{"id":3333,"date":"2019-12-12T07:22:43","date_gmt":"2019-12-12T12:22:43","guid":{"rendered":"http:\/\/www.dcamportal.org\/?p=3333"},"modified":"2021-09-27T08:48:57","modified_gmt":"2021-09-27T12:48:57","slug":"dcam-framework-0-introduction","status":"publish","type":"post","link":"https:\/\/staging.dcamportal.org\/fr\/dcam-framework-0-introduction\/","title":{"rendered":"Cadre DCAM \u2013 Introduction"},"content":{"rendered":"
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Introduction<\/h2>\r\n\r\n

The Data Management Capability Assessment Model<\/span> (DCAM<\/span>®) defines the scope of capabilities required to establish, enable and sustain a mature data management (DM) discipline. It addresses the strategies, organizational structures, technology and operational best practices needed to successfully drive DM. It addresses the tenets of DM based on an understanding of business value combined with the reality of operational implementation.<\/p>\r\n\r\n

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

The concept of data as a foundational aspect of business operations has arrived. It is now widely understood as one of the core factors of input into the full range of business and organizational processes. An organization that is effective in its use of data is one which implements and manages a data control environment. The data control environment yields a wide range of bottom-line benefits to the organization including reduced operational costs, automated manual processes, consolidated redundant systems, minimized reconciliation and enhanced business opportunities. An organization implements a data control environment to ensure trust and confidence in the data they are relying on for business processing and decision-making. This eliminates the need for manual reconciliation or reliance on data transformation<\/span> processes.<\/p>\r\n\r\n

The data control environment concept ensures the data is precisely defined, described using metadata<\/span>, aligned with meaning, and managed across the full data lifecycle<\/span>. The key to first establishing a data control environment, however, is the achievement of unambiguous shared meaning across the organization along with the governance required to ensure precise definitions. Data must be consistently defined by the real thing it represents such as, products, clients, customers<\/span>, legal entities, transactions, events and much more. All other processes are built upon this foundation.<\/p>\r\n\r\n

In a fragmented data environment, which is the opposite of a data control environment, application development can result in ad hoc data models which exacerbate the problem of common terms that have different meanings, common meanings that use different terms and vague definitions that don’t capture critical nuances. For many organizations, this challenge can be debilitating because there are thousands of data elements, delivered by hundreds of internal and external sources, all stored in dozens of unconnected databases. This fragmentation results in a continual challenge of mapping, cross-referencing and manual reconciliation. To achieve a data control environment, every data attribute<\/span> must be understood, at its atomic level, as a fact that is aligned with specific, durable business meaning without duplication or ambiguity. Managing data as meaning is the key to the alignment of data repositories, harmonization of business glossaries and ensuring that application data dictionaries are comparable.<\/p>\r\n\r\n

Achieving alignment of business meaning, including the process<\/span> of how terms are created and maintained, can be a daunting task. It is not uncommon to experience resistance from internal business and technology users–particularly when there are multiple existing systems linked to critical business applications. The best strategy for reconciliation in a fragmented environment is to harmonize based on legal, contractual or business meanings rather than trying to get every system to adopt the same naming convention. Nomenclature represents the structure of data and unraveling data structures and data models is expensive and not necessary. It is better to focus on precisely defining business concepts, documenting transformation processes and capturing real-world data relationships. Once established, existing systems, glossaries, dictionaries, repositories, etc. can be cross-referenced to the common meaning.<\/p>\r\n\r\n

Data as meaning must be managed along with its defining metadata<\/span> to ensure consistency<\/span> and comparability across the organization. Data meaning and \n metadata management<\/span> must be understood as the core of your content infrastructure and the baseline for process<\/span> automation, application integration, and alignment across linked processes. Some common types of metadata<\/span> include business, operational, technical, descriptive, structural, administrative.<\/p>\r\n\r\n

The implementation and management of a data control environment are governed by data policies, standards, processes and procedures. These are the essential mechanisms for establishing a sustainable DM initiative and for ensuring compliance with a data control environment in the face of organizational complexity. Managing meaning is the key to effective DM. Meaning is achieved through the adoption of semantic standards. Standards are governed by policy<\/span>. The policy<\/span> is established by executive management, supported by data owners and enforced by Internal Audit.<\/p>\r\n\r\n

Challenges in Creating a Data Control Environment<\/h3>\r\n\r\n

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

Diagram 0.1: Data Control Environment Challenges<\/p>\r\n\r\n

Achieving a data control environment requires the organization to overcome the following challenges.<\/p>\r\n\r\n