Benefits of Data Lineage : Enhancing Trust and Unlocking Business Insights
📚 Learn how data-driven businesses are leveraging lineage to facilitate self-service analytics, data governance, risk mitigation and more.
Data is important for modern businesses. It can help a company grow, but if it's not well managed, it can quickly become a liability. That's where data lineage comes in. Data lineage is when you track and keep records of data from where it started to where it ends up. With the increasing volume and variety of data being generated by organizations today, the importance of data lineage has grown significantly. In this article, we'll discuss the top benefits of data lineage and why businesses are eager to invest in this technology.
Top Benefits of Data Lineage
Improved Data Quality: Data lineage helps organizations maintain the accuracy and integrity of their data, ensuring that critical decisions are based on trustworthy information.
Better Data Governance: Data lineage provides a clear understanding of the flow of data within an organization, making it easier to maintain data accuracy, integrity and consistency. This leads to better data governance, where data is managed and controlled in a systematic and organized manner, reducing the risk of data breaches or inaccuracies.
Efficient Root Cause Analysis: Data lineage makes it easier to quickly identify the source of data errors or issues, which can lead to time-consuming and costly fixes. By having a clear understanding of the data flow, organizations can quickly and easily perform root cause analysis, reducing the time and cost of resolving data issues.
Improved Compliance: Data lineage helps organizations stay compliant with data privacy regulations by providing a complete view of where data is stored, processed, and transmitted. This makes it easier to detect and address potential security risks and ensure that data is handled in a secure manner, reducing the risk of data breaches or violations.
Clear Visibility of Downstream Impact: Data lineage provides organizations with a clear understanding of how changes to their data will impact other parts of the business, helping them make informed decisions.
Autonomous Data Quality: With data lineage, organizations can monitor and manage the quality of their data in real-time, reducing the risk of data errors and improving overall data quality.
Easy Auditing and Documentation: Data lineage provides a complete and detailed view of the data journey, making it easier to perform audits and maintain records. This helps organizations meet regulatory requirements, maintain data privacy, and ensure data accuracy, leading to more efficient and effective auditing and documentation processes.
These are just a few of the many benefits of data lineage. For a more in-depth understanding of the benefits, please refer to: The top benefits of data lineage, AI multiple guide to data lineage or Imperva what is data lineage.
Real-world Examples: How Data Lineage is Empowering Businesses
Here are some of the highly regarded and established data-driven companies that have embraced the implementation of data lineage and are now reaping its numerous advantages:
1.  Improved Data Infrastructure Reliability and Efficiency at NetflixÂ
2.  Easy Operational Maintenance and Better Execution of Data Programs at Slack
3.  Visualizing Data Timelessness at AirBnB
4.  Risk Mitigation and Driving Compliance at UBS
5.  Moving Beyond Data Discovery at Postman
1. Â Â Improved Data Infrastructure Reliability and Efficiency at Netflix
Netflix is a big company that provides online streaming services to people. They need their data to be good, so they can run their business well. So, they decided to use data lineage to help them.
Netflix has implemented a data lineage system to improve the reliability of its data infrastructure. The system is designed to track the flow of data through the entire data pipeline, from the ingestion of raw data to the final output of data products. The data lineage system is integrated with Netflix's data catalog and metadata management system, providing a comprehensive view of the data landscape at the company.
In order to ensure that the metadata was accurate, they created a number of custom applications and plugins that can capture and maintain metadata information. This includes details such as data source, data lineage, data relationships, data quality, and data privacy. This has allowed Netflix to make informed decisions about how to store and manage data, and how to ensure the quality of its data.
In short, Netflix's implementation of data lineage has resulted in improved data infrastructure reliability and efficiency, which has helped them in making better data-driven decisions.
2. Â Â Easy Operational Maintenance and Better Execution of Data Programs at Slack
Slack, the well-known team collaboration platform, faced a challenge in ensuring the accuracy and efficiency of its data programs. As data powers the platform and drives crucial business decisions, it is vital to maintain its integrity.Â
Slack has developed a custom data lineage tool that helps to address these challenges by providing a centralized repository for tracking data flow and lineage information. The tool is designed to be scalable, flexible, and extensible, and it integrates with other systems and platforms to provide a comprehensive view of the data landscape at Slack.
Furthermore, data lineage not only improves operational maintenance and efficiency, but it also helps organizations identify and address data quality concerns. This was particularly crucial for Slack, as maintaining accurate and trustworthy data is essential to their business.Â
By implementing data lineage, Slack was able to proactively identify and resolve data quality issues, leading to a more reliable and trustworthy data infrastructure.
