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Introduction and background
Business intelligence and analytics (BI&A) and the related field of Big Data analytics have gained great significance over the past two decades (Chen et al., 2012). Top performing organisations are three times more likely to use sophisticated methods in their use of data analytics than lower performers, making analytics a major differentiating factor (Davenport and Harris, 2007; Hopkins et al., 2010). BI&A is often referred to as the techniques, technologies, systems, practices, methodologies and applications that analyse critical business data to help an enterprise better understand its business and make timely business decision (Chen et al., 2012). According to a report by Gartner (2015), with the mobile and cloud computing technologies, the dynamic BI&A market is undergoing a fundamental shift from highly centralised, IT-led consolidation and standardisation projects to a more mobile, user-centric and interactive styles of analysis and reporting without requiring the users to have static location and IT/data science skills. These business-centric practices and methodologies can be applied to various high-impact applications such as in: e-commerce, market intelligence, e-government, healthcare, security, determining human/asset location and manufacturing etc. (Turban et al., 2008; Brown et al., 2011).
A closely related term to BI&A is data-driven decision making (DDD), which refers to the practice of making decisions on the analysis of data rather than purely on intuition, and can translate into increased productivity and market value (Provost and Fawcett, 2013). However, the act of gathering and storing large amounts of data for eventual analysis and decision making is not new. Over the past 20 years, the amount, type and flow of data in many organisations have exponentially increased and is now classified as Big Data (Gantz and Reinsel, 2011; Chen et al., 2014). Moreover, Big Data is accumulating from multiple sources such as systems, sensors and mobile devices, etc., at an alarming velocity, volume and variety to an extent that 90 per cent of the data in the world today has been created in the last two years alone (Zikopoulos et al., 2013; IBM, 2017). This influx of data offers insights for making right decisions at the right time based on efficient aggregation and analysis of internal and external data in organisations.
Given the competitive and...