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Cited by 43Big Data and Business IntelligenceBig Data Technologies and ApplicationsData Quality and Management

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

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Wook, Muslihah, Hasbullah, Nor Asiakin, Zainudin, Norulzahrah Mohd, Jabar, Zam Zarina Abdul, Ramli, Suzaimah, Razali, Noor Afiza Mat, Yusop, Nurhafizah Moziyana Mohd·2021·Journal of Big Data
DOI 10.1186/s40537-021-00439-5
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Abstract

The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

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  • The '4 V's' of big data are big data traits that can affect data quality but are not considered data quality dimensions.

    “We note that the ‘4 V’s’ (volume, velocity, veracity and variety) of big data195 implicitly suggest a framework for big data quality. However, the ‘4 V’s’ are in fact big data traits which can have an effect on data quality but are not considered data quality dimensions196.”

    From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [196]

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Cited by

Sorted by citations

Authors
Wook, M., Hasbullah, N. A., Zainudin, N. M., Jabar, Z. Z. A., Ramli, S., Razali, N. A. M., & Yusop, N. M. M.
Affiliations
National Defence University of Malaysia (MY)
Corresponding author
Wook, Muslihah
ORCID
Venue
Journal of Big Data
Publisher
Springer Science and Business Media LLC
Published
2021-03-23
Volume
8
Issue
1
ISSN
2196-1115 (ISSN-L)
DOI
10.1186/s40537-021-00439-5
OpenAlex
W3157047140
Cited by
43
References
4
Citations by year
2026: 3 · 2025: 5 · 2024: 10 · 2023: 11 · 2022: 10 · 2021: 4
FWCI
8.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal Of Big Data (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Greater South Information System
Funders
National Defence University of Malaysia (UPNM/2018/GPJP/2/TK/5)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
Keywords
data quality dimensions, big data analytics adoption, accuracy, credibility, completeness, timeliness, ease of operation

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