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Demystifying Data Lineage: Your Guide to Data Tracking in Indian Banks

Updated: Jun 24




In the dynamic world of Indian banking, data is the new gold. But unlocking its true value hinges on understanding its origin, transformations, and journey throughout your systems. This is where data lineage comes into play.


What is Data Lineage?

Data lineage is like a detailed map for your data. It tracks the complete lifecycle of a data element, tracing its path from its source (e.g., customer transactions) to its final destination (e.g., risk management reports). It encompasses:

  • Data Origin: Where does the data come from? Is it captured internally from core banking systems or sourced externally from social media platforms?

  • Transformations: What happens to the data along the way? Is it aggregated, filtered, or enriched with additional information?

  • Destination: Where does the data ultimately reside? Is it stored in a data warehouse, data lake, or another target system?


Why is Data Lineage Important for Indian Banks?

For Indian banks, data lineage plays a crucial role in several key areas:

  • Regulatory Compliance: The RBI emphasizes data governance and responsible data handling. Data lineage helps banks demonstrate compliance with directives like "Master Directions - Information Technology (IT)" by tracing how customer data is used throughout various processes.

  • Improved Data Quality: By understanding the transformations applied to data, banks can identify potential errors or inconsistencies at their source. This ensures that reports and insights are based on accurate and reliable information.

  • Enhanced Data Governance: Data lineage enables banks to implement robust data governance practices. They can track data access, identify data owners, and enforce data security measures throughout the data lifecycle.

  • Efficient Data Management: Knowing the flow of data streamlines management processes. Banks can identify bottlenecks, optimize data pipelines, and avoid duplication of efforts.


Achieving Data Lineage with Datahub.io

Datahub.io is an open-source data catalog platform that can be a valuable tool for Indian banks to achieve data lineage. Here's how Datahub can help:

  • Centralized Metadata Repository: Datahub acts as a central repository for storing metadata about your data assets. This includes information on data origin, transformations, and user-defined tags for better organization.

  • Lineage Visualization: Datahub offers visual representations of data flows, allowing users to easily see how data moves through various systems. This provides a clear understanding of data dependencies and transformations.

  • Collaboration and Governance: Datahub facilitates collaboration between data analysts, data engineers, and business users. It allows for defining data ownership, access controls, and data quality rules, promoting good data governance practices.

  • Integration with Existing Tools: Datahub integrates with various data management tools and frameworks commonly used in Indian banks. This allows for seamless data lineage tracking across the entire data ecosystem.


Implementing Data Lineage with Datahub.io: A Roadmap for Indian Banks

Here's a roadmap for Indian banks to kickstart their data lineage journey with Datahub.io:

  1. Define Data Lineage Scope: Identify the data sets that are critical for regulatory compliance or business decision-making. Prioritize achieving data lineage for these high-impact data sets first.

  2. Catalog Your Data Assets: Start by cataloging all relevant data sources and target systems within Datahub. This involves documenting data types, formats, and ownership details.

  3. Map Data Flows: Identify and map the transformations applied to data as it moves from its source to its final destination. This can be achieved through manual documentation or through automated tools that integrate with Datahub.

  4. Implement Governance Policies: Define data ownership roles within Datahub. Establish policies for data access, quality checks, and data retention based on RBI regulations and internal best practices.

  5. Promote Adoption and Training: Train data analysts, engineers, and business users on how to utilize Datahub effectively. Foster a data-driven culture where data lineage is valued and used for continuous improvement.


Conclusion:

Data lineage is a critical aspect of data governance in modern Indian banking. By adopting Datahub.io and establishing a robust data lineage framework, banks can ensure regulatory compliance, improve data quality, and unlock the true potential of their data assets for informed decision-making and enhanced customer experiences. 

Drona Pay has created a modular open source data stack that has helped banks modernise their data architecture while providing end to end lineage of the data from systems including CBS, LMS, Treasury, Internet Banking, Mobile Banking, Payments etc. The Drona Pay Modern Data Stack provides scalable data ingestion, processing, storage, governance and visualization built on top of leading open source elements including Iceberg, Debezium, Kafka, Airflow, Spark and Superset. By embracing the Drona Pay stack, Banks can have end to end lineage on the data reported internally and externally. 

Remember, data lineage is an ongoing process, requiring continuous monitoring, updating, and refinement as data sources and workflows evolve. By investing in data lineage, Indian banks can ensure responsible data handling practices and position themselves for success in the data-driven future of finance.

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