A data dictionary is a collection of the names, definitions, and attributes for data elements and models. The data in a data dictionary is the metadata about the database. These elements are then used as part of a database, research project, or information system. These are some of the most common elements used in a data dictionary, though there’s variation:
- Attribute name
- Attribute type
- Reference data
- Rules for validation, schema, or data quality
- Detailed properties of data elements
- Physical information about where data is stored
There are two types of data dictionaries: active and passive. An active data dictionary is tied to a specific database which makes data transference a challenge, but it updates automatically with the data management system. A passive data dictionary isn’t tied to a particular database or server, but it also must be manually maintained to prevent metadata from being out of sync.
Why Data Dictionaries Are Important
The main reason companies use data dictionaries is to document and share data structures and other information for all involved with a project or database. Using a shared dictionary ensures the same quality, meaning, and relevance for all data elements for all team members. The data dictionary will define conventions for the project and consistency throughout the dataset. Without a data dictionary, there’s a higher risk of losing crucial information in translation and transition. Using a data dictionary also helps teams analyze the data easier later on.
How to Create a Data Dictionary
Many businesses rely on database management systems (DBMS), and these systems most often have built-in active data dictionaries. Documentation can be generated with SQL, Server, Oracle, or mySQL. To create a passive data dictionary, analysts will need to build one separately from a DBMS since passive dictionaries aren’t managed by a management system. SQL, Server, and Oracle can be used to build a dictionary, and there’s even a template in Excel. The easiest way to integrate a dictionary is to use it as part of a DBMS.
Challenges with Data Dictionaries
A data dictionary benefits analysts by making a database consistent and simplifying the data analysis process. But a data dictionary on its own only carries consistency and standardization so far. Without data preparation, data dictionaries can be time consuming to create or only standardize part of a database or project. So while the data elements are consistent with a dictionary, that’s only one part of preparing data for the actual analysis process. And data preparation on a large scale—including as part of a data dictionary—can be time consuming, leaving many businesses in a data lurch.
Data Preparation with a Data Dictionary
The future of the data dictionary is to combine it with data preparation to save teams time and resources and to make a project consistent across the board. When data dictionaries are integrated into a data preparation system, the two work together to make consistency efficient and simpler for data analysts.
Trifacta provides efficient and effective data preparation that’s easily accessible for people in a variety of industries. To see more about how to use Trifacta, request a demo.