Tracing Value: 6 Use Cases for Data Lineage

Introduction

“How will changing this data source impact our financial reports?”

“Can we prove our compliance with data privacy regulations for customer information?”

“Why do our sales forecasts differ from actual results, and where's the discrepancy originating?”

These are three examples of questions that can be answered by looking at the data lineage. This technology covers a lot of business critical use cases - and good lineage help organization keep their data infrastructure healthy and trustworthy. Lineage is such an important technology, it provides a level of transparency between data dependencies that allow companies to complete migrations in no time, automate documentation & PII tagging, ensure compliance, conduct impact analysis, and so much more.

In this piece, I’ll cover the practical use cases around data lineage - and explain how lineage can really improve the life of your data tesm, and by extension, of your whole business. data Lineage has considerable strategic value, beyond techical considerations, and it’s important to understand this value.

1. Data Governance

The first use case for data lineage is around governance and compliance. Data lineage plays an important role in ensuring that organizations adhere to regulations such as GDPR by providing a clear view of data flows and transformations throughout the system. The above image shows the overlap of Data Lineage and Data Governance. As you can see, lineage plays an important role in enforcing good governance.

One of the primary use cases for data lineage in compliance is tracking the lifecycle of sensitive data. For example, if you have a client’s email in your HubSpot table, you need to tag this column as containing personally identifiable information (PII) to comply with GDPR. Data lineage ensures that this tag propagates through all related data structures, maintaining the necessary compliance attributes across the entire data ecosystem.

Data lineage also helps in understanding the origin, movement, and transformation of data. This understanding is important for compliance audits, where you need to demonstrate that data handling practices meet regulatory requirements. For example, if modifications are made to a dataset, data lineage can show whether these changes were authorized and trace their impact throughout the system.

Data lineage also supports the establishment of data governance policies by providing visibility into data dependencies and usage. This visibility helps in identifying and managing risks associated with data breaches or misuse. For example, if an unauthorized change is made to a sensitive data element, data lineage can help trace back to the source of the change and assess its impact.

2. Data Migration

Migration and system overhaul are important and frequent data management projects, where data lineage plays a role. When moving from one data warehouse to another (for example, from Snowflake to BigQuery), understanding the existing data structures and dependencies is key for ensuring a good transition. At lot of clients leverage CastorDoc lineage to complete their data migration. Jimdo’s story illustrates this use case well.

One of the key aspects of migration is retro engineering, which involves understanding your current setup. Before you can migrate, you need to thoroughly comprehend the existing ETL processes, SQL queries, and data structures.Data lineage helps in mapping out these components, showing how data flows from source to destination, and highlighting any transformations along the way. This mapping is critical for replicating or redesigning these processes in the new environment.

During a migration, it’s common to take the opportunity to optimize and clean up the system rather than simply copying everything over. Data lineage helps identify which parts of the data infrastructure are essential and which ones are redundant or obsolete. This is something we will cover in more details later in this article.

Another important aspect of migration is understanding data dependencies. Data lineage allows you to see which tables, columns, and dashboards depend on each other. This knowledge helps in planning the order of migration steps to ensure that no critical dependencies are broken during the transition. For instance, you might start with less critical parts of the data infrastructure and gradually move towards the core components, ensuring that dependent processes remain functional throughout the migration.

Finally, migrating to a new data warehouse often involves rewriting SQL queries and ETL processes to fit the syntax and capabilities of the new system. Data lineage helps in this process by providing a clear picture of how existing queries and processes are structured.

3. Metadata propagation

Data lineage is also great at propagating metadata to the right places in your data warehouse - saving data teams plenty of time and efforts. For instance, if you tag a column at the source, the tag propagates to all downstream columns that have a one-to-one relationship with the original column. This ensures consistent governance without requiring manual intervention.

This propagation works for both tags and descriptions. If a column has a specific name and description, the description can be automatically propagated to other columns with the same name downstream. This avoids the manual effort of copying and pasting descriptions.

