A metadata catalog is a centralized repository that organizes and manages data about your data assets.See How Atlan Simplifies Data Cataloging – Start Product Tour
It includes essential information such as data sources, ownership, and attributes. This catalog enhances data discovery and accessibility, making it easier for teams to find and utilize data effectively.
By providing a clear overview of available data, organizations can improve decision-making and operational efficiency.
What is a metadata catalog?
Permalink to “What is a metadata catalog?”A metadata catalog is nothing but a collection of all the data about your data. Metadata can include the data source, origin, owner, and other attributes of a data set.
These help you learn more about a data set and evaluate if it is well-suited for your use case.
A truly powerful metadata catalog will help you:
- Create a central repository for all your data and metadata, including the structure, quality, definitions, and usage of the data.
- Access the metadata right alongside the data itself—no asking around!
- Ensure data consistency and accuracy by updating itself auto-magically, while allowing humans to remain in the loop.
The basics of metadata and data catalogs
Permalink to “The basics of metadata and data catalogs”Before we go any further, let’s go through some commonly asked questions about metadata.
- What is metadata? Metadata is just data about other data. It gives basic information about a data asset to help users find the data they need.
- What is an example of metadata? An example of basic metadata is the author, date created, date last modified, source, and size of a data set. Some more complex examples of metadata are query logs, lineage, quality scores, and related discussions.
- What is the difference between data and metadata? Data is information that measures, describes, or reports on something. Metadata is relevant information that gives context to that data.
- Who uses metadata? Anyone who uses data would also use metadata. After all, you can’t use a data set until you first know if that data is right for your use case.
- Where is metadata stored? Metadata should be stored close to the data it is describing. This can be in a nearby table or field, a separate document like a data dictionary, or ideally in a metadata catalog.
- What are the benefits of metadata? Metadata is important for giving context to data. With the sheer amount of data available nowadays, you need more information about a data set before you can know if it’s right for you. Metadata also helps document data so it can be shared and reused across multiple use cases.
What are metadata catalogs useful for?
Permalink to “What are metadata catalogs useful for?”A well-organized data catalog with your metadata is useful for creating a single source of truth for all your company’s data. A metadata catalog can help your team discover, manage and understand all your data assets in one place.
This is important because the number of consumers of data is quickly increasing. Companies are increasingly investing in setting up data lakes, big data initiatives, and creating self-service data analytics ecosystems. This leads to many versions of the truth—multiple data sets, versions, and isolated knowledge.
Four ways to ace your metadata catalog needs
Permalink to “Four ways to ace your metadata catalog needs”- Understand the fine print and quality of your data.
- Crowdsource your metadata catalog.
- Get critical business context on your data.
- Search through petabytes of data.
1. Understand the fine print and quality of your data
Permalink to “1. Understand the fine print and quality of your data”Understand what each column means via shareable data dictionaries. Access detailed data quality reports and understand the quality of a data table. Quickly onboard new users and help admins to monitor data quality.
Tools and techniques that can help:
- Data dictionary
- Quality reports
- Metadata management
Understand the fine print and quality of your data. Image by Atlan
2. Crowdsource your metadata catalog
Permalink to “2. Crowdsource your metadata catalog”Convert human tribal knowledge into a living system by allowing your team to add notes, ratings, and tags to datasets. Easily evaluate the quality of your data and help your team access this information too.
Tools and techniques that can help:
- Data annotations
- User-generated ratings
- Data tags
Crowdsource your metadata catalog. Image by Atlan
3. Get critical business context on your data
Permalink to “3. Get critical business context on your data”Supplement your technical data with contextual business information. Easily understand how a data set can be used and what it contains. Add context to your data, alongside it.
Tools and techniques that can help:
- READMEs
- Metadata repository
- Business Glossary
Get critical business context on your data. Image by Atlan
4. Search through petabytes of data
Permalink to “4. Search through petabytes of data”A metadata catalog should enable you to find and discover the exact data table that you need for your use case. Metadata tags such as owners, source, timeframe, etc should help in filtering the data.
Tools and techniques that can help:
- Data filtering
- Powerful search
Search through petabytes of data. Image by Atlan
These techniques will help you ace an essential part of your metadata management. Most importantly, it should help you create a single source of truth across your data ecosystem.
How organizations making the most out of their data using Atlan
Permalink to “How organizations making the most out of their data using Atlan”The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:
- Automatic cataloging of the entire technology, data, and AI ecosystem
- Enabling the data ecosystem AI and automation first
- Prioritizing data democratization and self-service
These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”
For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.
A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.
Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes
Permalink to “Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes”- Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
- After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
- Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.
Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.
FAQs about Metadata Catalog
Permalink to “FAQs about Metadata Catalog”1. What is a metadata catalog?
Permalink to “1. What is a metadata catalog?”A metadata catalog is a centralized repository that organizes and manages information about data assets. It includes details such as data sources, ownership, and attributes, facilitating easier data discovery and access.
2. What is an example of a metadata list?
Permalink to “2. What is an example of a metadata list?”An example of a metadata list includes information such as the data source, data owner, creation date, last modified date, and data format. This list helps users understand the context and relevance of the data.
3. What is the difference between MDM and a data catalog?
Permalink to “3. What is the difference between MDM and a data catalog?”Master Data Management (MDM) focuses on ensuring the consistency and accuracy of critical business data across an organization. In contrast, a data catalog organizes and provides access to metadata about various data assets, enhancing data discovery and usability.
4. What are the three types of metadata?
Permalink to “4. What are the three types of metadata?”The three types of metadata are descriptive metadata, which provides information about the content; structural metadata, which describes how data is organized; and administrative metadata, which includes information about the management of data assets.
5. How can a metadata catalog improve data accessibility?
Permalink to “5. How can a metadata catalog improve data accessibility?”A metadata catalog improves data accessibility by centralizing information about data assets, making it easier for users to discover and utilize relevant data. This leads to better decision-making and operational efficiency.
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