Data Lineage vs. Data Catalog: What Are Their Differences and Use Cases?

As companies generate more data, the velocity of incoming data makes the data “fire hose” harder and harder to grasp. Directing data to the right applications for access by the right people, ensuring that data is properly protected, keeping track of if (and how) data has been transformed… the list of things that can quickly get away from you is endless.

Many methodologies and products exist in the market to help businesses understand what types of data flow through their infrastructure, and how it can best be used. This article examines and compares two such methodologies: Data lineage and data catalogs.

What is data lineage?

Data lineage is marked by all the places the data has traveled in its lifetime. Data lineage refers to the process of recording and tracking data’s entire journey through the business pipeline throughout its lifecycle: Where it enters the system, how it is transformed, and all the locations where it has been stored.

It’s a more complex task than you might think: If data is duplicated once it’s received, a single iteration of that data might be processed, stored, analyzed, and interpreted only once. However, data reuse can create multiple branches from the original, resulting in each branch having a different processing, storage, management, analysis, visualization, and interpretation “fingerprint”.

Data lineage tracks data by collecting metadata as the data flows through different systems. This metadata is used to generate a map that shows all the places where the data interacted with other processes or applications, and its ultimate destination or product. The result of a data lineage analysis is a data lineage diagram: A visual representation of all the places the data has been in the system. The diagram is updated dynamically as the data continues to pass through the system.

Benefits of data lineage

Some of the benefits of data lineage analysis include:

Improved root cause analysis

Data lineage focuses on validating data accuracy and consistency by enabling users to search upstream and downstream, from source to destination, to discover anomalies and correct them. Validated, trusted data on hand yields explainable BI (business intelligence), which is crucial for making business technology decisions. With explainable BI you can:

• Migrate systems with confidence
• Lower the cost of new IT development and application maintenance
• Combine new and existing datasets with an agile data infrastructure
• Democratize data throughout the organization, increasing trust and reliance on that data
• Improve data analysis

Optimized regulatory compliance

Data lineage helps companies meet data governance goals and lower the cost of regulatory compliance. The details tracked in the data lineage feed directly into compliance auditing requirements. They also improve risk management and ensure data is stored and processed according to regulatory standards.

More efficient resource allocation

Data lineage analysis helps companies meet data governance goals and lower the cost of regulatory compliance. The details tracked in data lineage feed directly into compliance auditing requirements. They also improve risk management and ensure that data is stored and processed according to regulatory standards.

Better troubleshooting of data quality issues

Because data lineage practices create a clean blueprint of where data enters the system, where it stops to be processed, and where it winds up, it’s a great aid to implementing process changes. It also provides insight that empowers data teams to resolve data quality incidents quickly and well, reducing the impact of data downtime.

What is a data catalog?

A data catalog is a structured, detailed inventory of all data assets collected in an organization. By centralizing information about data assets, a data catalog enables users to find and access the most appropriate data for their needs quickly and easily. Data catalogs use metadata (data that describes or summarizes data) to identify and classify assets, which can include:

• Structured data
• Unstructured data (such as documents, web pages, email, social media content, images, audio, and video)
• Computational data
• Query results
• Data visualizations and dashboards
• Machine learning models
• Connections between databases

Data catalogs typically include tools for collecting and curating the metadata associated with each data asset, to make it easier to identify and evaluate. They also provide tools that help users search the catalog, screen for potentially relevant data, and guard protected data against inappropriate viewing or reuse. Data catalog tools enhance the experience of working with a data catalog by providing:

• Connections to a wide variety of data sources on-premises or in the cloud.
• Support for quality and governance that ensures trusted data.
• Easy data discovery, including the ability to ‘review’ data for the guidance of future users.
• The ability to profile data assets, inferring their relevance to specific regulations and automatically classifying and tagging them for future reference.
• The ability to tag and prepare data assets for optimal use and transparency in AI models.

Benefits of data catalogs

Some of the benefits offered by data catalogs include:

Eliminate data wrangling

Data wrangling may sound like a cute term for “herding” or “collecting” data (see herding cats), but data wrangling is actually the process of transforming and mapping data from its original format into another format that can be used for a variety of downstream purposes. The goal of data wrangling is to ensure useful, high-quality data.

Data analysts typically spend a lot more time “prepping” data for reuse, than they do in analyzing the data. If they can search a data catalog for the type of data they’re looking for instead of reinventing the wheel each time they start an analysis project, they’re left with much more time to spend on analysis.

Increased data discoverability

The goal of a data catalog is to promote a less dependent, more “self-serve” environment for end users. A well-curated data catalog can help anyone quickly find results based on the metadata they search for, and receive relevant recommendations and/or warnings based on ratings and reviews from other users.

This not only helps end-users find, categorize, and share data assets quickly, it also aids in collaboration with other users.

Automated contextualization of data assets

Detailed descriptions of data, including comments from other data users, can help analysts better understand how data is relevant to the business. It also helps data professionals respond rapidly to problems, challenges, and opportunities, providing analysis and answers based on the most appropriate, contextual data.

Improved data compliance and governance

The better-curated the data catalog, the easier it is to perform data governance for an organization. Well-curated data catalogs also make it much easier to monitor ongoing data compliance.

Data lineage vs data catalog: When to use each approach

The use of data lineage and data catalogs are not mutually exclusive in IT organizations. Each fulfills a different, specific need.

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CData Virtuality interacts seamlessly with local and cloud data to provide a self-service Business Data Shop for all your data users. With support for a wide variety of data lineage tools, data governance tools, and enterprise data catalogs, you can have instant access to all your data sources, handled efficiently via a virtual access layer.