Data lineage: Everything you need to know

Data lineage: Everything you need to know

At InfoCentric, we work with a variety of businesses to make their data work for them.

From how data is acquired, to how it is managed throughout the business, we help ensure that data is effectively used to benefit the business.

In this article, we’re discussing how data lineage fits into the equation and why businesses should take note.

What is data lineage?

Data lineage is the process of tracking data as it moves from data source to consumption. 

Within any business, data will undergo varying levels of transformation and movement from source to final destination. Data lineage refers to the visibility and transparency of this movement and use at different stages.

Ideally, the flow of data within a business is traceable, adaptable and transparent. This helps to maintain compliance from a governance perspective, but also ensures that the data being used to make business decisions is accurate and in many cases, auditable.

How it works

Data lineage relies on Metadata. Metadata is “data about the data” such as the type, format, structure and author. Data lineage organises and tracks this metadata so that it can be used to determine the origins of data, how it has been transformed and how accurate it is.

These tools map, catalogue and discover data to keep track of the evolution of data over time. By understanding the different data elements, users can better leverage data to create a given outcome. 

Increasingly, data governance or meta data management platforms enable data science and advanced analytics teams to track machine learning algorithms via data lineage.  

Data lineage at different stages of the pipeline

Data lineage can look different at different stages of the data pipeline. Here’s how it might show up at different stages.

When data is being moved

When data is being moved between source and destination systems, data tracking enables any errors to be flagged. It tracks the flow of data from one place to the next, enabling data citizens to have full coverage over the pathways of data.

When data is being processed

Data lineage tools can analyse the accuracy of data processing in order to identify any errors or compliance issues. Data citizens are able to track specific operations and understand how “cleanly” data is being processed.

When data is being consumed

Data lineage is especially important to reporting, as it helps end users to validate the accuracy of data. Having a full picture of your data landscape allows users to submit queries and run reports using databases and data warehouses with confidence that the data is correct. 

Why is data lineage important?

By tracking the flow of data over time, businesses can understand where that data came from and how it has evolved during processing.

This makes it much easier for all areas of the business to have a clear view of where data is in the pipeline and how it is being used by each party. Using data tools, users can search data within the organisation and track its transformation journey.


There are several potential challenges and difficulties that organisations may face when implementing data lineage tools. Some of the main ones include:

  • Time Consuming: Keeping your data maps up to date can be time consuming particularly in large, complex organisations with large volumes of data.
  • Incomplete data: Poor or incomplete data can make it very difficult to create data lineage maps.
  • Lack of detail: The level of detail that can be ingested into a data governance platform will often determine how much value can be obtained from a data lineage tool.
  • Limited investment: Lineage needs to be adequately resourced to ensure it can be successfully leveraged by your end users and maintained over time to deliver business value.

Key benefits

  • Compliance: Enables users to establish compliance and trace any incidents back to their source.
  • Accuracy and data quality: By having multiple touch points, users can validate for accuracy and consistency at multiple stages of the life cycle.
  • Continual improvement: Enables users to gain context about historical processes and assess issues at their source.
  • Analysis: data lineage enhances control over the data chain, making analysis at key points easier.

In terms of leading data governance and metadata management platforms, we strongly recommend Alex Solutions, an Australian meta data management platform used by many enterprise customers in the top ASX100.  

Want to learn more about how data can be utilised within your business? InfoCentric can help. Get in touch today to speak to an expert.