Public Sector — Regulatory Data Feature Engineering Pipeline

Overview

The purpose of this DAG is to create robust feature pipelines from normalized regulatory data to support advanced pricing optimization models in the public sector. The architecture involves the ingestion of various data sources, followed by a series of processing steps that include feature selection, model training, and evaluation. The data sources include ERP transaction logs, regulatory compliance reports, and historical pricing data. The ingestion pipeline normalizes these inputs to ensure co

The purpose of this DAG is to create robust feature pipelines from normalized regulatory data to support advanced pricing optimization models in the public sector. The architecture involves the ingestion of various data sources, followed by a series of processing steps that include feature selection, model training, and evaluation. The data sources include ERP transaction logs, regulatory compliance reports, and historical pricing data. The ingestion pipeline normalizes these inputs to ensure consistency and accuracy. Processing steps begin with feature selection, where relevant attributes are identified and extracted. Next, models are trained using these features, followed by rigorous evaluation to assess their performance. Quality controls are embedded throughout the process to ensure the integrity and reliability of the models. Key performance indicators (KPIs) monitored include model performance scores and processing times, which provide insights into the efficiency and effectiveness of the pipeline. The outputs of this DAG include optimized feature sets, model performance reports, and actionable insights for pricing strategies. By leveraging this pipeline, public sector organizations can enhance their pricing strategies, ensuring compliance while maximizing revenue potential.

Part of the Pricing Optimization solution for the Public Sector industry.

Use cases

• Improves pricing accuracy through data-driven insights.
• Enhances compliance with regulatory standards.
• Reduces time spent on manual data processing.
• Facilitates informed decision-making for pricing strategies.
• Increases operational efficiency in data handling.