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Data Analytics Tools: A User Perspective

Author

Listed:
  • Paul Town

    (School of Digital Technologies, Manukau Institute of Technology, Manukau, Auckland 2104, New Zealand)

  • Fadi Thabtah

    (School of Digital Technologies, Manukau Institute of Technology, Manukau, Auckland 2104, New Zealand)

Abstract

Business Intelligence Tools (BI Tools) can be an intelligent way for individuals to undertake data analysis and reporting for guiding decision-making processes. There are many different BI Tools available in the market today, as well as information to assist organisations in evaluating their effectiveness. This paper focusses on two commercially available BI Tools: Tableau and Microsoft Power BI. It aims to determine which BI Tool is better for data analysis and reporting from an end user’s point of view. This paper undertakes an evaluation of both tools and compares which is more suitable for students using interface (navigation), cost, presence in the market, and available training and help as the evaluative criteria. Results produced in this paper found that overall, Tableau was more highly ranked than Power BI based on the evaluative criteria for end users for data analysis and reporting at least among the samples of the study. Tableau ranked higher than Power BI with its presence in the market, and available training and help. Power BI was rated more highly on its interface and both BI Tools were ranked the same in terms of cost to end users. This research is exploratory and may assist in formulating future research on BI Tools for specific user groups.

Suggested Citation

  • Paul Town & Fadi Thabtah, 2019. "Data Analytics Tools: A User Perspective," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-16, March.
    • Handle: RePEc:wsi:jikmxx:v:18:y:2019:i:01:n:s0219649219500023
    DOI: 10.1142/S0219649219500023
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    Citations

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    Cited by:

    1. Anthony Gramaje & Fadi Thabtah & Neda Abdelhamid & Sayan Kumar Ray, 2021. "Patient Discharge Classification Using Machine Learning Techniques," Annals of Data Science, Springer, vol. 8(4), pages 755-767, December.
    2. Mohammed Rajab & Dennis Wang, 2020. "Practical Challenges and Recommendations of Filter Methods for Feature Selection," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-15, March.

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