What is Data Lineage?

This blog was last updated in October 2023

What is Data Lineage?

When you think of lineage, what typically comes to mind is one’s ancestry or pedigree. It’s a family tree that traces a path back past your parents, grandparents, and more, showing from whom you descended and how you’re related.

The same can be said for data, too. Data lineage shows the history of the data you’re looking at today, detailing where it originated and how it may have changed over time. It’s a reflection of the data life cycle, the source, what processes or systems may have altered it, and how it arrived at its current location and state.

Data lineage helps you understand the data you’re using, what may have happened to change it over time, and who was involved in any processing.

There are many reasons a data asset may change. It could have been transformed through filters or calculations. New data may have been added or deleted along the way, or new updates may have been made.

In this way, data lineage is a powerful tool for understanding. Knowing the data lineage helps you find the source of errors, determine the data’s suitability for usage, understand how processes can be optimized or improved, speed the time to insights, and much more.

Why is Data Lineage Important?

Data lineage helps you holistically understand the data you intend to use. Was it updated? Did it go through a particular process? Has the quality been evaluated or improved? Was it used by a specific team or in a specific report? The more you know about data lineage, the better decisions you can make about data usage.

Here are a few ways data lineage can improve data usage:

Enhancing Data Consistency

Data assets are bound to change over time. New data might be added, old data deleted, or alterations made. The way data changes can affect how you use the data, and the value of the data to your business. Understanding changes to data ensures you get the most benefit from the data.

Enabling Data-Driven Decision-Making

Data is required for strategic business success. Today, every company and every department needs to become more data driven and build a data culture. But that data must be understood to be best used, make better decisions, and keep the business viable. Data lineage helps you understand how and when data should be used.

Streamlining Data Migrations

Data migrations rely on understanding the data. You wouldn’t want to migrate bad or misunderstood data to new systems. Similarly, you need to understand how data has been sourced, stored, and altered to know how to migrate it and where to move it to. Data lineage provides these details so data migrations are more efficient and successful.

Strengthening Data Governance

Understanding data life cycles is critical to data governance. Data lineage adds to the value of audits and highlights areas of potential risk. Lineage can even reveal areas of noncompliance and conflicts with internal policies or external regulatory requirements.

Informing IT Optimization

Tracking data as it moves through your organization informs IT of applications, gaps, and usage patterns so they can be more responsive to business needs. This interactive data lineage also helps IT understand the scope of data assets so they can optimize their own efforts and better prepare for future requirements.

What is Data Lineage in ETL?

In ETL (extract, transform, load), data lineage refers to the process of documenting and visualizing how data moves through integration stages. This involves tracing where the data comes from, identifying the transformations applied to it, and monitoring its final destination within a data warehouse or database.

Data lineage creates a detailed map of how data moves from source to target database in a system. It shows the extraction from source systems, the transformations for business needs, and the loading into a database for analysis. It helps organizations maintain data quality, understand the impact of changes, ensure regulatory compliance, and resolve data pipeline problems.

By visualizing data lineage, ETL developers, data engineers, and analysts gain insights into data dependencies, potential bottlenecks, and areas where improvements can be made to enhance data accuracy and efficiency. Additionally, data lineage plays a crucial role in data governance, ensuring transparency and accountability throughout the entire ETL process.

Data Lineage vs Data Provenance vs Data Governance

Data lineage, data provenance, and data governance are closely related terms. They are all essential for an organization to maintain data quality and security over time. Together, they form a comprehensive framework for effective data management.

 Data Governance: Building the Foundation

At the heart of this framework is data governance, which establishes a structured way to manage data assets within an organization. It defines key components that govern the entire data lifecycle, including data owners, business terms, rules, policies, and processes.

Data Lineage: Visualizing Data Flow

Within data governance, data lineage solutions are crucial for ensuring data complies with set standards. They provide visibility into how data flows and transforms within the data pipeline. Imagine data lineage as the tool that shows the path of your data, from where it starts to where it ends.

Data Provenance: Tracing Data Origins

Data provenance, closely linked with data lineage, focuses specifically on the origins or source of data. Think of it as a birth certificate for your data, documenting where it originated, how it changed over time, and its historical context.

Data Lineage's Granular Audit Trail

Data lineage excels at creating a detailed audit trail for data at a granular level. This level of detail is highly useful for debugging data errors, which allows data engineers to troubleshoot effectively and find solutions quickly.

Although data governance covers more than just data lineage and provenance, these aspects are crucial for maintaining organizational standards in data management. Together, they form a strong system that ensures data assets are of high quality, secure, and aligned with the organization's goals.

The Future of Data Lineage

The future will include more data. Every day brings more technology and devices generating more data points. Businesses are also relying more on cloud-based services and technologies to generate, process, store, access, and analyze said data. Winning organizations will be those that quickly find ways to understand the data and then put it to good use; this will require more insights into data lineage.

And, as businesses migrate to the cloud, new capabilities emerge. Today, businesses are learning they can scale data governance and management efforts using the cloud. Tomorrow, they’ll seize on opportunities drawn from machine learning, automation, and AI. Many already are, as these technologies can automatically process and alter data. But, they can also log every transformation—in the form of data lineage—making the data, and the data lineage, more crucial and valuable.