However, there’s a technical detail to consider: we avoid directly copying and pasting descriptions everywhere to prevent asynchronicity issues. Instead, we use symbolic links where the description remains at the source and is referenced downstream. This way, any changes to the source description are automatically reflected downstream, maintaining a single source of truth and preventing synchronization issues across the data lineage.

3. Debugging

Another use case of data lineage is related to debugging. For example, if you notice a data quality issue or something isn't working as expected, you can use data lineage to trace back to the source of the problem. Maybe a production table isn't being refreshed, which impacts all downstream processes relying on it. By following the lineage, you can pinpoint where the issue started, whether it's an upstream ETL job or a specific data entry, and address it directly. This approach saves a lot of time to data teams, and ensures that you resolve the root cause of the problem rather than just its symptoms.

4. Impact Analysis

Impact analysis is related to debugging but focuses on understanding the consequences of changes within a data system. This involves tracing the effects of modifying or deleting data elements, such as tables or columns, both upstream and downstream.

For instance, if you're considering deleting a table or a dashboard, it's essential to know what other elements rely on or feed into that component. Analysts might tell you that a particular dashboard is no longer in use, and you might think it's safe to delete. However, you must consider the broader impact. Are there any tables exclusively feeding this dashboard? If so, deleting the dashboard might make those tables orphans, which could then be removed as part of a cleanup process. Conversely, you should check if the dashboard pulls data from tables that are used elsewhere in the system. This comprehensive analysis prevents unintended consequences and helps maintain a clean, efficient data system.

On the downstream side, suppose you want to remove a column. You need to see how this change will impact subsequent SQL requests. For example, if there are SELECT * statements in your queries, removing a column from the first table in the graph will cause all subsequent queries to fail because they expect that column to be present. Therefore, understanding the downstream impacts is essential to prevent breaking downstream processes.

5. Reducing Technical Debt

Data lineage is also important for organizations looking to clean their data warehouses and reduce technical debt. By providing a full map of data flows and dependencies, data lineage helps identify unused or redundant tables, ETLs, and dashboards that can be safely removed.

For instance, if a dashboard is no longer in use, the tables feeding that dashboard might also be obsolete. Deleting these orphaned tables not only cleans up the data warehouse but also optimizes storage and reduces maintenance efforts.

Additionally, data lineage allows organizations to see where similar ETLs or tables can be merged, streamlining data processes and improving efficiency. This kind of cleanup is crucial for maintaining an organized and efficient data warehouse, ultimately reducing the technical debt that accumulates from outdated or unnecessary data structures.

For example, lineage can reveal tables or ETLs that are no longer in use or that can be consolidated, thus reducing technical debt and improving the overall efficiency of the new system.

6. Business Intelligence (BI) Enhancement

Data lineage plays a crucial role in enhancing business intelligence (BI) efforts by providing clarity on data origins and transformations. When a table or dashboard lacks proper documentation, lineage information acts as a signature, helping data analysts understand the sources and transformations applied to the data. For instance, if a data analyst encounters a specific field in a dashboard and is unsure about its contents, data lineage allows them to trace back to the source field in the original table. This tracing reveals any transformations or renaming that might have occurred along the way, providing the context needed to accurately interpret the data. By offering a clear view of how data flows and changes across systems, data lineage helps analysts make more informed decisions, improves the reliability of BI reports, and enhances the overall quality of insights derived from the data.

Conclusion

Data lineage is a powerful tool that provides critical insights into the flow and transformation of data within an organization. Its applications span across multiple areas, from ensuring regulatory compliance to facilitating complex data migrations. By offering transparency into data processes, lineage enables more effective governance, streamlines debugging efforts, and supports informed decision-making in data management.

in this piece - we looked at the many benefits of implementing data lineage. It enhances data governance, reduces technical debt, and improves the overall efficiency of data operations. For data teams, it saves a lot of time and resources while minimizing risks associated with data changes and updates.

As organizations continue to rely more heavily on data for decision-making, the importance of data lineage will only grow. At CastorDoc, we have developed a powerful data lineage technology already helping hundreds of organizations get more value out of their data. If you would like to see how this could work for your organization, get in touch with us and chat with the team.