Banking — Financial Data Normalization and Quality Control Pipeline

Overview

The purpose of this DAG is to process ingested financial data by applying normalization rules and quality checks. It begins by ingesting data from various sources such as ERP transaction logs, customer account records, and market data feeds. The ingestion pipeline ensures that all relevant financial data is captured accurately. The processing steps include format validation, where the system checks for compliance with predefined data formats, and duplicate verification, which identifies and remo

The purpose of this DAG is to process ingested financial data by applying normalization rules and quality checks. It begins by ingesting data from various sources such as ERP transaction logs, customer account records, and market data feeds. The ingestion pipeline ensures that all relevant financial data is captured accurately. The processing steps include format validation, where the system checks for compliance with predefined data formats, and duplicate verification, which identifies and removes any redundant entries. Integrity tests are then applied to ensure that the data adheres to business rules and quality standards. After processing, the data is cataloged for traceability, making it easier for stakeholders to access and utilize the information. Key performance indicators (KPIs) monitored include compliance rates and processing times, which are crucial for evaluating the efficiency of the workflow. In the event of any failures during processing, alerts are generated to prompt manual intervention, ensuring that data quality is maintained. This DAG not only streamlines data management but also enhances decision-making capabilities by providing reliable and accurate financial data, ultimately delivering significant business value in the banking sector.

Part of the Data & Model Catalog solution for the Banking industry.

Use cases

• Improved data accuracy leading to better decision-making
• Enhanced regulatory compliance and risk management
• Increased operational efficiency through automation
• Streamlined data accessibility for stakeholders
• Reduced manual intervention costs and time