Organizations need to understand data lineage before data can be effectively used. That’s already a fact today, and the future will only expand the gap between the data in front of you and the original data, making data lineage even more important tomorrow.

What Is the Value of Data Lineage in Business?

Reliable data is essential for effective decision-making and improving operations in all areas of your business, from human resources to sales. The real value of this information comes from stakeholders trusting its accuracy, as the quality of insights depends on the quality of the data.

This is where data lineage comes in. It provides a clear view of possible changes due to data migrations, system updates, errors, and more. It acts as a guardian for data integrity throughout its entire lifecycle.

How Data Lineage Works

Data lineage is a comprehensive tool that tracks and documents the entire journey of data within an organization. Here's a breakdown of how it works:

Data Source Identification

Data lineage starts by identifying different data sources in an organization, including databases, data warehouses, external providers, and spreadsheets. Each source is documented to create a complete inventory of data assets.

Mapping Data Movement

After identifying data sources, data lineage maps out how data moves from source to destination. It traces the path as data is extracted, transformed, and loaded (ETL) into various systems or applications. This involves recognizing data transformations, conversions, and any cleansing processes.

Dependency Tracking

By tracking dependencies between data elements, data lineage identifies which datasets or reports rely on specific data sources. This information is crucial for understanding the potential impact of changes to underlying data.

Data Quality Monitoring

Data lineage often includes data quality metrics and monitoring. It helps organizations assess the quality of data at various stages of its journey. Data quality checks can identify errors, inconsistencies, or anomalies in the data.

Timestamps and Versioning

Timestamps record when data was accessed, modified, or moved. This historical view allows organizations to trace data changes over time. Versioning is particularly important for auditing and compliance.

Visualization

Data lineage is often presented visually through diagrams or flowcharts. These visual representations make it easier for stakeholders to understand how data moves and transforms within the organization. Visualization tools may use different symbols or colors to represent different data actions.

Metadata Integration

Metadata, which includes information about data structures, definitions, and usage, is integrated into data lineage. Metadata helps provide context for data elements and assists in understanding their meaning and relevance.

User Access and Permissions

Data lineage may also include information about who has access to specific data elements and what permissions they have. This is vital for data security and compliance.

Impact Analysis

"What reports will be affected if we update this data source?" Questions like this are answered through impact analysis. It assesses the potential consequences of changes or issues related to data sources, transformations, or data elements.

Data Governance and Compliance

Data lineage plays a crucial role in data governance by enforcing data policies, standards, and compliance requirements. It ensures that data is used in accordance with regulations and organizational guidelines.

Operational Efficiency

By providing transparency into data processes, data lineage helps improve operational efficiency. Organizations can identify bottlenecks, redundancies, and opportunities for automation.

Data Lineage Techniques

While data lineage may seem straightforward, capturing and documenting data lineage can be done in many different ways. Here are just a few data lineage techniques.

Pattern-Based Lineage

Pattern-based lineage relies on metadata to determine data lineage, looking for patterns in the metadata and related data to classify or define different data. Pattern-based data lineage does not involve adjacent systems or tools, but only looks at the data itself. This provides the advantage of flexibility, as this method can determine the lineage of any data across any system or technology. But, since it is a relatively simplistic method, it might miss or misunderstand some data complexities or nuances.

Data Lineage by Tagging

Data lineage by tagging is sometimes called self-contained data lineage. It’s applicable when data is stored, processed, and managed within a single, self-contained system that tags data as it moves through its life cycle. The advantage is that the tagging is built into the system; the disadvantage is that it only works when you’re using a single system.

Data Lineage by Parsing

Data lineage by parsing is a more advanced method of data lineage. It employs software and automation to understand the logic used to process or transform data. This technique is good for capturing change across systems because it tracks data as it moves. The drawback is that this approach is more complex, and requires advanced understanding of the tools, programming languages, and systems used across the entire data life cycle.

5 Data Lineage Best Practices

To ensure that your data lineage process remains effective and provides valuable insights into your datasets, consider these best practices:

1. Engage Business Executives and Users Early

Effective data lineage initiatives, like data governance programs, need backing from senior executives. This includes their approval, as well as funding. Also, involve business managers and users to ensure that data management teams understand how data is used in business processes and to verify the relevance and accuracy of data lineage information.

2. Document Business and Technical Data Lineage

Various stakeholders use data lineage for different purposes. Business lineage, which focuses on the origin and flow of data from a high-level perspective, provides valuable context. Technical lineage, which caters to IT professionals, involves data transformations, integrations, and pipelines, catering to IT professionals. Collecting both types of lineage information ensures that it meets the needs of business users, analytics teams, and IT experts.

 3. Align Data Lineage with Real Business and IT Requirements

Data lineage should not be an academic exercise. To maximize its benefits, it should facilitate informed business decisions, enhance data governance, improve data quality, and contribute to broader data management objectives. Ensure that it addresses genuine business and IT needs to avoid being an underutilized investment.

