Guide: Data Management

Understanding the Fundamentals of a Data Dictionary

Many people believe that data is today’s new oil. Of course, in today’s society, the issues over oil and other natural resources are not being replaced. However, there are similarities to what many believe is oil’s technical counterpart, data.

In today’s data-driven economy, an organization must manage a massive amount of data meticulously to derive valuable insights. Internally and externally derived data must undergo refinement and control to manage risks and minimize costs.

Moreover, ensuring a collection of standards, measurements, policies, and processes enables the effective and efficient use of data.

Regardless of where you stand on the drawn tangent between oil and data, there is no denying that data is an invaluable asset to the tech world and the businesses that utilize its information. So, for the data your company controls, it’s important to have a system that organizes it for optimal use.

In this regard, a data dictionary is a collection of detailed, comprehensive information about business data.

This article details the following:

What is a Data Dictionary?

The metadata of a database gets stored in a data dictionary. A data dictionary is a file or a series of files. This database dictionary stores information about other database items, such as data ownership data relationships, among other things.

A data dictionary is also called a metadata repository. A relational database’s data dictionary is a critical component of the database’s functionality. Specifically, a database data dictionary gives additional information about the relationships between different database tables, aids in the organization of data in a logical and easily searchable manner, and helps avoid data redundancy concerns in the database.

Interestingly, despite its significance, it is virtually invisible to most database users. Typically, only database administrators have access to the data dictionary database and can interact with it.

Why is a Data Dictionary Important?

Information on the contents of a dataset or database gets provided in data dictionaries, including names of measured variables, data types and formats, and text descriptions of the variables. Furthermore, a data dictionary serves as a brief reference to comprehending and utilizing the information.

Following that, below are some of the most significant advantages that answer the question: Why are data dictionaries important?

A Data Dictionary is Important in Detecting Anomalies Quickly

Data dictionary metadata can identify anomalies in data and information gaps quicker. It displays the results of data checks such as minimum and maximum values or the number of distinct values. A data dictionary makes it possible to identify duplicate, incorrect, or problematic data in one look.

A Data Dictionary Functions to Evaluate Data Quality

When using data dictionaries, generating a consistent set of variable names and descriptions across an organization is much easier. This asset assists you in automatically determining the quality of your data and makes data analysis more efficient and straightforward. Moreover, creating a data dictionary can assist you in evaluating data quality fast and expediting your research.

A Data Dictionary Helps to Get High-Level Information About Data

All information about a data set gets documented in one place (including the sources and owners and descriptions and discussions). The information about the data set becomes more reliable. With the help of data dictionaries, relying on data becomes more dependable.

A Data Dictionary Builds Transparency within Data Teams

When the entire business understands what each detail in a data set implies, it unifies the organization, lowers dependencies, enables everyone to use the data consistently, and simplifies onboarding.

Additionally, a DBMS data dictionary contains metadata, such as information about the database. The data dictionary is critical because it provides information on the contents of the database, who has access to it, and where the database gets physically housed.

Read more about How Stale Metadata Causes Data Projects to Fail

Components of A Data Dictionary

It is critical to evaluate the components of a data dictionary while preparing to develop one.

Generally, a data dictionary will include the following three components:

  • “Attribute Name,” which is the label assigned to the attribute.
  • “Optional or Required,” which indicates whether or not the information in a feature gets required before storing the record.
  • “Attribute Type,” which describes the type of data you may enter in the field.

Along with these components, you may choose to include additional notes on each piece of data. These notes may consist of the source of information the attribute’s location inside:

  • The table
  • The field name of the physical database
  • The length of the field
  • Any additional default settings.

In general, the following components are various types of metadata, providing information about data:

  • Business Rules
  • Data Object Listings
  • Data Element Properties
  • Entity-Relationship Diagrams
  • Missing Data and Quality-indicator Codes
  • Reference Data
  • System-Level Diagrams

By knowing the data dictionary in DBMS and having one in place, teams can easily comprehend the data included in a data warehouse. Because a data dictionary describes the metadata stored in a particular system, companies can isolate certain data for identification and interpretation.

Passive Data Dictionary vs. Active Data Dictionary

There are two types of data dictionaries depending on the level of automatic synchronization: Passive Data Dictionary and Active Data Dictionary.

Passive Data Dictionary

The Passive Data Dictionary gets equipped with storage to centralize metadata. Furthermore, the passive data dictionary does not affect the database’s structure, which implies that you can change the data dictionary structure without affecting the database’s structure.

However, one of the disadvantages of using a passive data dictionary is that it does not require any special software to update or modify the data dictionary itself.

Another disadvantage of this dictionary is that it requires a significant amount of maintenance and necessitates other teams’ involvement in the manual care of the database. If this process is not done or handled correctly, there is a risk that the database and data dictionary will become out of sync.

In the end, this data dictionary is not a viable option for many users due to its high level of maintenance.

Active Data Dictionary

On the other hand, an active data dictionary is a highly consistent type of dictionary managed automatically by the database management system.

When the DBMS modifies or updates the active data dictionary, this dictionary is likewise adjusted automatically by the DBMS. Any change or modification to the database structure can also be visible in the data dictionary due to the data dictionary’s functionality, which is the DBMS’s automatic updating of information.

This dictionary affects the database structure throughout the modification process or when we change some data in the database.

The advantage of this data dictionary is that it does not require any external maintenance software or tools and incurs no additional maintenance costs because the database management system administers it automatically.

Data Dictionary vs. Data Inventory

When discussing Data Dictionary, another concept almost always surfaces; a Data Inventory. Thus, it is important to learn about how they are connected and how they are different.

Data dictionaries include information regarding the naming and defining of data assets and other related information. Repositories get used to store these files, which assist in data engineering tasks. Moreover, it becomes possible to identify data assets found in certain databases or data pipelines by searching for their names, settings, and other key features within these dictionaries of data assets.

When used properly, data dictionaries can assist in preventing inconsistencies and conflicts in data assets when used in project implementation. They also make it possible to define responsibilities and operate clearly and easily while maintaining consistency in enforcing those roles and uses.

On the other hand, a data inventory is a consolidated metadata collection that contains information about all of the datasets that an organization gathers and manages. This document, or collection of documents, identifies the location of each dataset and the type of data that it contains.

This inventory has a practical purpose in that it enables data analysts to establish what data is available to them and how they may access it. Data stewards are responsible for maintaining these data inventories and defining the data access regulations that apply to each dataset.

Ultimately, a data inventory lists all datasets currently available in your company, as well as all of the associated metadata. Now, the criteria for those datasets are laid down in a data dictionary, which indicates their right format, structure, and schema, in addition to other information.


Up-to-date data dictionaries and data inventories are essential components of successful data governance strategies. These provide efficient and effective data interactions, allowing teams to streamline processes and acquire important insights from data much more quickly.

In the end, a robust data governance architecture makes it possible to have easy data accessibility, data confidence, data understanding, data activation, and data delivery while also maintaining data security. Because a data dictionary serves as a centralized repository for metadata, the efficiency with which it gets maintained determines the efficiency with which data governance gets conducted.

Satori helps you build a continuously updated data inventory, including up-to-date classification of sensitive data.

To read more about Satori:

Last updated on

April 4, 2022

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