Data lineage

What is data lineage?

Data lineage is a data management practice that explains the entire lifecycle of the data from how it's created to how it's transformed until it reaches its destination. Organizations use data lineage to get visibility into data origins, movements, transformations, and how data flows from one system to another. The concept of data lineage is crucial in data fabric architecture, where integration of multiple sources happens from cloud or hybrid environments and understanding their movement and lifecycle is necessary.

Components of data lineage

Data sources: The starting point of the data, probably data lakes, warehouses, or applications like ERPs, SaaS systems, etc.

Metadata: Metadata is the data about your data, its origin, structure, owner, created and modified date, and more. It’s essentially used in data lineage to see who created your data, what is it about, and usage specifications.

Data tags: Data tagging denotes the labeling of data to track down its every movement.

Data transformations: Every modification that happens to your data as it moves from origin to destination, like cleansing, deduplication, standardization, formatting, etc. 

Governance principles: The control and governance policies on how your data must be maintained.

BI Tools: There are visualization tools used to turn data lineage flow charts into visuals for easy monitoring. 

Types of data lineage

Based on use cases and details it covers, data lineage is of 7 types.

Business lineage: Offers data lineage records from a business point of view, explains business processes that happen across the data’s journey.

Technical lineage: It provides more detailed lineage information from a tech perspective, giving more emphasis on ETL and transformation processes, database operations, schema changes, and more.

End-to-end lineage: This is the combination of both business and tech lineage, offering comprehensive lineage reports.

Horizontal lineage: This type of lineage tracks the data flow across different systems within the same architecture. For instance, it could track how effective a migration is, that happens within the same environment.

Vertical lineage: tracks vertically the flow of data from one architecture to another, like from source to data warehouse to analytics, which can explain how data transformations happen.

Granular lineage: Offer a more detailed view of data flow that covers every specific and individual data element in the datasets. 

Automated lineage: The lineage process is automatic here, set up with the help of tools to capture the data flow as it moves through systems. Suitable for smaller IT teams who can’t manually intervene.

Benefits of data lineage

Easy auditing and compliance: High transparency about data, systems, and business processes, making it easy to apply regulations and control policies. It ensures that your organization has sets of records about every datapoint and how it’s used for business purposes.

Can easily trace back issues for instant troubleshooting: Identifying and tracking every data point, can find out issues and fix right away before it affects end user consumption.

Impact analysis: Easy to identify the impact of applied changes after data transformation to prevent mishaps and negative consequences. 

High data quality: Address issues with data quality faster and ensure optimal standards for quality and governance. 

More confidence about data: Both business users and data teams know where the data comes from and what changes happen on the way. This can help them rely on data and make decisions with more confidence.

How to implement data lineage in organizations?

Data lineage can be a conceptual approach. It could be practically implemented with the help of metadata management tools. These tools crawl and track data movements and capture them like visual flowcharts. IT leaders, analysts, and governance specialists could use these flowcharts to understand and analyze data flow. Here is how you can implement data lineage in five steps.

• Identify data sources

• Map data flows by connecting systems and keep in considerations of any transformations, dependencies involved.

• Employ metadata management tools like Microsoft Purview or Informatica to capture the lineage.

• Collect and maintain data lineage charts consistently, accounting for every change and evolution data systems go through.

• Make sure the data lineage process is aligned with your organizational governance structure.

Data lineage is an inevitable part of your data architecture. Implement it today for a more simplified auditing and reporting and enhanced data quality.