Insurance — Data Quality Assessment for Insurance Decision-Making

Overview

The purpose of the assurance_kmds_data_quality_assessment DAG is to ensure high-quality data for informed decision-making within the insurance industry. This DAG ingests data from various sources, including policyholder databases, claims records, and external market data. The ingestion pipeline efficiently consolidates and prepares this data for processing. The primary processing steps involve applying a series of quality control tests to identify anomalies, such as missing values, duplicates, a

The purpose of the assurance_kmds_data_quality_assessment DAG is to ensure high-quality data for informed decision-making within the insurance industry. This DAG ingests data from various sources, including policyholder databases, claims records, and external market data. The ingestion pipeline efficiently consolidates and prepares this data for processing. The primary processing steps involve applying a series of quality control tests to identify anomalies, such as missing values, duplicates, and outliers. These tests are designed to ensure that the data meets predefined quality standards essential for accurate forecasting and risk assessment. Following the quality checks, the DAG generates detailed reports that outline the quality metrics and suggest corrective actions to address any identified issues. The processed data, along with the quality assessment reports, are stored in a centralized data warehouse, making them readily accessible for further analysis. Monitoring is facilitated through a dedicated interface displaying key performance indicators (KPIs) such as the percentage of compliant data and the frequency of anomalies detected. This ongoing assessment of data quality not only enhances operational efficiency but also supports strategic decision-making, ultimately driving better business outcomes in the insurance industry.

Part of the Supply/Demand Forecast solution for the Insurance industry.

Use cases

• Improves accuracy of insurance risk assessments
• Enhances operational efficiency through streamlined data processes
• Supports compliance with regulatory data standards
• Enables proactive identification of data issues
• Drives better customer insights and service delivery