Big data and data quality dimensions: a survey

Abstract

Data is a vital asset in virtually all types of organizations. These days data or information acquired from data analysis is the basis of decision making in various businesses or organizations in general and this offers numerous benefits by building accurate and dependable process. The degradation of its quality has erratic consequences resulting to wrong insights and decisions. Moreover, these are the days of Big Data (BD) which comes with varieties of vast amount of unprecedented data with unknown quality which makes its Data Quality (DQ) evaluation very challenging. DQ is therefore critical for the processes of data operations and management in order to detect associated performance problems. Besides, data of high quality has the ability to attain top services within an organization through enlarged prospects. Nonetheless, recognising different characteristics of DQ from its definition to the different Data Quality Dimensions (DQDs) are crucial for equipping methods and processes for the purpose of improving DQ. This paper focuses on the review of BD and the most commonly used DQDs for BD which are basis for the assessment and evaluation of the quality of BD.