Metadata Stewardship and Quality in Telecom Sector

Abstract

The spread of the telecom network infrastructure, constellations of satellites and the use of the Internet of Things (IoT) have brought about unprecedented volumes of heterogeneous information which facilitate the contemporary monitoring systems of the environment and the earth. Metadata control and information quality have also become significant in the telecom industry in controlling data reliably, enhancing data services, and providing quality analytics in the changing environment. Telecom environments are producing data environments that are large scale in terms of network, customer and service metadata; and the alternatives to convergent governance practices, common data standards and the effective stewardship mechanisms are lowering the data usability and real-time situational awareness. The difficulty is especially severe in remote sensing-driven intelligent visual analytics system in which the quality of metadata has a direct impact on discoverability, interoperability, and accuracy of decision making. This study determines these gaps by looking at strategic roles of metadata stewardship. The proposed approach in this paper is a telecom-conscious metadata stewardship through built-in automated quality measurement, semantic balancing, and governance, focusing on the policies. The framework describes a multidimensional metadata quality model that spans over completeness, consistency and accuracy, timeliness, lineage and interoperability in heterogeneous telecommunication domains. The use of machine learning-supported anomaly detectors, rule-based validation and metadata lineage screening could increase the level of transparency, auditability and regulatory compliance. Based on these comparisons, the proposed GAN-BERT-based structure presented higher accuracy (82.59%), precision (79.24%), recall (61.53%) and F1-score (79.01%) in comparison to the SSL, EM and the Co-Training models, in terms of the metadata quality prediction and the anomaly detection tasks. Telemetry open datasets have been used and simulated remote sensing metadata feeds with heterogeneous attributes related to customers, network and services were used in the experimental evaluation. The framework also enhanced the schema conformance, metadata transparency and traceability of governance, and scalability of high throughput telecom workloads. The experimental use presented in terms of telecom datasets and simulated remote sensing feeds indicates reduced metadata errors, increased schema conformance, and the maximized scalability on the high-throughput workloads, resulting in metadata stewardship as one of the enablers of intelligent visual analytics and real-time earth monitoring in telecom settings. The framework outlined offers a flexible and adaptable approach suitable for future network generations and can have significant application in terms of AI-driven governance, monitoring, and regulatory adherence for today's telecom networks.