Leveraging Data Lineage Tools to Drive Business Value

Many organizations today recognize the importance of having a good system in place for data lineage as part of a broader data governance program. In the complex landscape of modern data engineering, understanding and providing access to the “narrative” of organizational data—its origin, transformations, and endpoint—is crucial. This narrative, known as data lineage, acts as a compass for data teams, guiding them through the intricacies of data ingestion, transformation, and consumption. This article expands on the common applications of data lineage, highlights key requirements for data lineage mapping, and underscores the significant advantages it brings to data-driven enterprises.

Democratizing Data Discoverability Through Data Lineage

This is perhaps the most popular need for data lineage – providing organizational-wide access to understanding what data assets – for example tables and views – exist, how they are created, and who is using them. A key need is driven by the teams that are consuming data, for example Analytics and Data Science. These teams need a place to go for them to understand:

1. What do we have, e.g., is there an existing resource that we can use for a new project
2. How is it created, e.g., what sources feed the table that we are looking at, and how are the different fields it has are derived from those sources
3. Occasionally, teams may also want to know what is already used and how popular it is, for example, to understand how to use a certain data asset or to confirm the appropriate one to use

Data Discoverability has emerged as a popular Data Catalog functionality, commonly found in metadata tools which, as the name suggests, index and catalog data in warehouses and databases so that consumers can have a search interface for these assets. Data Catalogs have become an important part of a modern data platform due to this functionality.

Elevating Data Quality Through Data Lineage

A second application for data lineage is the improvement of data quality. Here, there are typically two types of use cases where accurate lineage information can directly impact data quality improvements:

1. Preventing data quality issues
2. Root cause analysis when data quality issues occur

Preventing data quality issues describes mostly the situation in which a change is introduced, which as a result, whether intentionally or unintentionally, introduces a change to the data that has unexpected consequences. For example, a schema change in an upstream database can result in query failures in downstream business intelligence dashboards. Through accurate lineage information, and a proper change management procedure, we could argue that such issues can be avoided - the individual making the change should get the technical support and should follow some procedure to validate, communicate, and deploy the change, without having unexpected consequences. Given the complex nature of data architectures, doing this successfully remains a challenge. However, it’s fairly intuitive to establish that accurate lineage information covering all parts of the data lifecycle is foundational to these changes running continuously and successfully.

The process of root cause analysis (RCA) involves pinpointing the earliest contributor to a certain issue, for example a data incident, that is usually detected downstream where the data is consumed. When teams attempt to root-cause an issue, the goal is to find both the entire path that is affected, as well as the reason for the issue, so both could be remediated quickly. In the case of data pipelines, RCA accuracy is also time critical – the quicker it is done, less data is impacted which ultimately means less data that needs to be corrected. Therefore, it’s easy to understand why end-to-end lineage is extremely important for this use case as well, as it allows organizations to efficiently handle data incidents and address the core reasons that had created them in the first place.

For these two reasons, data lineage serves as an indispensable tool in the arsenal of data engineers. Having traceability ensures that any anomalies detected in data outputs can be quickly attributed to their sources, whether these are inaccuracies in the source data or errors in the source code which are usually introduced through code changes.

For data engineering professionals, leveraging data lineage translates to a proactive approach in maintaining and enhancing data quality. It facilitates the identification of error patterns and the implementation of systemic solutions, improving the reliability and accuracy of data assets across the organization.

Scalable Data Management with Advanced Data Lineage Tools

Scalable and efficient data management is pivotal in ensuring the accessibility and utility of data within an organization. Data lineage tools offer comprehensive insights into the data lifecycle, exposing dependencies and data flow mechanisms. Having this visibility is instrumental in optimizing storage, retrieval, and processing strategies, and enables data engineers to enhance system performance and scalability. For example, up-to-date lineage can help data engineering teams discover unused assets or duplicate logic, which may often result in cost and performance impact.

