Parth Shukla — Updated On May 2nd, 2023
Azure Cloud Computing Data Engineering Intermediate

Currently, most businesses and big-scale companies are generating and storing a large amount of data in their data storage. Many companies are there which are entirely data-driven. Businesses and companies are using data to get insights about the progress and future steps for business growth. In this article, we will study the data lineage and its process, the significant reasons behind businesses investing in it, and the benefits of it, with its core intuition. This article will help one understand the whole data lineage process and its applications related to business problems.

What is Data Lineage?

Data lineage is a process of getting an idea about where the data is coming from, analyzing it, and consuming it. It reveals where the data has come from and how it has evolved through its lifecycle. It traces where the data was generated and the steps in between it went through. A clear flowchart for each step helps the user understand the entire process of the data lifecycle, which can enhance the quality of the data and risk-free data management.

Data Lineage

The main objective of it is to track the data from where it has been generated and the path it follows throughout its lifecycle. Some significant data-driven companies like Netflix, Google, Coca-cola, Microsoft, and Uber use data lineage for many purposes.

  • Data lineage enables companies to track and solve problems in the path of the data lifecycle.
  • It provides a thorough understanding of the solutions to errors in the way of the data lifecycle with lower risk and easy solution methods.
  • It allows companies to combine and preprocess the data from the source to the data mapping framework.
  • Data lineage helps companies to perform system migration confidently with lower risk.

Top Data Lineage Tools

Data lineage tools help organizations manage and govern their data effectively by providing end-to-end data lineage across various data sources, enabling data discovery, mapping, and data lineage visualization, and providing impact analysis and data governance features.

Here are some of the top data lineage tools and their features:

1. Alation

Alation provides a unified view of data lineage across various data sources. It automatically tracks data changes, lineage, and impact analysis. It also enables collaboration among data users.

2. Collibra

Collibra provides end-to-end data lineage across various data sources. It enables data discovery, data mapping, and data lineage visualization. It also provides a business glossary and data dictionary management.

3. Informatica

Informatica provides data lineage across various data sources, including cloud and on-premise. It enables data profiling, data mapping, and data lineage visualization. It also includes impact analysis and metadata management.

4. Apache Atlas

Apache Atlas provides data lineage for Hadoop ecosystem components. It tracks metadata changes, lineage, and impact analysis for data stored in Hadoop. It also enables data classification and data access policies.


MANTA provides data lineage for various data sources, including cloud and on-premise. It enables data discovery, data mapping, and data lineage visualization. It also provides impact analysis and data governance features.

6. Octopai

Octopai provides automated data lineage for various data sources, including cloud and on-premise. It enables data discovery, data mapping, and data lineage visualization. It also includes impact analysis and data governance features.

Data Lineage Application Across Industries

Data lineage is a critical process across various industries. Here are some examples:

  • Healthcare: In the healthcare industry, data lineage is important for ensuring patient data privacy, tracking data lineage for medical trials, and tracking data for regulatory compliance.
  • Finance: Data lineage helps financial institutions comply with Basel III, Solvency II, and CCAR regulations. It also helps prevent financial fraud, risk management, and transparency in financial reporting.
  • Retail: In the retail industry, data lineage helps in tracking inventory levels, monitoring supply chain performance, and improving customer experience. It also helps in fraud detection and prevention.
  • Manufacturing: In manufacturing, data lineage tracks the production process and ensures the quality of the finished product. It helps identify improvement areas, reduce waste, and improve efficiency.
  • Government: Data lineage is critical for ensuring transparency and accountability. It supports regulatory compliance, public data management, and security.

Why Are Businesses Eager to Invest in Data Lineage?

Just the information about the source of the data is not enough to understand the importance of the data. Some preprocessing on data, error solution in between the path of data, and getting key insights from the data is also important for a business or company to focus on.

Knowledge about the source, updating of the data, and consumption of the data improves the quality of the data and helps businesses get an idea about further investing in it.

There are some advantages of data lineage because of which businesses are investing in it.

  • Profit Generation: For every organization, generating revenue is the primary need to grow the business. The information tracked from data lineage helps improve risk management, data storage, migration process, and hunting of some bugs in between the path of the data lifecycle, etc. Also, the insights from the data lineage process help the organizations understand the scope of profit and can generate revenue.
  • Reliance on the data: Good quality data always helps to keep the business running and improving. All the fields or departments, including IT, Human resources, and marketing, can be enhanced through data lineage, and companies can rely on data to improve and keep tracking things.
  • Better Data Migration: There are some cases where there is a need to transfer the data from one storage to another. The data migration process carries out very carefully as there is a high amount of risk involved in it. When the IT department needs to migrate the data, data lineage can provide all the information about the data for the soft data migration process.

