High Tech — Data Cataloging and Lineage Management for Pricing Optimization

Overview

The primary purpose of this DAG is to create a comprehensive data catalog that integrates metadata and establishes lineage relationships among various data sources. In the high-tech industry, effective data governance is crucial for pricing optimization, enabling organizations to make informed decisions based on accurate and accessible data. The architecture of this DAG consists of several key components that work together to ensure seamless data ingestion, processing, and output generation. The

The primary purpose of this DAG is to create a comprehensive data catalog that integrates metadata and establishes lineage relationships among various data sources. In the high-tech industry, effective data governance is crucial for pricing optimization, enabling organizations to make informed decisions based on accurate and accessible data. The architecture of this DAG consists of several key components that work together to ensure seamless data ingestion, processing, and output generation. The data sources include ERP transaction logs, customer feedback databases, and market trend analytics. The ingestion pipeline begins with the extraction of metadata from these sources, which is then transformed to create a unified catalog. Processing steps include validating the metadata for accuracy, establishing lineage relationships to trace data origins, and applying role-based access controls (RBAC) to safeguard sensitive information. Quality controls are integrated to monitor the catalog update frequency and the number of cataloged elements, which are critical KPIs for tracking the effectiveness of the data governance strategy. Outputs include a detailed data catalog, lineage reports, and access control logs. Monitoring mechanisms are in place to alert administrators in case of failures, ensuring prompt resolution of issues. This DAG not only enhances data governance but also provides significant business value by improving data accessibility, reducing compliance risks, and enabling more strategic pricing decisions.

Part of the Pricing Optimization solution for the High Tech industry.

Use cases

• Improves data accessibility for pricing optimization teams
• Reduces compliance risks associated with data governance
• Enhances decision-making through accurate data lineage tracking
• Streamlines data management processes for operational efficiency
• Facilitates better alignment of pricing strategies with market trends