What is Data Cataloging & Why Its 6 Components Matter?

Last Updated on: July 17th, 2023
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What is data cataloging?

Data cataloging is the process of creating and maintaining a centralized and organized repository of metadata and information about the data assets within an organization.

A data cataloging tool helps users find, understand, and use the data more effectively by providing a comprehensive view of all available datasets, their relationships, and their properties.

In this article, we will learn essential components of data cataloging, how it unifies metadata across different systems, and how it can benefit different teams in your organization.

Let’s dive in!

Table of contents

  1. What is data cataloging?
  2. Understanding the six essential components of data cataloging
  3. From chaos to order: How data cataloging unifies metadata across different systems
  4. User personas and use cases: How data cataloging can benefit IT teams, business analysts, and end users
  5. Rounding it all up
  6. What is Data Cataloging? Related reads

Understanding the six essential components of data cataloging

Data cataloging typically includes the following six essential components:

1. Metadata

This includes information about the data, such as table names, column names, data types, descriptions, and other relevant details. Metadata helps users understand the structure and meaning of the data.

2. Data lineage

This shows the origin and history of the data, including how it was transformed, processed, or combined with other datasets. Data lineage helps users trace data back to its source and understand how it has changed over time.

3. Data quality indicators

These provide insights into the accuracy, completeness, and consistency of the data, helping users assess its reliability for their purposes.

4. Data governance policies

These outline the rules and processes for accessing, modifying, and using the data, ensuring that data is used appropriately and securely.

5. Data usage analytics

This offers insights into how frequently and by whom the data is being used, helping organizations identify popular datasets and prioritize improvements.

6. Collaboration tools

These enable users to share insights, ask questions, and discuss the data, promoting a more data-driven culture within the organization.

By implementing a data catalog tool, your company can:

1. Improve data discovery

Business users can quickly find relevant datasets without having to rely on technical staff or dig through multiple systems.

2. Enhance data understanding

Users can easily access metadata, lineage, and quality information to better understand the context and trustworthiness of the data.

3. Foster collaboration

Data cataloging promotes knowledge sharing and collaboration among business users, leading to better data-driven decision-making.

4. Streamline data governance

Centralizing metadata and governance policies helps ensure that data is used responsibly and securely.

5. Monitor data usage

Understanding how data is being used can help prioritize improvements and identify areas where additional training or support may be needed.

Data cataloging can empower your business users to leverage data more effectively and make better-informed decisions without needing deep technical expertise.

From chaos to order: How data cataloging unifies metadata across different systems

Metadata and data cataloging are closely related concepts but have distinct roles in organizing and managing data. Data cataloging can utilize a variety of metadata from your tech stack, including Snowflake, Power BI, SQL on-prem server, Power BI on-prem, SAP HANA, ThoughtSpot, and even Microsoft Teams.

Here are some examples of metadata that a data catalog can collect and integrate from these systems (note that this list cites popular systems and is not an exhaustive list):

1. Snowflake

  • Table and schema names
  • Column names, data types, and descriptions
  • Data warehouse, database, and schema sizes
  • Query history and usage statistics
  • Access control policies and roles

2. Power BI (cloud and on-prem)

  • Dashboard and report names, descriptions, and creators
  • Dataset names, sources, and refresh schedules
  • Table and column names, data types, and relationships
  • Measures, calculated columns, and DAX expressions
  • Visualization types, chart titles, and axis labels

3. SQL on-prem server

  • Database, table, and schema names
  • Column names, data types, and descriptions
  • Indexes, primary keys, and foreign keys
  • Stored procedures, functions, and triggers
  • Query history, execution plans, and performance statistics


  • Database, schema, and table names
  • Column names, data types, and descriptions
  • Calculation views, analytic views, and attribute views
  • Stored procedures, functions, and triggers
  • Data provisioning methods and replication status

5. ThoughtSpot

  • Search index names and descriptions
  • Data source connection details
  • Table and column names, data types, and relationships
  • Synonyms and search keywords
  • Worksheet and pinboard names, creators, and descriptions

6. Microsoft Teams

  • Team names, descriptions, and members
  • Channel names and purposes
  • Conversations, messages, and file attachments
  • Meeting and call details (e.g., date, time, participants, duration)
  • Apps, bots, and custom connectors used within the teams

By collecting metadata from these various systems, your data catalog can provide a unified view of your organization’s data assets, making it easier for business users to discover and understand the data they need for their analysis and decision-making.

