DRASTIC: Big Data Quality in Big Data Integration

Abstract

In today’s digital era, organisations are increasingly relying on data-driven decision-making to enhance operational efficiency and strategic planning. Data from multiple sources should be integrated to support this movement. However, this process is complex due to the emergence of big data. Consequently, it significantly increases the challenges of managing and integrating data, which can degrade data quality and lead to poor decision outcomes. In fact, existing data quality characteristics are no longer adequate in the big data era. Therefore, this paper conducted a comprehensive literature review of peer-reviewed articles retrieved from electronic databases published between 2010 and 2025 to examine existing data quality characteristics and identify gaps related to the 5V's big data characteristics. Moreover, this paper compares and evaluates existing data quality characteristics and their sufficiency for assessing the quality of data in big data integration. Based on these evaluations, this paper proposes DRASTIC, a set of 14 data quality characteristics, with dependency and scalability introduced as new characteristics in the context of big data integration because these characteristics are underexplored in existing literature. The findings contribute to the literature by extending current data quality characteristics and addressing the challenges posed by big data's unique characteristics in data integration.