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Data Dictionary: What is it and how do you create your own?

Data Dictionary: What is it and how do you create your own?

At this point, it’s cliche to emphasize the importance of data nowadays. Nearly every business relies heavily on data, and people are increasingly aware and protective of the data companies store.

But data is only as valuable as how well it’s understood and used by those within the company.

Without a common understanding of your data, it’s impossible for teams to effectively collaborate, analyze, or standardize the data you’ve got stored.

This is why a data dictionary is such an important tool.

In this article, we will cover:

  • What is a data dictionary?
  • The importance of data dictionary in a business
  • Components of a data dictionary
  • How to create a data dictionary
  • Benefits of using a data dictionary

Enhance your data dictionary with curated knowledge, streamline collaboration, and revolutionize data management. Schedule a Customized Demo now and unlock OvalEdge's potential!

What is a Data Dictionary?

A data dictionary is essentially a document or set of documents that outlines the data elements, their definitions, and the characteristics within a database or data source. It serves as a centralized resource for managing and documenting data, and helps ensure that data is consistent, accurate, and complete.

In a business, a data dictionary can help teams collaborate, analyze, and standardize data effectively. It provides clear definitions and rules for data entry and management, which helps ensure that data is consistent and accurate across different systems and applications.

The components of a data dictionary include data element names, definitions, data types, and any constraints or rules associated with the data.

To create a data dictionary, one must identify the data elements and their characteristics, then document them in a standardized format.

Benefits of using a data dictionary in your business

A data dictionary is a fundamental tool for businesses dealing with large amounts of data. It helps ensure that employees across different departments have a common understanding of the data they are working with, which facilitates collaboration, analysis, and standardization of data.

Without a data dictionary, it can be difficult for employees to understand the structure and relationships of data within a database or information system. This lack of transparency can lead to confusion and errors, making it challenging for businesses to make well-informed decisions based on their data.

On top of this, a data dictionary can help improve data quality and accuracy by providing clear definitions and rules for data entry and management. This ensures that data is consistent and accurate across different systems and applications, which is essential for businesses that rely heavily on data to make decisions.

Related: Building a Business Glossary - Why and How

Here’s a summary of the different benefits:

  • Helps ensure that data is accurate, consistent, and complete, which improves data quality
  • By providing a standardized view of data across an organization, a data dictionary can increase efficiency and reduce errors
  • A data dictionary helps establish clear rules and standards for data use, which improves data governance
  • Provides a common language and understanding of data, helping to improve collaboration between teams and departments
  • A data dictionary helps ensure that data is compliant with regulatory requirements and internal policies, which can simplify compliance efforts
  • You end up with accurate and consistent data, enabling better decision-making and more informed insights
Related: Data Catalog vs Data Dictionary - Differences & Use Cases

Components of a data dictionary

The contents and structure of your data dictionary will vary from company to company, depending on the different needs and requirements of each organization.

But there are many common components that are valuable to most businesses:

  • Data element names: The names of the data elements should be clear and descriptive to ensure that all users can easily understand them.
  • Data element definitions: Clear and concise definitions for each data element that describes its purpose and meaning.
  • Data element attributes: Additional characteristics of the data elements, such as data type, length, format, precision, and scale.
  • Metadata: Additional information about the data elements, including their creator, creation date, and usage.
  • Relationships: Information on how the data elements are related, such as parent-child relationships or many-to-many relationships.
  • Data owner and steward information: Who is responsible for the data elements, including data owners and data stewards.
  • Business rules: Rules and constraints for the data elements, including mandatory fields, allowable values, and formatting requirements.
  • Data usage information: How data elements are used within the organization. This includes details on which systems or applications use them and how they are used.
  • Data source information: Information about the sources of the data, including where it was collected, how it was collected, and how often it is updated.
  • Glossary: A list of terms and definitions related to data elements. It is intended to ensure consistency and understanding across the organization.
Related: What is Active Metadata Management? Definition & Benefits

How to create a data dictionary

As we mentioned before, every company’s data dictionary will be different. But by following this step-by-step guide, you’ll be able to implement the key foundations you need.

Step 1: Identify data elements

Start by identifying all the relevant data elements for your organization or system. This could include information about customers, products, transactions, or any other data critical to your business.

Step 2: Define each data element and assign attributes

After identifying the data elements, define each one by creating a clear and concise definition that describes its purpose and meaning. Then, assign attributes to each data element, such as data type, length, format, precision, and scale. This will help ensure the data is accurate and consistent across all systems and applications.

Step 3: Document relationships and define business rules

Next, document the relationships between the data elements. This includes parent-child relationships, many-to-many relationships, and any other relevant relationships. After that, define any business rules and constraints that apply to the data elements. These may include mandatory fields, allowable values, and formatting requirements. By doing so, you can help ensure that the data is valid and conforms to the organization's standards.

Step 4: Capture metadata, source information, and usage information

It is important to capture metadata for each data element, including information such as who created it, when it was created, and its usage. Additionally, recording information about the sources of the data - such as where it was collected, how it was collected, and how often it is updated - can help ensure that the data is trustworthy and relied upon.

Step 5: Assign data owners and stewards

Assign data owners and data stewards for each data element to ensure that someone is responsible for maintaining its accuracy and completeness. Data owners are responsible for ensuring the data is accurate and meet the organization's standards, while data stewards are responsible for managing the data on a day-to-day basis.

Related: Data Governance & Data Stewardship Explained

Step 6: Publish

After completing the data dictionary, publish it and make it available to all relevant stakeholders within the organization. Be sure to maintain it regularly, updating it as needed to reflect changes in the organization or system. This will help ensure that the data remains accurate, consistent, and well-documented.

Pro tip: Use OvalEdge to make this process easy

This process can be overwhelming if you try to do it manually. But by using a tool like OvalEdge, the whole thing can be much simpler.

OvalEdge crawls all existing data dictionaries, making the data available to end users with a graphical user interface. You can then enhance the data dictionary within OvalEdge, by curating and adding domain knowledge.

This both speeds up the process, and makes it much easier to invite others to access the data dictionary.


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