Data Architecture

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Data Architecture

Data architecture comprises the design and maintenance of the enterprise information architecture. This includes development and implementation of policies, standards, processes and methodologies for identifying and defining what information is collected, and how it is stored, standardized and integrated throughout the enterprise. Data architecture activities include the development, use, and maintenance of artifacts such as diagrams of business processes and data flows and data models that support information systems development and maintenance.

Data architecture management is the foundational component for any information system. The artifacts produced from data architecture activities, such as use cases, DFDs, and data models, guide the remainder of information systems development.

Data architecture management, however, does not stop with systems development. It is an ongoing process. The artifacts produced from the requirements analysis stage live on past system implementation, and they play a vital role as communication documents for developers who make changes and upgrades to an information system at a future time.

            Ensuring that organizations have quality data starts with the beginning of the SDLC. If business and end user requirements are not adequately identified, documented, and translated into meaningful and accurately documented artifacts that guide the rest of the development cycle, then the developmental effort runs the risk of producing systems that have accessibility, usability, reliability, and data quality problem.

The following are among the functions of data architecture management:

• Establish standards, policies, and practices that support data architecture management functions
• Develop quality control metrics and practices for data architecture management
• Apply techniques, tools, and documentation such as, interview, observation, document analysis and Joint Application Development, for identifying and gathering functional and non-functional system requirements
• Apply techniques and tools such as use cases, data flow diagrams, and entity relationship diagrams for analysis of current and proposed systems
• Develop, maintain, and update conceptual, logical and physical database models
• Create, maintain, and update the data dictionary containing all metadata entity names, attributes, and relationships