Abstract:
Over the last 20 years, and particularly with the advent of Big Data and analytics, the research area around Data and Information Quality (DIQ) is still a fast growing research area. There are many views and streams in DIQ research, generally aiming at improving the effectiveness of decision making in organizations. Although there are a lot of researches aimed at clarifying the role of BIG data quality for organizations, there is no comprehensive literature review that shows the main differences between traditional data quality researches and Big Data quality researches. This paper analyzed the papers published in Big data quality and find out that there is almost no new mainstream about Big Data quality. It is shown in this paper that the main concepts of data quality does not changes in Big Data context and that only some new issues have been added to this area.
Machine summary:
Big Data Quality: From Content to Context Ahmad Khalilijafarabad PhD, Department of Information Technology Management, Faculty of Management, University of Tehran, Tehran, Iran.
It is shown in this paper that the main concepts of data quality do not changes in Big Data context and that only some new issues have been added to this area.
72762 © University of Tehran, Faculty of Management / Introduction Big Data has become a very attractive area of research and development for both academia and industries in recent years (Chen, Mao, & Liu, 2014).
The Big Data concept was firstly defined in 2005 Tim O'Reilly and since then a lot of researchers have studied its applications, challenges, tools, technologies and quality (Kataria & Mittal, 2014).
Number of Published papers about Big Data As it has mentioned, data quality is critical issue in information management and Big Data.
With today’s rapid technological changes such as Big Data (Cai & Zhu, 2015; Saha & Srivastava, 2014), crowdsourcing (Lukyanenko & Parsons, 2015), social information system (Tilly, Posegga, Fischbach, & Schoder, 2015) and semantics web (Fürber, 2015), it becomes critical to identify emerging research directions.
Subsequently, management science researchers in the 1980s focused on eliminating data quality problems in data production processes and related systems.
They have suggested a variety of dimensions such as timeliness, latency, scalability, accuracy, consistency, usabiliy (Desai, 2018; Firmani, Mecella, Scannapieco, & Batini, 2016; Gao, Xie, & Tao, 2016; Onyeabor & Ta’a, 2018).
The papers with keywords "data quality," "information quality" and Big Data in their topic are selected.