Data lineage: Studying the life cycle of data

Like animals and plants, data too has a life cycle. It goes through various changes from its inception to its destination. When dealing data maturity – that helps maintain high standard data quality – it is necessary for organizations to understand the life cycle of data. Data lineage shows the complete data flow, from the beginning to the end.

What is data lineage?

Data lineage is the process of understanding, recording, and visualizing the data from its origin to its consumption. It records all the changes that data goes through its journey. This helps in better understanding what happened to the data, allowing businesses to make more informed decisions.

Why is it important?

Data lineage is important when decisions need to be made on accurate and reliable information. As discussed in a blog earlier, many businesses still use data silos, which is not accessible by other departments. In addition to that, companies can have also various sources of data. These factors can lead to inconsistencies in analysis and obstruct the decision-making process.

When the life cycle of data is recorded through data lineage, businesses know where the data is coming from as well as where it transformed. This helps in finding anomalies and also prevents the wastage of time to find an accurate and reliable source.

Some other advantages of having a data lineage process are:

• Data governance: It is a concept of data management, which is guided by principles and practices ensuring that high-quality data is available at an organization. The tracking of data helps in compliance auditing. It also ensures that the data is in line with the policies and regulatory standards of the company.
• Data migration: Data Lineage allows easy and swift information regarding location and lifecycle of data sources. This allows hassle-free data migration to a new storage or system.
• Eliminating errors: A company that has developed a data culture heavily relies on data to make decisions. However, there is a possibility of error as and when data transforms. Data lineage can help track data so that the mistakes can be rectified.

Data lineage examples and techniques

There are several techniques an organization can implement to perform data lineage. Here are a few:

• Data tagging: The technique can be used when transformation engines tag data. This allows easy recognition of changes in data. However, organizations need too have a fixed transformation engine that employees are familiar with.
• Parsing: This method depends on reading the logic behind data processing. It then reverse engineers the data transformation logic to execute data tracing. However, parsing is complex as users need to know several programming languages.
• Self-contained data environment: This provides storage, processing, and master data management. In many cases, these environments contain a data lake that stores all data in all stages of its lifecycle.