Enhancing Data Quality Management: A Comprehensive Metamodel for External and Social Media Data

Data quality is crucial for ensuring the reliability and validity of decision-making processes, particularly in light of the growing reliance on external and social media data. This paper addresses the complexities of managing data quality in these contexts, where challenges like volume, variety, and veracity complicate data accuracy, completeness, and consistency. We propose a comprehensive metamodel for external and social media data, which offers a structured framework to understand the relationships among different data entities and guides the assessment and improvement of data quality. The metamodel categorizes data types, identifies key quality dimensions, and illustrates interactions among data entities, with a focus on external and social media data. This enhances understanding of data quality dynamics and supports informed decision-making by defining relationships and boundaries between these data categories. We validate the metamodel's effectiveness on a case study relative to Product Placement Analysis for smartphone Selling, by addressing data quality issues, such as inconsistent product mentions and inaccurate engagement metrics, demonstrating its practical applicability. This research contributes a novel approach for improving data quality in external and social media data, supporting more reliable analytics and decision-making.