Data dictionaries are a common feature of many database management systems (DBMSs). They provide documentation about the structure and metadata of a database, which can help in identifying problems or anomalies. They can also be a useful tool for developing reports and performing analysis on data.

Data dictionary entries are grouped into a number of categories, including entities, attributes, relationships, and policies. Entity entries include a description of the entity itself, its identification attributes, and its representation and control attributes. Relationships, on the other hand, define the relationship between two entities. They can also contain a variety of other information, such as a definition of subtypes and the names of the entities that are involved in the relationship.

Attribute definitions can contain details about the attribute’s type, data type, default value, and any constraints that have been placed on it. These details can relieve the designer from having to worry about specific SQL syntax and can simplify development by reducing the number of coding errors.

Representation and context units are a subset of metadata elements, which describe data in the form of text, numbers, or symbols. These units can be used to describe data in different contexts, such as a document in an external system or an image on a computer screen.

Unlike the other types of metadata defined by a DBMS, Representation and Context units are not mapped directly to data objects in the data model; they instead are linked to the entities in the Data Model. This distinction is important when the Data Model includes a repository system, as it allows the repository to preserve bits while still allowing the Representation and Context to be accessed.

The Representation and Context unit also provides a basis for the storage of information in a database, such as a unified identifier for a file or an image. It can also be used to identify a digital object within a repository, thereby facilitating retrieval and archival.

A unified identifier is important for a variety of reasons, such as ensuring that the same file or image can be used by multiple applications, and enabling data analysts to create composite views of a single source of information without requiring the creation of a separate record. It can also be used to create a virtual copy of the original file or image, which allows data archivists to easily preserve it in case it is lost.

Data dictionaries are an essential component of any enterprise’s BI infrastructure. They allow a centralized repository of the data schema and documentation required to support business processes. They can be used to present and share these documents across the organization or to build a business glossary that is a reference guide for defining the relationship between business processes and the data that supports them.

In addition to being a key component of any enterprise’s BI infrastructure, data dictionaries are also a critical part of data governance. They can be used to track and manage the quality of data sourced for a dimensional model.