Data Dictionary

What is a Data Dictionary?

A data dictionary provides detailed technical specifications about data elements, structures and attributes within a specific data source, such as a database or data warehouse. This could include the data type, default values, origin, or its relationship with other data assets. It essentially provides the metadata about the database itself.

A data dictionary is a central repository that provides a reference guide for technical data management staff, such as data professionals, database administrators and developers. The data dictionary helps build and enforce a shared understanding across the organization at a technical level to ensure consistency of data management. It ensures that all technical staff are clear on what specific variable names mean or what sort of values a field should contain.

The two types of Data Dictionary

Data dictionaries can be either active or passive:

• Active: tied to a specific database, and that automatically updates with the database. This makes management simpler, but means that terms in the data dictionary may not be consistent across different data sources.
• Passive: independent of specific databases, meaning it has to be manually managed and updated. While this adds to workloads, it does allow a consistent data dictionary to be applied across multiple data sources.

What does a Data Dictionary contain?

A data dictionary can include:

• A listing of data elements (names, definitions, purpose)
• Detailed properties of these data elements (type, size, values and allowed ranges, etc)
• Reference data (classification and descriptive domains)
• Data source (data warehouse, data lakes, databases, applications)
• Metadata around owners and editors of data assets, creation date, and the date/time it was last updated
• Relationships (how data elements are related)
• Usage information within the organization
• Physical information about where data is stored

How is a Data Dictionary used?

Data dictionaries improve data management across the organization by providing a documented, consistent language for technical data management, across departments. They enforce data standards, such as around metadata. As each data dictionary covers a specific database or data warehouse, it is possible to have multiple data dictionaries for different data sources.

Data dictionaries avoid confusion and potential errors between teams by ensuring that all technical staff understand the structure and relationships of data within a database or information system.

What are the benefits of a Data Dictionary?

Data dictionaries deliver six key benefits:

1. They increase collaboration between teams working on the same data by providing a common language
2. They improve efficiency and decision-making by standardizing data terminology
3. They make data easier to integrate and analyze
4. They enhance data governance by enforcing standards
5. They improve data quality and accuracy through clear rules for data entry and management
6. They improve data security and compliance by defining and enforcing data access rules and permissions

What are the challenges to creating an effective Data Dictionary?

Data dictionaries deliver major benefits around consistent technical data management. However, organizations face two key challenges when creating effective data dictionaries over the long-term:

Increasing volume of data

As new sources of data or new data elements are added to the data stack, the data dictionary needs to be updated to preserve integrity. This maintenance can be time-consuming and resource-intensive, although automation tools are now available to assist in the process.

Complexity

Data stacks and individual databases are becoming more complex, and project teams are growing. This can make it hard to document individual data elements within data dictionaries, and ensure that they are being found and used consistently across the organization. Whether active or passive, the data dictionary needs to be kept synchronized with the current database structure and content to avoid conflicts or inconsistencies.

What is the difference between a Data Dictionary and a Data Glossary?

Data dictionaries and data glossaries are both key to successful data governance. However, they are different and should not be confused:

• Data dictionaries are repositories of technical terms used to describe data elements, and are used and managed by IT.
• Data glossaries are repositories of business terms used to describe data within an organization. For example, they might cover what the term “customer” actually means in the context of data.

Read our entry on what a data glossary is to learn more.