Building a Framework for Continuous Data Quality Monitoring in Media Platforms

Authors

Mahesh Mokale

Abstract

The digital media landscape has witnessed unprecedented growth over the last decade, with platforms distributing vast volumes of content across multiple channels—OTT, web, mobile, and social media. This explosion in content generation and consumption introduces significant complexities in data management, particularly in maintaining high standards of data quality. Poor data quality—characterized by incomplete metadata, duplication, schema inconsistencies, and misclassification—can lead to significant downstream issues including impaired recommendation engines, broken content discovery, reduced audience engagement, flawed analytics, and ultimately, loss of revenue. This paper presents a comprehensive framework for continuous data quality monitoring specifically designed for media platforms. Unlike conventional quality assurance models that rely on ad hoc or batch validation, our proposed approach emphasizes automation, scalability, and real-time responsiveness. The framework incorporates layered validation techniques such as schema enforcement, field-level checks, metadata enrichment, rule-based anomaly detection, and feedback integration from both internal editorial teams and end-users. It is designed to operate within modern media tech stacks that include distributed ingestion pipelines, microservices architectures, and cloud-native infrastructure. In developing this framework, we evaluated multiple tools and technologies available up to 2023—including Great Expectations, Deequ, Apache Griffin, and OpenRefine—for their applicability in high-throughput media environments. We also highlight the case of a leading OTT platform that successfully implemented this architecture, leading to significant improvements in metadata completeness, error detection, and operational efficiency. This work provides a foundational blueprint for media organizations to evolve from reactive data management to a proactive, always-on monitoring model. In doing so, it enables better user experiences, smarter content curation, improved compliance, and enhanced monetization strategies.

Keywords

Building a Framework for Continuous Data Quality Monitoring in Media Platforms. Mahesh Mokale. 2024. IJIRCT, Volume 10, Issue 1. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2504020