How to Implement Data Lineage?

Implementing data lineage in an organization involves several steps. Here is a detailed guide on how to implement data lineage:

  1.  Identify Data Sources

    Start by identifying the data sources that are used in your organization. These could be databases, applications, or files.

  2. Define Data Elements

    Identify the data elements that are important to your organization. This could include customer information, financial data, or product data.

  3. Map Data Flows

    Once you have identified your data sources and elements, you can start mapping data flows. It involves identifying data movement across your organization, including how data is transformed and stored.

  4. Use Data Lineage Tools

    Several data lineage tools available in the market can help you automate the process of mapping data flows. These tools can also help you track changes in data over time.

  5. Establish Data Governance Policies

    Establish data governance policies to ensure data is accurately captured and maintained throughout its lifecycle. This includes defining data quality standards, data retention policies, and data access policies.

  6. Monitor Data Lineage

    Regularly monitor your data lineage to ensure that it remains accurate and up-to-date. This will help you identify any issues or inconsistencies in your data.

  7. Train your Staff

    Provide training to your staff on using data lineage tools and interpreting the data lineage information. 

Data Lineage to build transparency
Source: Y42

Benefits of Data Lineage

There are some obvious benefits of the data lineage, which is why businesses are eager to invest in the same.

Some major benefits are listed below:

Data Lineage


1. Better Data Governance

Data governance is the process in which data is governed, and analysis of the source of the data, the risk attached to it, data storage, data pipelines, and data migration is performed. Better data lineage can help conduct better data governance. Good quality of it can provide all this information about the data from its source to consumption and help achieve a better data governance process.

2. Better Compliance and Risk Management

Major data-driven companies have a huge amount of data, which is tedious to handle and keep organized. There are some cases where there is a need for data transformation or preprocessing data; during these types of processes, there is a huge risk involved lose the data. Better data lineage can help the organization keep the data organized and reduce the risk involved in the process of migration or preprocessing.

3. Quick and Easy Root Cause Analysis

During the entire data lifecycle, many steps are in between, and many bugs and errors are involved. With a good-quality data lineage, it can help businesses to find the cause of the error easily and solve it efficiently with less amount of time.

4. Easy Visibility of the Data

In a data-driven organization, due to a very high amount of data stored, it is necessary to have easy visibility of the data to access it quickly while spending less time searching for it. Good-quality data lineage can help the organization access the data quickly with easy data visibility.

5. Risk-free Data Migration

There are some cases where data-driven companies or organizations need the migrations of the data due to some errors occurring in existing storage. Data migration is a very risky and hectic process with a higher rate of data loss risk involved. It can help these organizations conduct a risk-free data migration process to transfer the data from one to another data storage.

Data Lineage Challenges

Lack of Standardized Data Lineage Metadata 

It becomes difficult to track data lineage consistently across different systems and applications. Solution: Standardizing metadata and using common data models and schemas can help overcome this challenge.

Complex Data Architectures

With the growth of big data, there is an increase in the complexity of data architectures, making it harder to trace data lineage across multiple systems and platforms. Solution: Using advanced data lineage tools and technologies designed to work with complex data architectures can help overcome this challenge.

Data Lineage Gaps

There can be gaps in data lineage due to incomplete or inconsistent data, missing metadata, or gaps in the data collection process. Solution: Establishing a comprehensive data governance framework that includes regular data monitoring and auditing can help identify and fill data lineage gaps.

Data Lineage Security and Privacy Concerns

Data lineage information can be sensitive and require protection to avoid security and privacy breaches. Solution: Implementing appropriate security measures, such as data encryption and access controls, and complying with data privacy regulations can help to ensure data lineage security and privacy.

Lack of Awareness and Training

Lack of awareness and training among data stakeholders on the importance and use of data lineage can lead to limited adoption and usage. Solution: Providing training and awareness programs to educate data stakeholders on the importance and benefits of data lineage can help to overcome this challenge.

Data Lineage vs Other Data Governance Practices

Data lineage is a critical component of data governance and is closely related to other data governance practices, such as data cataloging and metadata management. However, data cataloging is the process of creating a centralized inventory of all the data assets in an organization. At the same time, metadata management involves creating and managing metadata associated with these assets.

