The rapid increase in the number of data sources in recent years has made it essential for organisations to track where their stored datasets come from. Understanding where data comes from is crucial to avoid creating new data use cases based on untrusted or poor-quality information, which can ultimately undermine value creation.

In the data business, this process is called 'data lineage'. According to IBM's definition, it is the process of tracking the flow of data over time to provide a clear understanding of its origin, evolution and ultimate destination within the data pipeline.

In this article, we will take a detailed look at what data lineage is, and explain why businesses need to ensure it in order to maximise the value of their data.

Why data lineage matters: Ensuring quality, transparency and trust.

Firstly, each organisation must include key actions to consistently improve their data quality in their data management strategy. To achieve this, companies need to track every piece of data back to its source. This also involves checking how the dataset is used. Which teams are using it? What are the use cases? How is it stored?

More specifically, data trackability involves documenting and following the entire data pipeline. This provides visibility of, and makes it easy to identify, the dependencies between datasets. It also enables errors to be quickly detected and ensures that the analysis performed by business teams is based on trustworthy data.

Over the years, data lineage and traceability have become key elements in the development of data governance frameworks for organisations, as they enforce transparency, quality and trust when creating data use cases. Data lineage is also essential for ensuring that data pipelines are functioning correctly and supporting business operations.

A multi-level view of data lineage: systems, tables, columns and transformations.

Data lineage operates at various levels. It provides a comprehensive overview of the stages in the data journey, tailored to your needs. Firstly, system lineage offers a broad view of architectural components such as databases, data lakes, ETL tools and apps. It shows how these systems communicate with each other.

From a table perspective, data lineage tables go one level deeper, showing the links between different datasets. This is an ideal way to see which tables are or aren't used by a dashboard, for example.

Even more precise is data lineage for columns, which allows you to understand how each field is built or transformed by identifying the dependencies between tables.

Finally, transformation lineage reveals the logic applied to the data, including business rules, filters and calculations performed during processing. Together, these four levels provide comprehensive insight into a data's journey within your organisation.

The mechanisms and tools behind effective data lineage.

Data lineage relies on multiple complementary mechanisms. Firstly, some platforms automatically detect the transformations applied to data through pipeline analysis. For others, technical teams are responsible for manually documenting these transformations.

At the heart of the process is metadata, which describes the sources, transformations, and destinations of the data. This information is generally centralised within a data catalogue, which allows it to be classified, enriched and exploited.

Finally, the lineage can be presented in visual form, allowing users to quickly understand the dependencies and easily identify them.

Different tools and platforms are available under different forms: open-source solutions offers flexibility, community support and are oftenly integrable directly in existing pipelines. On the other hand, cloud platforms propose automatic lineage based on native metadata from cloud services, which ease the deployment and the maintenance's process.

Also, data catalog tools like Collibra or DataGalaxy combine features that support governance, lineage, and documentation, providing a central workspace where teams can organize metadata, track data dependencies, and maintain a reliable source of truth across the entire data ecosystem.

The Foundations and Challenges of Implementing Reliable Data Lineage

To ensure efficient data lineage within your organisation, it is crucial to standardise precise definitions from the outset of the implementation process. For example, if you label a field in your table as 'name'. Some business teams may be confused, as they would not know whether this refers to the first or last name.

To avoid any misunderstandings, teams are encouraged to collaborate together to ensure common definitions in favor of robust data lineage and consistent documentation.

In addition, data observability is extremely important by monitoring the data during the entire lifecycle. Thanks to automated indicators and alerts, it enables rapid detection of anomalies, processing errors or disruptions in pipelines before any impacts on analysis or business decisions.

Despite its many advantages, implementing data lineage presents several challenges and limitations. For example, cost and complexity can be significant, as capturing, storing and maintaining detailed lineage across multiple systems often requires substantial resources. Furthermore, the lack of standardisation in how lineage is represented and tracked can hinder integration between tools and teams.

In conclusion, data lineage is essential for implementing a data governance framework in any organisation. Given the increasing prevalence of AI tools and automated analysis, the process of data lineage has never been more important than it is in 2025.