Telecom — Telecom Data Quality Validation and Normalization Pipeline

Overview

The Telecom Data Quality Validation and Normalization Pipeline is designed to ensure the integrity and compliance of telecom data through rigorous validation and normalization processes. The primary purpose of this DAG is to ingest various data sources, including call detail records, customer profiles, and billing information, and apply a series of quality tests to verify that the data meets established standards. The ingestion pipeline begins with the collection of raw data from multiple source

The Telecom Data Quality Validation and Normalization Pipeline is designed to ensure the integrity and compliance of telecom data through rigorous validation and normalization processes. The primary purpose of this DAG is to ingest various data sources, including call detail records, customer profiles, and billing information, and apply a series of quality tests to verify that the data meets established standards. The ingestion pipeline begins with the collection of raw data from multiple sources, which is then processed through several steps. First, the data undergoes validation checks to identify any discrepancies or anomalies. Next, normalization processes are applied to standardize formats and values across the dataset. Non-compliant data is flagged and archived for auditing purposes, ensuring that all data handling adheres to governance and compliance requirements. The results of these processes are then integrated into a centralized data catalog, providing complete traceability of data quality assessments. Monitoring is conducted through key performance indicators (KPIs) such as data accuracy rates, the volume of flagged records, and processing times. This pipeline not only enhances data quality but also supports regulatory compliance, ultimately delivering significant business value by improving decision-making processes and customer satisfaction in the telecom sector.

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

Use cases

• Improved data accuracy enhances operational efficiency
• Reduced compliance risks through rigorous data governance
• Enhanced customer insights from reliable data sources
• Streamlined auditing processes save time and resources
• Increased trust in data-driven decision-making