The integration of sophisticated data lineage tools is vital for achieving these management efficiencies. A data management tool should ideally integrate very similarly with existing frameworks, for example git, to provide detailed lineage information that can inform ongoing decisions. The data management tool can also drive strategic data architecture decisions and facilitate the effective governance of data assets.

As data architectures continue to evolve and as it seems, get more fragmented in nature due to specialized use cases, the practice of data management, which lineage is a key part of, becomes even more important. In modern data platforms where data is transformed between operational databases, data lakes, and warehouses of different kinds, ongoing management without lineage is extremely difficult. 

Consequently, maintaining data lineage in a non-automated way makes it impossible to rely on, due to the constant changes across the organization. This has unfortunately resulted in many attempts to implement data lineage to take extended periods of time, and often fail. It is not uncommon for organizations to report that it has taken them 12 months to implement a new data lineage tool, which also becomes out of date if the organization doesn’t have enough headcount to support it.

Data Lineage Benefits for Compliance and Regulatory Adherence

In the current era of stringent data governance regulations, ensuring compliance is a paramount challenge for data engineering teams. Data lineage emerges as a crucial ally in this domain, furnishing auditable records of data processes and lineage crucial for meeting regulatory demands. It aids in the construction of comprehensive audit trails, simplifying compliance with regulations such as GDPR, HIPAA, and more. Furthermore, in some compliance frameworks, for example SOX and in particular BCBS 239, there’s an explicit requirement for maintaining strong data governance in place and keeping accurate data architecture information, both of which necessitate ongoing lineage information that is always up to date.

Beyond mere regulatory adherence, data lineage plays a strategic role in risk management within data operations, as it enables the early identification of compliance gaps and vulnerabilities, allowing for preemptive risk mitigation measures. Taking this proactive approach does not only circumvent regulatory pitfalls but also boosts stakeholder confidence in data practices, which ultimately translates to better utilization of data and more business value.

Empowering Data Development with Data Lineage Insights

The last use case we’ll highlight in this article about lineage is data development. In data development endeavors, agility and precision are key. Data lineage equips engineering teams, which can be data engineers, analytic engineers or really any person that owns some part of the data stack, with better foresight into the ripple effects of planned changes within the data ecosystem. This insight is critical for safeguarding the integrity of data pipelines and analytical outputs against disruptions arising from modifications. It encourages an iterative development process, fostering efficiency innovation while ensuring the stability and quality of data solutions.

Moreover, the role of data lineage in collaborative development cannot be overstated. By providing transparency into data flows and transformations, it creates a more aligned environment among developers, engineers, and analysts across different business units. When different teams work in different projects and use different tools for development and deployment, running a large-scale organization with this in place is extremely taxing. Having an ongoing collective understanding of code dependencies across every team that is involved with data streamlines development with business objectives, and ultimately expedites the delivery of value through new data solutions.

Conclusion

For data teams tasked with managing complex data ecosystems, the importance of data lineage cannot be understated. From improving data quality and management to ensuring compliance and fostering data development, the benefits of data lineage are comprehensive and multifaceted. By adopting the best data lineage tools, data teams can unlock these benefits, leading to more informed decision-making and operational efficiencies. As the demand for accurate, reliable, and compliant data continues to grow, the strategic implementation of data lineage will remain essential for organizations looking to thrive in the data-driven landscape. Embracing data lineage not only solves immediate challenges but also sets the stage for a future where data integrity, agility, and compliance are paramount.

Foundational’s Approach to Data Lineage

At Foundational, we took a unique approach to data lineage by directly analyzing the source code. Foundational will proactively analyze every GitHub repository, across data and engineering, providing coverage from the operational database all the way to the dashboard. Every pull request is analyzed, monitored, and validated so that lineage is always up to date with the latest commit, and data incidents can be prevented before any faulty code is merged.

Our goal is to streamline data development across the entire company, helping teams deploy changes faster and with greater confidence – Chat with us to see a demo.