Defense & Aerospace — Data Normalization for Aerospace Recommendations

Overview

The primary purpose of this DAG is to ensure that all ingested data adheres to defined normalization standards, crucial for maintaining data integrity in the Defense & Aerospace industry. The data sources include ERP transaction logs, sensor data from aerospace systems, and compliance reports. The ingestion pipeline begins with data collection from these sources, followed by a series of processing steps. The first step involves data validation, where quality checks are applied to identify anomal

The primary purpose of this DAG is to ensure that all ingested data adheres to defined normalization standards, crucial for maintaining data integrity in the Defense & Aerospace industry. The data sources include ERP transaction logs, sensor data from aerospace systems, and compliance reports. The ingestion pipeline begins with data collection from these sources, followed by a series of processing steps. The first step involves data validation, where quality checks are applied to identify anomalies and ensure conformity to standards. Next, the data is normalized to a consistent format, facilitating easier analysis and integration. Following normalization, the data is cataloged to ensure traceability, which is essential for compliance and auditing purposes. Key performance indicators (KPIs) monitored throughout this process include the compliance rate of the data and the processing time, both of which are critical for operational efficiency. In case of any failures during processing, an error report is generated for further analysis, allowing for timely resolution of issues. The outputs of this DAG include standardized datasets ready for analysis, compliance reports, and error logs. By implementing this normalization process, organizations in the Defense & Aerospace sector can enhance the quality of their data, leading to improved decision-making and operational efficiencies.

Part of the Recommendations solution for the Defense & Aerospace industry.

Use cases

• Enhanced data quality leading to better decision-making.
• Improved compliance with industry standards and regulations.
• Increased operational efficiency through streamlined processes.
• Reduced risk of data-related errors and anomalies.
• Greater traceability for auditing and reporting purposes.