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What are the components of the data dictionary?

A data dictionary is a tool used by technical teams within companies that provides a description for different metadata variables within a dataset. It is most commonly organized in the format of a spreadsheet. Every data attribute (or field) is marked as a row and each column contains applicable information to know about the specific attribute. Typically, a data dictionary will contain three main elements. There is “Attribute Name,” which is the label designated to the attribute. “Optional or Required” which shows if the information is required in an attribute before saving the record. Lastly, there is “Attribute Type” which explains what type of data is allowed to be entered in the field.

Along with these elements, you may also note other information about each piece of data. This might include where the information is sourced from, where the attribute can be found within the table, the field name of the physical database, the length of the field and any other values that are considered default.

By having a data dictionary in place, BI teams will be able to understand the data available in a data warehouse. Since a data dictionary defines the metadata stored in a specific system, you can isolate certain data to analyze and understand its impact. Within the data dictionary, there is a singular source of reference for various data attributes, such as: business definitions, constraints, data type, default values, length and transformation regulations. In addition to allowing metadata to be instantly located and identified, a data dictionary also assists in creating dashboards and reports.

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