architecture, governance, and DQ should be integrated into the organization's Software Development Lifecycle (SDLC) processes to ensure that DM considerations are being adequately addressed at the appropriate stages of the development cycle. Nothing should operate in a silo. Operating within an ecosystem recognizes interdependencies and ensures collaboration.<\/p>\r\n\r\n\r\n

      Processes, Tools, & Constructs<\/h3>\r\n\r\n
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      • Business Process<\/span> Integration<\/li>\r\n
      • Technology Roadmap Integration<\/li>\r\n
      • Regulatory Requirements Integration<\/li>\r\n
      • Control Function<\/span> Requirements Integration<\/li>\r\n
      • Audit\/Compliance Requirements Integration<\/li>\r\n
      • Data Supply Chain Management Construct<\/span><\/li>\r\n
      • Capability Optimization<\/li>\r\n\r\n
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        • RACI Matrix<\/span><\/li>\r\n
        • Process<\/span> Designs and End-to-End Process<\/span> Integration<\/li>\r\n
        • Procedures Guide<\/li>\r\n
        • Process<\/span> Performance Measurement<\/li>\r\n\r\n <\/ul>\r\n\r\n <\/ul>\r\n\r\n\r\n

          Core Questions<\/h3>\r\n\r\n
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          • Is the concept of a DCE understood by stakeholders?<\/li>\r\n\r\n
          • Are the aspects of data control like terms, definitions, relationships, integration and precedence established on a consistent basis?<\/li>\r\n\r\n
          • Are control processes applied across the full data lifecycle<\/span>?<\/li>\r\n\r\n
          • Are the concepts of data control aligned across the full organizational ecosystem?<\/li>\r\n\r\n <\/ul>\r\n\r\n\r\n\r\n