Identifying Data Quality/Information Quality Research: Framework and Evolution

Over the past three decades, data quality/ information quality (DQ/ IQ) is emerging into a possible distinct discipline. As the research overlaps with other disciplines or research fields such as IS, Marketing, Computing Science, etc., it is important to identify the core characteristics of DQ/IQ research and to study its development over time. Although scholars have make contribution to the identity of DQ/ IQ research through qualitative and quantity approaches, there is lacking of a more objective approach that comprehensively studies the identity and evolution of DQ/ IQ research. In this study, Latent semantic analysis (LSA) approach was used to identify the core areas and evolution of DQ/IQ research field. Relevant keywords from selected 317 journal papers and conference proceeding papers during 1976 through 2012 were analyzed. We identified five core research areas of DQ/ IQ that have emerged from the research literature in the last 36 years: (1) assessment of DQ/ IQ; (2) computing and technological aspect of DQ/ IQ; (3) DQ/ IQ system application; (4) organizational level impact of DQ/IQ; (5) data process management of DQ/ IQ. By examining the evolution of DQ/ IQ research over the past 36 years, we found that the core areas have remained stable, but the topics within each core area appeared and disappeared over time. We conclude that DQ/IQ research has remained relatively stable by focusing on the DQ/IQ research cycle of data/ information management: technology ¨ application ¨ process management ¨ assessment ¨ impact. Insights and suggestions are discussed and presented finally for future research.