Although the success of big data technologies depends highly on the quality of underlying data, no standard measurement model has been yet established for assessing quantitatively the quality of big data. This research aims at investigating thoroughly the quality of big data and laying rigorous foundations for its theoretically valid measurement. We recently proposed a quality measurement hierarchy for methodically selected 10V’s of big data, based on the existing ISO/IEC standards, and NIST (National Institute of Standards and Technology) definitions and taxonomies. In this paper, pursuant to our latest research, we derive measurement information model for the most widely used 3V’s of big data: Volume, Velocity and Variety. The proposed 3V’s measures, declined into a hierarchy of 3 indicators, 2 derived measures and 4 base measures, are validated theoretically based on the representational theory of measurement. Our future research will enhance the theoretical findings presented in this paper with empirical evidences through evaluation of these measures with open-access data.
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