Data Inventory vs. Data Catalog: Definitions, Differences, and Examples

Updated October 28th, 2023
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Data inventory vs. data catalog: Key differences #

The main difference between a data catalog and a data inventory is that a data inventory details the type and location of each data point in an organization. A data catalog references an organization’s datasets in various categories for search and discovery.

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Here’s a table that highlights the differences between data inventory vs. data catalog.

Aspect Data Inventory Data Catalog
Definition A data inventory details the type and location of each data point in an organization. A data catalog references an organization’s datasets in various categories for search and discovery.
Scope It helps map an organization’s data, primarily for compliance with regulations (GDPR / CCPA). It enables data search and discovery of data assets, with the right context.
It also ensures data quality, integrity, and reliability.
Users Data inventory is for IT teams to find and map all essential data assets. Data catalog is for technical and business users to access the right data and extract insights.
Key difference It includes the technical metadata associated with each data asset. Data catalogs include all metadata types — technical, business, operational and social.
Top benefits Inventorying

IT teams know what data their organizations collect, store and use, including dark data.		 A single source of truth

A data catalog is a central repository for everyone within an organization to find and access the data they need.
Trustworthy data

Since a data inventory maps all data along with technical information, IT teams can trace its origins and verify its authenticity and credibility.		 High-quality, timely and trustworthy data

Modern data catalogs automate lineage and can propagate policies through lineage. They also create automatic data profiles and run automated quality checks frequently to spot anomalies or inconsistencies in data.
GDPR / CCPA compliance for sensitive data

Data inventory helps with regulatory compliance by finding and mapping sensitive data.		 End-to-end governance and data democratization

Modern data catalogs help with compliance by enabling granular (column-level) access controls, lineage mapping, tag-based access policies, and automated PII data classification.
Relationship A data inventory involves identifying all the data of an organization. It is the first step toward creating a data catalog. Inventorying data is an essential aspect of data catalogs. They’re created after identifying the data within an organization’s warehouses and lakes.


Data inventory vs. Data catalog — are they the same, if not, what is the difference? #

Data inventories and data catalogs are metadata management tools, but that’s where the similarities end. While data inventory handles technical metadata, data catalogs also help manage business metadata.

Definition:

  • Data inventory identifies the type and location of each data asset.
  • Data catalog is an organized inventory of data assets across all your data sources.

Scope:

  • Data inventory helps to stay compliant with data regulations(GDPR/CCPA).
  • Data catalog enables easy search and discovery of data.

Users:

  • Data inventory is for IT teams to map all data assets.
  • Data catalog is for business users to access the right data to derive insights.

This article will explore the concepts of data inventory and data catalog and their differences. Let’s begin with understanding a data inventory.

What is a data inventory? #

April Reeve, in the presentation ‘The Data Catalog — The Key to Managing Enterprise Data Big and Small’, defines a data inventory as:

“A physical list of what data you have and where it is located. It tends to be more on the technical metadata side.”

The technical metadata includes names (table and column names), ownership, location, and size. Such metadata gives organizations a deeper understanding of their data and information resources.

Regulations such as GDPR and CCPA make conducting a data inventory and mapping exercise mandatory.

Under Article 30 of the GDPR, data inventory is the first step toward compliance. The inventory must include:

  • The personal data that you collect and use
  • Details of where and how you store this data (including the server locations)
  • A map of all the transformations it undergoes

Similarly, the CCPA expects organizations to maintain a data inventory with information on:

  • The personal data that they collect and details of the ways of acquiring that data
  • The formats and storage locations
  • The classes of data assets and their descriptions

So, a data inventory helps you understand the data assets that you collect, store, and use. Since data inventory only contains technical metadata, it’s common for organizations to have a data dictionary or a data glossary along with the data inventory to provide more context.

Let’s explore the differences between these concepts.

Data Inventory vs. Data Dictionary #


According to the DAMA Dictionary of Data Management, a data dictionary is:

“A place where business and/or technical terms and definitions are stored. Typically, data dictionaries are designed to store a limited set of metadata on the names and definitions relating to the physical data and related objects.”

So, a data dictionary provides definitions of your data assets. Think of it as a repository of names, descriptions, and other attributes that include contextual information about data.

Together with the technical metadata from the data inventory, a data dictionary helps you understand your data by adding meaning to the terminologies used.

To know more about a data dictionary, check out our comprehensive article titled What is a data dictionary and why do you need one?

Data Inventory vs. Data Glossary #


A data glossary defines the commonly used business terms in an organization. Think of it as a collection of all terms that define your data’s key characteristics, organized in a way that is easy to search.

A data glossary is often referred to as a business glossary since the terminologies in a data glossary are synonymous with business concepts. It acts as a bridge between IT and business.

Interested in learning more about a data glossary (or a business glossary)? Then check out our in-depth guide here.

Now, let’s explore data catalogs.

What is a data catalog? #

In the 1990s, when data started exploding in volume and format, IT teams were responsible for building an “inventory of data”. However, this became a struggle as the volume of data kept exploding.

With the rise of big data and analytics, a simple IT inventory of data wasn’t enough. At the same time, the number of data consumers within organizations also grew. So, organizations needed data catalogs that merged data inventory with adequate business context for the modern data user, leading to the rise of data catalogs.

According to Gartner:

“A data catalog creates and maintains an inventory of data assets through the discovery, description, and organization of distributed data sets. The catalog provides context to enable data analysts, data scientists, data stewards, and other data consumers to find and understand a relevant dataset for the purpose of extracting business value.”

Data catalogs help all data users — technical and business — find and extract value from relevant datasets. Data consumers can use data catalogs to:

  • Create a repository of all their data assets
  • Provide access to metadata and data
  • Understand the data lineage
  • Maintain data consistency and accuracy
  • Simplify data compliance
  • Self-service capabilities

To know more about modern data catalogs, check out our in-depth article on the evolution of data catalogs and the capabilities they need for the modern data stack.

With the core concepts out of the way, let’s look at the differences between a data inventory vs. a data catalog.

Instead of data inventory vs. data catalog, think data inventory + data catalog #

When evaluating data inventory vs. data catalog, you must have noticed how they complement each other and are essential steps in helping you understand and organize your data assets.

That’s why the first step toward effective metadata management is to create a data inventory, classify your data assets and add context. To create a data inventory, you should:

  1. Establish an oversight authority: Assign the responsibility of establishing data definitions, classes, rules, and procedures.
  2. Define the scope: Document your data goals and use it to define the scope of your data inventory
  3. Catalog data assets: Define the context you need for your data assets (descriptions, relationships with other assets, ownership) and set up a glossary (tags, labels, definitions) to ensure that all assets have a uniform meaning throughout your organization.

Data inventory and data catalog: Best practices for getting started #


While cataloging your assets, make sure that they:

  • Align with and support external regulatory requirements
  • Apply to data in different states (rest, transit, and use)
  • Are machine-readable
  • Can be automated to simplify tracking, monitoring, and updates

Final Word #

It’s common to compare data inventory vs. data catalog when looking for a solution that brings visibility into an organization’s data. As we’ve seen, both data inventories and data catalogs are crucial for effective metadata management.

While a data inventory tells you what data you have, data catalog helps you understand and use it. So, deploying both, or a platform that supports inventorying and cataloging data, is the best way forward.

Need help choosing the right data catalog for your organization? Here’s a comprehensive guide on evaluating data catalogs.

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