User personas and use cases: How data cataloging can benefit IT teams, business analysts, and end users

We can categorize the users of a data catalog into three main groups, each with its specific use cases:

1. IT Team (Data engineers, DevOps, etc.)

a. Data lineage

Understand the flow and transformation of data across various systems, allowing them to trace data back to its source and analyze its history. This helps with debugging issues, ensuring data accuracy, and maintaining compliance.

b. Impact analysis

Assess the potential effects of changes to data sources, schemas, or processes on downstream systems and reports. This helps them make informed decisions when planning and implementing changes, minimizing unintended consequences and reducing the risk of data-related issues.

2. Business analysts (Data analysts, information analysts, etc.)

a. Tagging

Annotate datasets, tables, and columns with relevant tags or labels to improve data discovery and understanding. This enables analysts to quickly find and understand the data they need for their analysis.

b. Categorizing

Organize data assets into meaningful categories or groups, making it easier for users to navigate and discover relevant datasets.

c. Data dictionary

Create and maintain a data dictionary, which is a centralized repository of definitions, descriptions, and metadata for all data elements. This helps analysts and other users to understand the meaning and context of the data.

d. Data governance

Collaborate with IT and other stakeholders to develop and enforce data governance policies, ensuring that data is used responsibly, securely, and in compliance with regulations.

3. End users (Business users, decision-makers, etc.)

a. Data discovery

Leverage the metadata, tags, categories, and data dictionary created by the other user groups to easily find and understand relevant data for their specific needs.

b. Collaboration

Use the data catalog’s collaboration tools to ask questions, share insights, and discuss data with colleagues, fostering a data-driven culture and improving decision-making.

c. Trust and confidence

Access data quality indicators, lineage information, and governance policies to assess the reliability and trustworthiness of the data, enabling them to make more informed decisions.

d. Self-service

Empower end users to explore and analyze data on their own without relying on technical staff, improving their efficiency and productivity.

By understanding the different user groups and their unique use cases, you can tailor the implementation of a data catalog to meet the specific needs of your organization and drive maximum value from your data assets.

Rounding it all up

Data cataloging involves the systematic organization and indexing of metadata and other related information to create a searchable catalog of data assets within an organization. A data catalog serves as a centralized inventory of available data sources, datasets, databases, files, reports, and other data artifacts.

It acts as a data discovery tool, enabling users to find, explore, and understand the available data assets.

Data cataloging goes beyond mere metadata management by providing a user-friendly interface and search capabilities to locate and access data assets effectively. It may include features such as data profiling, data lineage visualization, user annotations, data usage statistics, and collaborative functionalities.

Data cataloging solutions help data consumers, analysts, and scientists discover relevant data assets, understand their contents and characteristics, and assess their fitness for specific use cases.

Deploying a data catalog tool starts the seeding process of data democratization and data enablement in your organization. It says that your organization is serious about maximizing the value of data.

It also recognizes that we can extract much more from data when we create an even playing field for the diverse data users in an organization. A data catalog is a starting point for that inclusive initiative.

Are you considering data cataloging for your organization — you might want to check out Atlan.

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Everything you need to know about modern data catalogs

Adopting a modern data catalog is the first step towards data discovery. In this guide, we explore the evolution of the data management ecosystem, the challenges created by traditional data catalog solutions, and what an ideal, modern-day data catalog should look like. Download now!

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