Media — Data Quality Assurance for Demand Forecasting

Overview

The primary purpose of this DAG is to implement robust quality controls on the data ingested for demand forecasting in the media industry. It ingests various data sources, including audience metrics, content performance data, and market trends. The ingestion pipeline begins with the collection of raw data, which is then subjected to a series of processing and transformation steps to extract relevant features. Each data point is validated against predefined compliance criteria, ensuring that it m

The primary purpose of this DAG is to implement robust quality controls on the data ingested for demand forecasting in the media industry. It ingests various data sources, including audience metrics, content performance data, and market trends. The ingestion pipeline begins with the collection of raw data, which is then subjected to a series of processing and transformation steps to extract relevant features. Each data point is validated against predefined compliance criteria, ensuring that it meets the expected standards. In case of any non-compliance, alerts are generated to notify stakeholders, enabling prompt corrective actions. The results of these quality checks are stored for traceability, ensuring that the data used in forecasting models is both reliable and compliant. The outputs of this DAG include comprehensive quality reports, compliance status updates, and alerts for non-conforming data. Monitoring key performance indicators (KPIs) such as data accuracy, completeness, and timeliness is essential to maintain high data quality standards. The business value of this DAG lies in its ability to enhance the accuracy of demand forecasting, leading to better decision-making and optimized resource allocation in the media sector.

Part of the Market & Trading Intelligence solution for the Media industry.

Use cases

• Improved forecasting accuracy leads to better inventory management
• Enhanced decision-making through reliable data insights
• Reduced risk of financial losses from inaccurate forecasts
• Increased operational efficiency by automating quality checks
• Strengthened compliance with industry standards and regulations