Abstract

In this digitised age, vast amounts of information are readily available to be autonomously rationalised/analysed to gain valuable knowledge and insights to support decision makers. This information referred to as ‘Big Data’ is quantified from large datasets, hence the name. It is also derived from the information being high in variety and velocity. Because of this data’s characteristics, they are beyond the ability of commonly used software tools and storage systems to capture, store, manage, as well as process the data within a tolerable elapsed time. Due to the progression of this Big Data, innovative solutions are needed to be implemented to uncover valuable insights from these datasets. Moreover, decision makers must be able to retain valuable insights from this erratic data. Such value can be provided using big data analytics. This paper aims to review the literature covered on Big Data analytics considering; what it is, its characteristics and its implication in decision making.

Introduction

“Big Data” is a term used to specify information. Information that due to its characteristics is unable to be processed by conventional database methods and tools[1] [2]. They are data sets whose size is beyond the ability of commonly used software tools and storage systems to capture, store, manage, as well as process the data within a tolerable elapsed time [4]. [12] Today, enterprises are exploring large volumes of highly detailed data to better Business Intelligence[5]. Business intelligence (BI) is the ability of a company to make meaningful use of data it collects during its day-to-day business operations (Kimble & Milolidakis, 2015).[6]

The BI could play an important role in improving organizational performance by identifying new opportunities, highlighting potential threats, revealing new business insights and enhancing decision making processes among many other benefits [7] In addition, ‘big data’ has the capability of transforming the decision making process by allowing enhanced visibility of firm operations and improved performance measurement mechanisms [10] (Literature bookmark 2) Literature paper states that McKinsey and Company found that “collecting, storing, and mining big data for insights can create significant value for the world economy, enhancing productivity and competitiveness of companies and the public sector and creating a substantial economic surplus for consumers” [9] It is these insights that retailers can achieve up to 15–20% increase in ROI (Return on Investment) by bettering insight from analytics [8]

With any innovation, big data presents multiple challenges to adopting firms. For example [14], notes that enterprises will face challenges in processing speed, data interpretation, data quality, visualization, and exception handling of big data. I highlight 4 technical and managerial challenges: data quality, data security, privacy and investment justification.

Currently, BI solutions mainly focus on structured and internal data of enterprise. As a result, a lot of valuable information embedded in unstructured and external data remains hidden, which could potentially lead to incomplete view of the reality and resultantly biased business decision making. However, there are great advantages in using BI with the advent of computing and internet technologies facilitatating the collection of a large volume of heterogeneous data from multiple sources on an ongoing basis thus posing new challenges and opportunities for business intelligence.

The subsequent report, aims to discuss data security, privacy and investment justification in order to investigate how this may be beneficial for businesses to thrive. T