High Tech — Data Quality Validation for Forecasting

Overview

The primary purpose of this DAG is to ensure the integrity and quality of data used for forecasting in the high-tech industry. It begins by ingesting various data sources, including market trend reports, sales forecasts, and customer feedback data. The ingestion pipeline is designed to capture these inputs efficiently, ensuring that data is readily available for processing. Once the data is ingested, the DAG applies a series of quality control tests to identify missing or erroneous data points

The primary purpose of this DAG is to ensure the integrity and quality of data used for forecasting in the high-tech industry. It begins by ingesting various data sources, including market trend reports, sales forecasts, and customer feedback data. The ingestion pipeline is designed to capture these inputs efficiently, ensuring that data is readily available for processing. Once the data is ingested, the DAG applies a series of quality control tests to identify missing or erroneous data points. These tests include checks for data completeness, accuracy, and consistency, ensuring that all data adheres to the established quality criteria. If any data fails these quality checks, it is flagged and quarantined for further review, preventing it from affecting the forecasting models. The results of the quality tests are logged, and alerts are generated in case of non-compliance, allowing for timely intervention and correction. The outputs of this process include a detailed quality report, a list of quarantined data entries, and an updated dataset that meets the quality standards. Monitoring key performance indicators (KPIs) such as the percentage of compliant data, the number of alerts generated, and the time taken for data validation is crucial for assessing the effectiveness of the DAG. By ensuring high-quality data is used for forecasting, businesses can make more informed decisions, ultimately leading to improved market strategies and enhanced competitiveness in the high-tech sector.

Part of the Market & Trading Intelligence solution for the High Tech industry.

Use cases

• Improved forecasting accuracy through high-quality data
• Enhanced decision-making capabilities for market strategies
• Reduced risk of errors in data-driven processes
• Increased operational efficiency in data management
• Strengthened compliance with industry standards and regulations