Data Catalog vs Data Lineage: Tools for Complete Data Intelligence

A data catalog on its own is not nearly enough for the data management needs of an enterprise (and you might already know that from our previous articles.) Although it captures structure and metadata relationships within systems, it doesn’t explain how data flows across pipelines, processes, systems, and business contexts. That’s where data lineage comes in. It provides a dynamic view of how data moves and transforms across your ecosystem, enabling transparency, impact analysis, and trust in your data assets.

While data catalogs and data lineage support different aspects of data governance, they complement each other to form a more complete and reliable data management framework. Let’s start by breaking down the key differences between the two.

Understanding data catalogs

What is a data catalog?

A data catalog is a centralized inventory of an organization’s data assets. You can think of it as a searchable directory that provides context for data: what it is, where it lives, who owns it, and how it can be used.

A data catalog typically includes metadata, data classifications, business glossaries, and data stewardship information, but not the data itself. It’s more of a compilation of data about the actual data.
Data catalogs make it easier for enterprise teams to find and understand data across the entire company. They also help with efficiency around data, avoiding duplicates and ensuring reliable data is used in reports, marketing materials, etc.

When you use Collibra, a data catalog can become even more efficient by integrating governance frameworks to enable proper data documentation, management, and access. To learn more about creating a data catalog, read our article on how to build a data catalog.

How do data catalogs benefit organizations?

Companies that implement and use data catalogs can count on multiple benefits, including:

• A unified view of all data assets, gathering information about data from different sources across the company, including unstructured data, and providing them with valuable business context.
• Improved discoverability, with people being able to quickly find relevant datasets, reducing time spent searching for information.
• Enhanced data governance, providing thorough documentation for metadata, ownership, and policies.
• Better cross-team collaboration, making it easier for teams to share knowledge about data assets and reducing the burden on IT, data, and analytics teams as the only ones who can access, retrieve, and interpret data.
• Increased data literacy among non-technical data users, made easier by readily available definitions, context, and guidelines.
• When you work with Murdio using Collibra, we can help you fully automate the process of metadata ingestion using Edge or custom integrations.

Exploring data lineage

What is data lineage?

Data lineage is a separate conceptual layer of data management that you can also integrate with your data catalog. In Collibra and other data intelligence platforms, it’s basically another module or view that enriches the metadata in your data catalog.

To give you a definition, according to the Data Management Body of Knowledge (DMBOK), data lineage encompasses the complete data life cycle, with a detailed view of its origins, movements, transformations, and destinations. This way, it gives companies a comprehensive view of their data, helping trace errors, ensure data quality, and meet regulatory requirements as it offers visibility into data dependencies and potential quality issues. 

With data lineage, any time there’s a change in a data asset, you can track it down right to its source, making sure the data’s reliable and consistent across the company, and people are using the same data consistently.

In Collibra, data lineage visualizations help users understand data transformations and their impact across their data ecosystems. If data is changed or updated at some point, it’s easy to track its origins and compare datasets.

Types of data lineage

You can look at data lineage from different perspectives, depending on your use case and the types of information you’re looking for. So the below aspects that are sometimes called “types” are simply different layers of data lineage:

• Business lineage provides a simplified, high-level view of data movement and transformations designed for non-technical stakeholders.

• Technical lineage offers detailed insights into how data is transformed, including SQL scripts, ETL processes, and API interactions, designed for technical teams.

• Operational lineage focuses on real-time data flows, and it’s crucial for runtime monitoring, observability, and troubleshooting.

When you browse available resources, you can also come across other classifications of data lineage based on different criteria, including automated data lineage and descriptive data lineage.

Why is data lineage important for business?

