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Overview of Data Quality: Examining the Dimensions, Antecedents, and Impacts of Data Quality

Author

Listed:
  • Jingran Wang

    (Sogang University)

  • Yi Liu

    (University of the Incarnate Word)

  • Peigong Li

    (Shanghai Lixin University of Accounting and Finance)

  • Zhenxing Lin

    (Shanghai Lixin University of Accounting and Finance)

  • Stavros Sindakis

    (Hellenic Open University)

  • Sakshi Aggarwal

    (Entrepreneurship and Education for Growth)

Abstract

Competition in the business world is fierce, and poor decisions can bring disaster to firms, especially in the big data era. Decision quality is determined by data quality, which refers to the degree of data usability. Data is the most valuable resource in the twenty-first century. The open data (OD) movement offers publicly accessible data for the growth of a knowledge-based society. As a result, the idea of OD is a valuable information technology (IT) instrument for promoting personal, societal, and economic growth. Users must control the level of OD in their practices in order to advance these processes globally. Without considering data conformity with norms, standards, and other criteria, what use is it to use data in science or practice only for the sake of using it? This article provides an overview of the dimensions, subdimensions, and metrics utilized in research publications on OD evaluation. To better understand data quality, we review the literature on data quality studies in information systems. We identify the data quality dimensions, antecedents, and their impacts. In this study, the notion of “Data Analytics Competency” is developed and validated as a five-dimensional formative measure (i.e., data quality, the bigness of data, analytical skills, domain knowledge, and tool sophistication) and its effect on corporate decision-making performance is experimentally examined (i.e., decision quality and decision efficiency). By doing so, we provide several research suggestions, which information system (IS) researchers can leverage when investigating future research in data quality.

Suggested Citation

  • Jingran Wang & Yi Liu & Peigong Li & Zhenxing Lin & Stavros Sindakis & Sakshi Aggarwal, 2024. "Overview of Data Quality: Examining the Dimensions, Antecedents, and Impacts of Data Quality," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 1159-1178, March.
    • Handle: RePEc:spr:jknowl:v:15:y:2024:i:1:d:10.1007_s13132-022-01096-6
    DOI: 10.1007/s13132-022-01096-6
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