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Analyzing data quality issues in research information systems via data profiling

Author

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  • Azeroual, Otmane
  • Saake, Gunter
  • Schallehn, Eike

Abstract

The success or failure of a RIS in a scientific institution is largely related to the quality of the data available as a basis for the RIS applications. The most beautiful Business Intelligence (BI) tools (reporting, etc.) are worthless when displaying incorrect, incomplete, or inconsistent data. An integral part of every RIS is thus the integration of data from the operative systems. Before starting the integration process (ETL) of a source system, a rich analysis of source data is required. With the support of a data quality check, causes of quality problems can usually be detected. Corresponding analyzes are performed with data profiling to provide a good picture of the state of the data. In this paper, methods of data profiling are presented in order to gain an overview of the quality of the data in the source systems before their integration into the RIS. With the help of data profiling, the scientific institutions can not only evaluate their research information and provide information about their quality, but also examine the dependencies and redundancies between data fields and better correct them within their RIS.

Suggested Citation

  • Azeroual, Otmane & Saake, Gunter & Schallehn, Eike, 2018. "Analyzing data quality issues in research information systems via data profiling," International Journal of Information Management, Elsevier, vol. 41(C), pages 50-56.
    • Handle: RePEc:eee:ininma:v:41:y:2018:i:c:p:50-56
    DOI: 10.1016/j.ijinfomgt.2018.02.007
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    Citations

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    Cited by:

    1. Otmane Azeroual, 2020. "Data Wrangling in Database Systems: Purging of Dirty Data," Data, MDPI, vol. 5(2), pages 1-9, June.
    2. Otmane Azeroual & Joachim Schöpfel & Dragan Ivanovic, 2020. "Influence of Information Quality via Implemented German RCD Standard in Research Information Systems," Data, MDPI, vol. 5(2), pages 1-10, March.
    3. Han Meng & Xiaoyu Qi & Gang Mei, 2024. "A Deep Learning Approach for Stochastic Structural Plane Generation Based on Denoising Diffusion Probabilistic Models," Mathematics, MDPI, vol. 12(13), pages 1-22, June.
    4. Otmane Azeroual & Gunter Saake & Mohammad Abuosba & Joachim Schöpfel, 2020. "Data Quality as a Critical Success Factor for User Acceptance of Research Information Systems," Data, MDPI, vol. 5(2), pages 1-13, April.

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