3. Â Â Visualizing Data Timelessness at AirBnB
At AirBnB, a challenge was ensuring the accuracy and up-to-date nature of their data, crucial to powering their platform and making critical business decisions. By utilizing a custom data lineage, AirBnB was able to visualize the timelessness of their data and understand the impact of any changes made to it. This ensured that the company was making informed decisions based on accurate and up-to-date data and prevented negative impacts to their operations.
Data lineage allowed AirBnB to clearly track and visualize data from its origin to its endpoint, making root cause analysis quick and efficient in the event of any issues. This improved their overall data management and quality, resulting in a more reliable and trustworthy data infrastructure.
Learn more about the benefits of data lineage for visualizing data timelessness at AirBnB.
4. Â Â Risk mitigation and driving compliance at UBS
UBS, one of the world's largest wealth management firms, leveraged data lineage to mitigate risk and drive compliance. One of the biggest challenges for organizations in the financial industry is ensuring compliance with regulations and managing risk. This is especially important for companies like UBS, who must maintain a high level of security and privacy for their clients.Â
UBS implemented a graph database solution using Neo4j, which is a graph database management system. The solution provided UBS with a more flexible and scalable way of managing complex data relationships, as well as improved data discovery capabilities.Â
With data lineage, UBS was able to understand the flow of sensitive data and ensure that it was being used in accordance with regulations and privacy policies. It also helps organizations identify and address any potential security or privacy risks. This is crucial for UBS, who must ensure that their client data is protected. By tracking and documenting the flow of data from its origin to its ultimate destination, UBS was able to quickly identify and address any security or privacy issues, reducing the risk of data breaches and ensuring compliance with regulations such as MiFID II.
 Learn more about the benefits of data lineage for risk mitigation and compliance at UBS
5. Â Â Moving beyond data discovery at Postman
Postman, the popular API development platform, faced the challenge of moving beyond data discovery and achieving deeper insights into their data.
Postman was facing challenges with its existing data infrastructure, which was unable to effectively manage and analyze the large amounts of data generated by its platform. This was resulting in data loss, inconsistent data quality, and difficulties in generating meaningful insights from the data.
To address these challenges, Postman implemented a new data stack that incorporated a missing layer for data management and analysis:
Documenting with Confluence
Creating a data dictionary with Google Sheets
Implementing a pre-built data workspace with Atlan
The new data stack was designed to provide a more scalable and flexible infrastructure for managing and analyzing data, as well as to ensure the reliability and consistency of the data.
The implementation of the new data stack has enabled Postman to effectively manage and analyze the large amounts of data generated by its platform, resulting in improved data quality, reduced data loss, and enhanced data analysis capabilities.
Learn more about the good of using data lineage for data discovery at Postman
These are just a few examples of the many companies that have invested in data lineage and are reaping the rewards. In today's data-driven world, companies need to ensure that they have the right tools and processes in place to manage their data effectively. By using data lineage, companies can have greater visibility into their data, allowing them to make informed decisions, reduce risk, and drive compliance.
For organizations working with disparate data sources, large volumes, and a variety of data, the benefits of data lineage are clear. It provides a comprehensive view of a company's data operations, making it easier to identify potential risks and take corrective actions. Additionally, data lineage is also crucial in ensuring compliance with regulations such as GDPR, HIPAA, and SOX.Â
  To conclude, data lineage is a paramount aspect of contemporary data infrastructure and holds immense significance for organizations striving to maintain the authenticity, security, and regulatory compliance of their data. The advantages of incorporating data lineage are evident, and a plethora of companies, both large and small, across various industries, have already begun to reap its benefits. As the digital landscape continues to evolve, the importance of data lineage will only continue to grow, and organizations that prioritize it will be in a much better position to succeed in the long term.
To learn more about data lineage, you can visit Atlan’s what is data lineage,  Atlan's platform for data lineage governance, Atlan’s open source data lineage tools or read Atlan's data catalog primer. You can also find a wealth of information on the topic by exploring the following resources:
I can also answer the question: why are companies so eager to invest in data lineage solutions? In my opinion, the answer is simple - data lineage provides organizations with the visibility, transparency, and control they need to manage their data operations effectively. With the right data lineage solution in place, organizations can ensure that their data remains accurate, consistent and secure, even as it moves through various systems and platforms. This helps to minimize the risk of data breaches, comply with data privacy regulations, and maintain the integrity of data.
Data lineage solutions are typically built on top of a data catalog, which is a central repository of all the data assets in an organization. A data catalog is essential for data lineage, as it provides the context and metadata required to understand how data is related and where it has come from. You can read more about data catalogs and how they play a role in data lineage governance in our Data Catalog Primer.
Atlan, for example, provides a platform for data lineage governance that offers a unified view of data operations and helps organizations to understand the relationships between their data assets. The platform integrates with existing data systems and provides a complete picture of how data is transformed and used. It also helps organizations to identify and resolve any data quality issues, improving the accuracy of their data and reducing the risk of incorrect or misleading information.
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