4. Adopt an Enterprise-Wide Approach

Make sure that data lineage serves practical purposes. It should aid in making informed business decisions, strengthen data governance, enhance data quality, and contribute to broader data management goals. Avoid it becoming an underutilized investment by ensuring it meets real business and IT needs.

5. Build a Data Catalog with Embedded Lineage Information

Finding and understanding important data can be a big challenge for business intelligence and analytics users. Simplify their experience by making a data catalog that not only lists available data resources but also includes lineage information. This gives users insights into the data’s origins and how it changes, making data discovery more efficient.

By following these data lineage best practices, you can enhance the value and effectiveness of your data management efforts.

Data Lineage Use Cases

The demand for real-time insights has never been greater. Teams can leverage end-to-end data lineage tools to enhance their workflows in several impactful ways:

1. Data Modeling

To visually represent how various data elements are connected within an organization, it's crucial to define the supporting data structures. Data lineage is vital in modeling these complex relationships, showing dependencies across the data ecosystem.

As data evolves with new sources and integrations, the overall data model must adjust. Data lineage ensures an accurate reflection of these changes through model diagrams, highlighting new connections or removed tables. This empowers analysts and data scientists, enabling them to conduct insightful analyses with a better understanding of the data sets.

2. Data Migration Planning

During data migration projects, where data is moved to new storage systems or new software solutions are introduced, understanding data locations and lifecycles becomes parmount. Data lineage offers a detailed view of how data has evolved within the organization, helping teams plan carefully for system migrations or upgrades.

This efficient approach speeds up the transition to the new storage environment and provides ways to optimize the data system by archiving or purging outdated data. Ultimately, this enhances overall system performance by reducing the volume of data requiring management.

3. Compliance Assurance

Data lineage ensures compliance by aiding in audits, improving risk management, and guaranteeing strict adherence to data governance policies and regulations. For example, GDPR legislation in Europe and state-level data protection policies in the United States require robust data protection measures.

Data lineage tools empower organizations to efficiently address compliance challenges, as failing to comply can be both resource-intensive and costly.

4. Impact Analysis

Data lineage tools offer insights into the potential impact of business changes, especially for downstream reporting. For instance, if a data element's name changes, data lineage helps leaders understand the potential effects on dashboards and users relying on that reporting. Additionally, data lineage assesses the impact of data errors across the organization, tracing them to their origins. This facilitates improvements in data processing and effective communication with relevant teams to address issues.

To summarize, data lineage is a key element in modern data management. It provides transparency, governance, and optimization for various business processes. It enables organizations to use data as a strategic asset, paving the way for data-driven decision-making and fostering a culture of innovation.

How to Use Data Lineage to Ensure Data Quality

Considering the number of data assets and complexity of data infrastructures in most enterprises down to even midsize businesses, it’s nearly impossible to manage data lineage manually. A dedicated data lineage tool is a more prudent choice, and can automatically and intelligently map data lineage from the point of origin to its current location. But, regardless of which data lineage tool you choose, the process for getting started is similar.

Steps to Use Data Lineage to Support Data Quality

1. Identify your data landscape with a list of data assets, systems, and locations. Consider surveying workers to ensure no data elements are missed or related processes are overlooked.

2. Trace data back to its origin to create a connection between where data is now and where it was generated.

3. Pinpoint data sources and interactions along the way, including details on links and systems.

4. Generate a data lineage map at the individual system level and at the macro organization level.

Building your data lineage map is not a trivial task. This project may take a considerable amount of time and effort, depending on how well your data is currently documented and the breadth of your data landscape. Again, a data lineage tool can be very helpful in accelerating your data lineage efforts by using automation and AI.

What to Look For in Data Lineage Tools

Choosing the right data lineage tools requires a strategic assessment of factors aligning data management with business objectives. Consider diversity in data sources, formats, volumes, and the complexity of data flows. Equally important are your data governance framework, policies, and roles, forming the structure for data lineage practices. To make an informed decision, evaluate data lineage tools based on these essential criteria:

1. Data Source Versatility

Prioritize tools that effortlessly access a wide range of data sources and employ AI-driven metadata extraction.

2. Centralized Metadata Repository

Look for solutions that aggregate metadata into a centralized hub for effective management.

3. Visualized Representations

Choose tools that offer clear visualizations of data lineage to enhance comprehension.=

4. Developer-Friendly APIs

Opt for tools with APIs to enable custom application development for lineage record queries.

5. Efficient Search Capability

Ensure that the tool provides robust search functionality for rapid data flow tracing, both forward and backward.

Alation’s Data Lineage Tools

Effective data lineage requires accuracy, completeness, and detail. Alation provides advanced data lineage tools for comprehensive visibility and understanding of the data life cycle across your enterprise. Capabilities include automated, table-to-table lineage, as well as lineage APIs for manual augmentation of data flow graphs.

In partnership with Manta, Alation automates the process of generating enriched, column-level lineage across data sources and reduces the need for technical resources to develop end-to-end lineage. The integrated Alation+Manta solution offers advanced, cross-system, column-level lineage to ease regulatory compliance adherence, perform impact analysis, and notify stakeholders of upstream data changes in real-time.

To learn more about Alation for data lineage, sign up today to get a demo.