![]() ![]() Most database management systems (DBMS) have built-in, active data dictionaries and can generate documentation as needed ( SQL Server, Oracle, mySQL). Learn more about naming conventions and find guides to writing column descriptions at Best Practices for Data Dictionary Definitions and Usage and Captain Obvious' Guide to Column Descriptions - Data Dictionary Best Practices. Although getting critical feedback about their data may be initially troublesome for some data creators, developing good data design and description habits is worth the effort and ultimately benefits everyone who will use the data. Poor table organization and object naming can severely limit data understandability and ease-of-use, incomplete data definitions can render otherwise stellar data virtually useless, and failure to keep the dictionary up to date with the actual data structures suggests a lack of data stewardship. The Alaska Science Center Research Data Management Plan has excellent examples of a Data Description Form and other forms to capture metadata before, during, and at the end of a project.ĭata Dictionaries Can Reveal Poor Design Decisionsįor both data reviewers and data users, the data dictionary can reveal potential credibility problems within the data. The easiest path is to adopt and cite a data standard, thus avoiding the need to provide and manage your own documentation. Try to use naming conventions appropriate to the system or subject area. When data structures change, update the dictionary. As required and optional data elements are identified, add them to the data dictionary. Plan ahead for storing data at the start of any project by developing a schema or data model as a guide to data requirements.
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