Telecom — Data Governance Framework for Recommendation Systems

Overview

The primary purpose of this DAG is to establish a robust data governance framework specifically designed for recommendation systems within the telecom industry. It begins by ingesting various data sources, including customer interaction logs, usage statistics, and regulatory compliance documents. The ingestion pipeline is meticulously designed to ensure that only relevant and high-quality data is processed. Following ingestion, the data undergoes a series of processing steps that include auditin

The primary purpose of this DAG is to establish a robust data governance framework specifically designed for recommendation systems within the telecom industry. It begins by ingesting various data sources, including customer interaction logs, usage statistics, and regulatory compliance documents. The ingestion pipeline is meticulously designed to ensure that only relevant and high-quality data is processed. Following ingestion, the data undergoes a series of processing steps that include auditing for compliance with industry regulations, applying quality control measures, and implementing data masking techniques to protect sensitive information. These processes are critical in ensuring that the data used for generating recommendations is both reliable and secure. The outputs of this DAG include documented compliance reports, quality assurance logs, and masked datasets for future use. Monitoring is a key component, with KPIs such as compliance rates and audit turnaround times being tracked to assess the effectiveness of the governance framework. The business value derived from this DAG is significant, as it not only enhances the trustworthiness of recommendation systems but also mitigates risks associated with data breaches and regulatory non-compliance, ultimately leading to improved customer satisfaction and loyalty.

Part of the Recommendations solution for the Telecom industry.

Use cases

• Enhances customer trust through data protection
• Reduces risk of regulatory penalties and fines
• Improves accuracy of recommendation algorithms
• Streamlines data management processes
• Facilitates better decision-making with reliable data