Data lineage is what gives a data catalog the ability to actually track what happens to different data assets as they travel across the enterprise. And this, in turn, is important for multiple reasons, including:

• Regulatory compliance, making it possible to audit data according to regulatory frameworks such as GDPR, CCPA, HIPAA, and others.
• Data quality and trust, making it possible to identify inconsistencies and errors in data pipelines.
• Impact analysis to help assess how changes in one dataset affect downstream reports and systems.
• Root cause analysis to pinpoint data issues and prevent operational disruptions.

Key differences between data catalogs and data lineage

Here’s a basic breakdown of the features of a data catalog vs. data lineage. Keep in mind that while you can technically use them on their own, you’ll get the best data management results by integrating the two.

Here’s how the two compare:

Data catalog

• Purpose: Helps users find and understand data assets
• Primary benefit: Improves discoverability and helps with data governance
• Target users: Business analysts, data stewards, data consumers
• Metadata focus: Business glossary, ownership, classification
• Visualization: Structured lists, search, tagging

Data lineage

• Purpose: Tracks data movement and transformation
• Primary benefit: Enhances data transparency and troubleshooting
• Target users: Data engineers, compliance teams, IT operations (but also non-technical users with business lineage)
• Metadata focus: Data flow, transformation logic, dependencies
• Visualization: Flow diagrams, lineage graphs

When should you use a data catalog vs. data lineage?

Ideally, you should use them together to get a clear view of your entire data landscape. Though, since technically they serve slightly different purposes, it’s probably safe to say that you should:

• Use a data catalog to improve data discoverability, enforce governance policies, and enhance cross-team collaboration.
• Use data lineage to track data movement, analyze dependencies, troubleshoot issues, and comply with data regulations.

To give you an example, here’s a case study of custom Collibra development we did for an international retail chain, which included implementing cross-system technical lineage and custom development for Collibra-SAP lineage.

One thing you need to know about data lineage is that it needs to span all your systems that the data flows through, which usually means integrating your data catalog software with other solutions such as SAP, for example.

In this particular case, we built a solution that enabled reporting teams and other data consumers to visualize data flows across systems (e.g., SAP, Data Lakes, and Databases) for easier impact analysis.

The custom technical data lineage was an element of a larger whole that also included automated data governance workflows, customized data quality solutions in Collibra, software integrations, and a whole lot of consultancy and advisory services spanning metamodel architecture and business alignment.

Synergies between data catalogs and data lineage

How can data catalogs and lineage complement each other?

As we previously noted, data catalogs and data lineage solutions are designed to work together for best results. When integrated, they create a comprehensive enterprise data management framework.

A data catalog gives a structured overview of data assets and provides context, while data lineage provides traceability, showing how these data assets evolve over time.

In fact, data lineage enhances trust in the data catalog that it complements. When you can physically see the data journey, you gain clear insight into its accuracy and reliability.

Plus, the two working together are key for governance and compliance, making data policies easier to apply, enforce, and audit.

What are the benefits of integrating both tools?

Integrating data lineage solutions into data catalogs, which by the way can both be done using Collibra, allows for:

• Understanding where data originates, how it’s used, and who owns it.
• Tracing data errors back to their source with lineage while leveraging the catalog to find additional context.
• Maintaining auditable records of data flows and governance policies.
• Making it easy for users across the enterprise to access and trust data insights.

Both cataloging data and providing complex data lineage were also part of our project for a leading Swiss bank, whose goal was to comply with FINMA Circular 2023/01 requirements around sensitive critical data elements:

A centralized data catalog helped structure a Data Governance Framework and classify and catalog data across over 100 applications.

Tracking data lineage supported impact analysis and regulatory reporting, ensuring compliance and risk mitigation.

Conclusion: It’s not data catalog vs. data lineage. It’s data catalog AND data lineage

We basically always recommend applying data lineage for your data catalog to automatically track data transformation and flow. For more data catalog recommendations, check out this article on data catalog best practices.

And since we specialize in Collibra, we can do that using one platform for a more comprehensive data management ecosystem.

If that’s something you’re looking to do for your enterprise, we’d be happy to help!