Data lineage helps establish the relationships between data elements, sources, and flows and provides a clear understanding of how data moves throughout an organization. It complements data cataloging and metadata management by providing a deeper insight into data’s origin, quality, and usage.

While data cataloging and metadata management provide a high-level view of an organization’s data assets, data lineage provides a granular understanding of how data is processed, transformed, and used. Data lineage helps to identify potential data quality issues, track changes to data over time, and ensure compliance with regulatory requirements.

Data Mapping vs Data Lineage

Data MappingData Lineage
Focuses on identifying the relationships between data elements and their corresponding data sources, destinations, and transformations.Focuses on tracking the complete journey of data from its origin to its final destination, including all the data sources, transformations, and destinations in between.
Primarily used to understand data flow between systems and applications.Primarily used to understand the history and lifecycle of data within an organization.
Typically involves manual or semi-manual documentation of data flows.Can be automated or semi-automated using tools and platforms that capture and track metadata.
Often used for specific projects or initiatives, such as data integration or data migration.Used for ongoing data governance and compliance efforts, as well as for specific projects.
Helps ensure consistency and accuracy in data movement across systems.Helps ensure data quality and compliance with regulatory requirements by providing a clear understanding of data lineage.

Regulatory Compliance

  • Compliance with regulations like GDPR and CCPA requires companies to comprehensively understand their data.
  • Data lineage provides a detailed data usage history, making it easier to comply with regulations like GDPR and CCPA.
  • With data lineage, organizations can easily identify where data is being stored, who has access to it, and how it is being used.
  • By maintaining a clear data lineage, organizations can demonstrate compliance to regulatory bodies and provide evidence of their data privacy and security practices.
  • Data lineage can also help with compliance by enabling organizations to easily audit their data usage and identify areas that may be non-compliant.
  • Data lineage can be particularly useful in the case of data breaches, as it allows organizations to quickly identify what data was affected and take appropriate action to notify affected individuals and regulatory bodies.

Future of Data Lineage

  • Adoption by More Industries: As more industries recognize the importance of data governance, data lineage will become more widely adopted as a critical tool for ensuring regulatory compliance and data quality.
  • Increased Automation: Automation will play a more significant role in data lineage, reducing the amount of manual effort required to maintain data lineage and providing more timely and accurate data lineage information.
  • Integration with Machine Learning and AI: Data lineage will be integrated with machine learning and artificial intelligence to enhance its capabilities for data discovery, quality management, and governance.
  • Improved Interoperability: Improved interoperability between data lineage tools and other data management systems will allow for more comprehensive data governance across organizations.
  • Greater Emphasis on Security: With increased concerns about data breaches and cyber threats, data lineage will be essential in ensuring data security by tracking data access and providing visibility into how data is used.
  • Emergence of Blockchain-based Data Lineage: Blockchain technology is being explored to provide more secure and transparent data lineage by creating an immutable record of data transactions.

Way Ahead

Data lineage is a crucial step for any organization that deals with data. By implementing data lineage, companies can achieve better data governance, manage risks more effectively, and gain easy access to data. Top companies like Netflix, Google, and Microsoft have already embraced data lineage and have significantly benefited from it. So, if you want to learn more about data lineage and other essential data skills, consider enrolling in our Blackbelt program. It’s a comprehensive program that will help you become an expert in data science and analytics.

Frequently Asked Questions

Q1. What is data lineage in ETL?

A. Data lineage in ETL refers to the complete end-to-end history of the data from its source to destination, including transformations and metadata changes.

Q2. What are the two types of data lineage?

A. The two types of data lineage are forward lineage and backward lineage. Forward lineage tracks data flow from source to destination, and backward lineage tracks data flow from destination to source.

Q3. What is data governance and data lineage?

A. Data governance is a process of managing data quality, security, and compliance, while data lineage is a part of data governance that tracks the data flow across the organization.

Q4. What is the difference between data mapping and data lineage?

A. Data mapping involves associating source data with target data, while data lineage tracks the flow of data and metadata across various systems.

Q5. What is data lineage of a dataset?

A. Data lineage of a dataset refers to the origin of the data, its transformations, and the places where it has been stored or used.

Q6. Is data lineage a metadata?

A. Yes, data lineage is a type of metadata that provides information on the movement and transformation of data across different systems.

About the Author

Parth Shukla

UG (PE) @PDEU | 25+ Published Articles on Data Science | Data Science Intern & Freelancer | Amazon ML Summer School '22 | AI/ML/DL Enthusiast | Reach Out

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