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.
Highlights
Driven by globalisation and increasing market competitions, various industries have turned to big data analytics (BDA) for its ability to transform enormous raw data into decision-making tools [1]
H1 to H5 were the influence of big data traits (BDT) on data quality dimensions (DQD), whereas, only one hypothesis of DQD, H10, was identified as significant for evaluating the influence of ease of operation towards BDA application
The results have shown that accuracy, believability, completeness, and timeliness had no significant effect on BDA application
Summary
Driven by globalisation and increasing market competitions, various industries have turned to big data analytics (BDA) for its ability to transform enormous raw data into decision-making tools [1]. BDA consists of a set of advanced analytical techniques adapted from related fields, such as artificial intelligence, statistics, and mathematics, which are used to identify trends, detect patterns, and unveil hidden knowledge from a huge amount of data [2]. This technology has been applied in different fields, including finance [3], insurance [4], and cyber security [5], to name a few. These types of data are collected or received from diverse platforms, such as network sensors, social media, and the Internet of Things
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