Data Dictionaries: A Comprehensive Guide

Data dictionaries are an invaluable tool for any data-driven organization, but they can often seem like a complex and daunting task to build. Not only do you need to understand the definition of a data dictionary — you also have to know its components, benefits and how to create one.

In this article, we'll cover everything about data dictionaries — from beginning to end — so that you'll have a good foundation of what a data dictionary is for.

Read on for a detailed guide!

What is a data dictionary?

A data dictionary is a structured repository of metadata that provides a comprehensive description of the data used. Its main purpose is to provide a common language and understanding of:

  • The data
  • Its meaning
  • How it relates to other data elements

To put things simply, a data dictionary provides additional context and information about each data point so that analysts can understand the data better.

Types of data dictionaries

In general, when working with data dictionaries, they can be split into two different types: active and passive.

Active data dictionary

An active data dictionary is a document that's updated whenever changes are made to the data in a database.

Usually managed by the IT department, this type of data dictionary is and provides up-to-date definitions for each piece of data in a database or system. This form of data dictionary actively prevents any discrepancies or changes in data integrity.

Passive data dictionary

A passive data dictionary is usually a static document that's manually updated and not tied to any system or database. This type of data dictionary is typically used for reference purposes, such as in analytics projects where analysts need to understand the meaning of different data points and their relationships with each other.

Since passive data dictionaries are not created within databases automatically, they are highly prone to discrepancies whenever changes are made to databases. However, since these static documents are only for reference used by analysts, they are still used for quick communication in a more ad-hoc manner.

When I have worked as a data analyst, I took on the task of building and maintaining a basic passive data dictionary that I share with my data analyst co-workers. Although it was prone to error, it provided much greater clarity when doing exploratory data analysis to understand the data better.

Components of a data dictionary

A data dictionary can be broken down into several basic components:

  • Data Element Name: This is the name of the data element.
  • Data Type: This describes the type of data that can be stored in a field, such as text or numeric.
  • Domain Value: A domain value defines what values can be used for a particular data element.
  • Definition/Description: This explains the data element, its purpose and its context.
  • Source: This describes where the data element is sourced from.
  • Date Created: This records the date when the data element was created.
  • Last Updated: This records the date when the last updates were made.
  • Approved By: This is a record that shows who approved the data element.
  • Owner: This is a record that shows who is responsible for maintaining and updating the data element.
  • Relationships: This describes how the data element relates to other elements within the system or database.
  • Validation rules: This describes any business rules that need to be applied to the data element.

These are just some common components a data dictionary should have. Each data dictionary is different based on the needs of the business.

Benefits of having & using a data dictionary

Setting up a data dictionary does require some effort, so let's explore some of the benefits you'll get upon creating a detailed one.

Better communication

Having a well-defined data dictionary makes it easier for everyone to communicate effectively since it provides the same language and understanding of the data across your organization. This helps prevent miscommunication and misinterpretation of data, as each stakeholder can refer to the same document when discussing different kinds of data.

Improved data quality

A data dictionary serves as the authoritative definition for data, which helps ensure that your database is populated with accurate and consistent information.

This improves the overall quality of your database, leading to more reliable and useful insights when you run analytics on it.

Easier maintenance

Having a defined data dictionary makes it much easier to maintain your database and keep track of changes. This is especially useful when you need to add new data elements or update existing ones, as the data dictionary can be used as a reference for everyone to clearly understand what’s being modified.

Easily searchable

With the use of a well-indexed data dictionary, you can easily search for the data elements you need.

This helps save time and effort when analysts are looking for specific information, reducing the need to manually comb through an entire database.

(Learn how federated search works.)

How to create a data dictionary

To create a data dictionary, follow these five steps:

Step 1. Identify your data elements

Start by listing out the different data elements in your database. Collect information about each element, such as:

  • Name
  • Type
  • Source
  • Other related information

Step 2. Document the structure

Next, document the structure of your database to understand how different data elements are connected. List all relationships between data elements to provide a clear picture of the entire database.

(See how CMDBs can inform this step.)

Step 3. Define each data element

For each data element, define its purpose, domain value and any other definitions you need. Doing so will ensure that all stakeholders have a shared understanding of it.

Step 4. Set up validation rules

Validation rules help ensure accurate input into the database, so make sure to document them in your data dictionary.

Step 5. Monitor and update

Your data dictionary should be kept up-to-date with changes made to the database. Therefore, having someone responsible for monitoring and updating it is crucial.

Some types of users who can update a data dictionary include:

  • Database administrator
  • Data engineer
  • Data analyst
  • Business intelligence analyst

(Read about the concepts of continuous monitoring & monitoring for observability.)

Examples of good data dictionaries

To provide a better understanding of what data dictionaries should be like, you can take inspiration from the following examples.

1. MicroStrategy Intelligence Server Statistics Data Dictionary

This data dictionary from MicroStrategy contains various performance metrics and objects related to the Intelligence Server. It includes definitions for each metric, as well as any notes or explanations needed to understand it better.

Take, for example, their data dictionary named "STG_CT_DEVICE_STATS", which stores information about the mobile client and mobile device.

In this example, there was the data element name, description, and datatype.

2. American Time Use Survey Data Dictionary

The American Time Use Survey Data Dictionary from the Bureau of Labor Statistics describes the different data items used in their survey. This allows researchers to better understand how variables are coded and each item's meaning.

For example, in the 2021 ATUS Interview Data Dictionary, their “TRTEC” variable is described as “Total time spent providing eldercare (in minutes)". It also included the validation rules of having a "Min Value" of 0 and a "Max Value" of 1440.

Next level: data dictionary FAQs

With the basics out of the way, let’s look at some common related questions.

What is the difference between a database and a data dictionary?

  • A database is a collection of related data that can be queried.
  • A data dictionary is an organized list of the structure and attributes of the data stored in a database.

The data dictionary provides additional information about the data elements and their relationships within the database, which helps with understanding and managing it.

(Read about different databases: SQL and NoSQL.)

Is a data dictionary the same as a schema?

No, a data dictionary is not the same as a schema. A schema refers to the structure and organization of the database, while a data dictionary provides additional details about each element in the database.

The schema describes the tables and their relationships, while the data dictionary explains what each item means and how it should be used.

What is a data dictionary in software engineering?

In software engineering, a data dictionary is a set of information about the system and its components, such as databases, programs, file, and tables.

It documents the structure and attributes of each item in the system for better understanding and management. It also includes any rules related to data elements or processes in order to maintain accuracy and consistency.

The data dictionary is used for software development as a reference point for developers, product managers, engineers and data administrators.

(Compare software development practices like DevOps, SRE & platform engineering.)

Final thoughts

Having an accurate and up-to-date data dictionary is essential when managing and working with data, especially from large datasets and databases. It serves as a reference for everyone to clearly understand what’s being modified, as well as providing key benefits such as easier searches and increased accuracy.

By having a comprehensive data dictionary, you can ensure better communication, improved data quality and easier maintenance. 

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This posting does not necessarily represent Splunk's position, strategies or opinion.

Austin Chia
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Austin Chia

Austin Chia is the Founder of AnyInstructor.com, where he writes about tech, analytics, and software. With his years of experience in data, he seeks to help others learn more about data science and analytics through content. He has previously worked as a data scientist at a healthcare research institute and a data analyst at a health-tech startup.