Insurance — Data Normalization and Quality Control for SOPs

Overview

The primary purpose of this DAG is to standardize and validate data extracted for integration into Standard Operating Procedures (SOPs) and Playbooks within the insurance industry. It begins by ingesting various data sources, including claims data, customer information, and regulatory compliance reports. The ingestion pipeline is designed to ensure that data is collected efficiently and accurately. Once the data is ingested, it undergoes a series of processing steps that include validation check

The primary purpose of this DAG is to standardize and validate data extracted for integration into Standard Operating Procedures (SOPs) and Playbooks within the insurance industry. It begins by ingesting various data sources, including claims data, customer information, and regulatory compliance reports. The ingestion pipeline is designed to ensure that data is collected efficiently and accurately. Once the data is ingested, it undergoes a series of processing steps that include validation checks to identify any discrepancies or errors. Quality control tests are then applied to ensure the integrity of the data, followed by data masking procedures to protect sensitive information. The normalized data is subsequently archived in a data catalog, which not only facilitates traceability but also ensures compliance with industry regulations. Monitoring key performance indicators (KPIs) such as error rates and processing times is critical for maintaining data quality and operational efficiency. The business value of this DAG lies in its ability to enhance the reliability of SOPs, reduce compliance risks, and streamline operational workflows, ultimately leading to improved decision-making and customer satisfaction.

Part of the SOPs & Playbooks solution for the Insurance industry.

Use cases

• Increased reliability of SOPs leading to better operational decisions.
• Reduced risk of compliance violations through rigorous data checks.
• Enhanced customer trust by safeguarding sensitive information.
• Streamlined data processing workflows for improved efficiency.
• Improved visibility into data quality metrics for proactive management.