What Is Data Lineage?

Key takeaways

Data lineage is a detailed record of how data moves and changes over time in a data pipeline. Here are some important facts to know:

• Documenting data lineage helps meet regulatory compliance requirements, such as those outlined in the California Consumer Privacy Act (CCPA) [1].

• When troubleshooting data flows or pinpointing pipeline errors, data lineage offers critical historical context.

• You can enhance your ability to implement and manage data lineage by becoming a certified professional in data management. 

Explore the vital connection between data lineage and data governance. If you’re ready to get started right away, consider pursuing IBM’s Data Engineering Professional Certificate to learn how to extract, transform, and load (ETL) data using Python programming language and Linux shell scripts.

What is data lineage?

Data lineage is the documentation of all inputs, outputs, and processes impacting data over its full lifecycle. The process begins by identifying the data source from which data flows. This could be, among others, a file, database, or data warehouse. The next step involves tracking the data’s path from its origin to its intended destination, recording any changes made during transit. Contemporary data management tools automatically map and document data lineage, minimizing the need for manual oversight. Visual diagrams are a standard feature in most data lineage tools, enhancing clarity and ease of navigation.

What is the purpose of data lineage?

Data lineage involves following data as it moves through systems, which helps organizations enhance their understanding of data quality and governance. It also helps organizations identify hidden flaws in data collection and transformation processes, particularly those that lead to missing or incomplete data. The enhanced visibility is increasingly valuable in today’s complex data environments, where multiple teams build and maintain pipelines with limited cross-functional insight.

Data lineage sheds light on important aspects of data by answering questions such as:

• What processes altered the data and in what manner?

• When did the modifications occur?

• From which location were the changes made?

• Why were the changes made?

Data lineage example

The finance industry relies on impeccable data accuracy, since even seemingly small errors—like an extra zero—can lead to serious regulatory consequences. When inconsistencies appear in accounting, data lineage allows firms to trace discrepancies back to their exact origin, thereby improving the credibility of financial reports.

Key components of data lineage

A data lineage framework includes five main data elements. Together, they offer a more granular view of data’s path within an organization.

• Data sources: Systems, tools, or applications that serve as the starting point of data, such as a database

• Data transformations: Modifications made to improve data, often through extract, transform, load (ETL) processes, as it moves through each step

• Data storage: Dedicated environments, such as data lakes, that house data as it moves through organizational workflows

• Data destinations: Applications or systems that receive and make use of processed data

• Metadata: Information that describes the properties and context of data

What is data lineage in data governance?

Data lineage contributes to compliance efforts, forming a critical part of effective data governance, a framework that supports responsible data use through policies centered on accuracy, privacy, and legal regulations. For example, data lineage facilitates full visibility into customer data collection, processing, and sharing as mandated by the California Consumer Privacy Act (CCPA) [1]. 

Types of data lineage

Depending on your role, you may encounter two common types of data lineage: business lineage and technical lineage. While business lineage maps the impact of data on business operations, technical lineage traces the backend processes that drive data movement and transformation.

• Business lineage: This gives you a high-level view of how data flows from its source to its destination. It highlights how data underpins organizational processes and contributes to strategic business planning. It’s especially helpful when you need to confirm data reliability as a business analyst, without getting into technical complexities. 

• Technical lineage: This type of lineage enables you to delve into tables, columns, and query-level data transformations. It’s the tool you turn to when you're managing pipelines or troubleshooting issues in detail as a data engineer or architect. 

It’s worth noting that several other forms of data lineage exist, based on the scope of tracking. For example, an intra-system data lineage, ideal for tracking data through a standalone system, enables you to analyze how data flows through different parts of the same system.

What is the difference between data flow and data lineage?

While data lineage is useful for tracing data-related errors, data flow provides insight into systems’ operation and interactions with data.  

Using data lineage helps you visualize what has happened to data over time. It reveals the source of the data, how it was transformed, and who made those changes. This gives you a clear timeline of your data’s history, which is crucial for maintaining data quality and accuracy.

Data flow is ideal for analyzing how data moves within a system. It illustrates the pathways and processes involved in data handling, acting as a map that supports system analysis and documentation.

Pros and cons of data lineage 

Data lineage tools offer multiple visualization options, including charts and diagrams, to portray the flow of mission-critical organizational data. Learn more about some notable benefits and challenges associated with implementing data lineage.

Advantages

Beyond data governance, data lineage plays a key role in reducing downtime, improving resource allocation, and enhancing visibility into data.

• Streamlined resource allocation: Data lineage helps identify duplicate reports, obsolete data, and inefficient processes, allowing for better allocation of computing and workforce resources.

• Reduced downtime: By highlighting dependencies ahead of time, data lineage allows rapid identification of downstream applications at risk, ensuring timely prioritization of fixes.

• Enhanced data visibility: The ability to track the origin and transformations of data builds stakeholder trust and promotes transparency across the organization.

Disadvantages 

Data lineage tools, while effective, can underperform when handling existing, and often outdated, infrastructure, large-scale data, or cloud environments.

• Incompatibility with existing systems: Integrating data lineage with existing systems can be particularly difficult due to outdated or inconsistent data formats and standards, making the process both complex and resource-heavy.

• May falter under high data load: As your data grows more complex, lineage information multiplies, which can obscure the insights you actually need.

• Reliability concerns in cloud environments: The abstraction between physical and virtual infrastructure in cloud computing complicates efforts to trace data origin and ownership, making regulatory lineage reporting challenging.

How to implement data lineage in your organization

A strong data lineage strategy begins with identifying documentation goals, cataloging vital data assets, choosing an appropriate tool, and actively tracking lineage records. Learn more about each of these critical steps below. 

1. Define objectives for data lineage.

Identify the reasons for adopting data lineage, and evaluate their relevance to your business goals. Common drivers include business evolution, data migration, audit demands, or regulatory compliance. 

2. Create an inventory of critical data assets.

Identify the most critical data elements for your business operations. Develop an inventory listing each asset’s sources, transformations, and destinations, which will serve as a foundation for mapping data lineage.

3. Select the right data lineage tool.

The right choice depends on your specific business goals and infrastructure. Choosing a tool that provides both technical and business lineage can help you bridge data flows with business operations. Also consider capabilities like automatic metadata capture, impact analysis, and compliance reporting to facilitate data governance. 

4. Actively monitor and maintain your data lineage records.

Keep your data lineage current by updating it whenever you introduce new data sources or adjust data flows. You may also consider becoming certified in data management to better validate data processes and records.

Explore our free data engineering career resources

From popular data visualization tools to career development tips, subscribe to Career Chat, a weekly LinkedIn newsletter by Coursera. Or, explore these free digital resources:

• Practice with a tutorial: How to Use Data Validation in Google Sheets

• Watch on YouTube: Career Spotlight: Data Engineer

• Hear from an insider: 6 Questions with an IBM Data Scientist and AI Engineer

Accelerate your career growth with a Coursera Plus subscription. When you enroll in either the monthly or annual option, you’ll get access to over 10,000 courses.

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial