Big Data and Business Analytics Trends

In the late 1980s, data warehouse technology, which is generally categorized as online analytical processing (OLAP) was introduced by the relational database management system (RDBMS) companies to support the business decision and business intelligence. It was originally designed to archive large amounts of data out of production databases and to keep them lean and mean for good performance. In data warehousing, multiple copies of data are located on multiple database servers referred to as data mart. The data mart can be independent or an enterprise data mart. From there, data is then extracted and loaded into two analytical data marts. Here, the data analysts create their algorithms to run their jobs. One of the data marts links to a statistical analyst and the other to a business user. While data warehouse has not failed in creating business value through detailed reporting based on complex statistical modeling, it is challenging to continuously move data over the network and takes a long time to yield results. Furthermore, there are limitations in the data volume that can be stored on the system. In addition, current data creation is continuously generated, thereby making it difficult to process big data. Big data has garnered lots of attention recently in government, industries, sciences, engineering, healthcare and medicine, finance and prominently in businesses. Accordingly, data generated in these areas are characterized by high volume, inability to be categorized into the relational database management system and the data are generated, captured and processed rapidly. Therefore, the major challenges facing various organizations, industries, and other business sectors are how to design appropriate techniques to handle and process this large volume of data to ensure effective and efficient decision-making.

Recently, big data and business analytics approaches have been developed and implemented to analyze a large volume of data generated by different business organizations. Consequently, every business needs faster insight into growing volumes of transactional data. Analyzing data in real time helps organizations view the past and foresee the future. This is the beauty of streaming analytics and is endowed by knowing what occurred (descriptive), understanding why it happened (diagnostic), looking ahead to what might take place (predictive) and, ultimately, determining how to influence future occurrences (prescriptive). These four analytics flavors which are explained in Section 3 of this article have huge business benefits but are progressively more difficult to implement and use. The big data opportunity is not only for achieving high efficiency in business operations. There are also important opportunities for economic growth and improving the standard of living to the society. There are various ways in which big data analytics can improve business organizational outputs and industries. These include improved health care delivery, the standard of education, national security, and enable good governance. In addition, it has potential to assist policy-makers to gain insight in enabling policies that will grant safe playground for investors, help waste managers find the type of waste that is more generated from a particular locality and provide insight for sharing of waste collection material. Moreover, education monitoring agency can deploy big data and business analytics approaches to evaluate the performance of teachers and improve work attitude. Furthermore, mobile network location data can be used for traffic management to prevent traffic jams in big cities or better plan the public transport system.

The goal of this study is to implement a comprehensive investigation into big data and business analytics methods for improved business decision making, technological approaches, applications, and open research challenges. Furthermore, the study attempts to draw attention to the tremendous benefits big data has brought to companies in developed countries and how these can be replicated by indigenous business organizations. Moreover, the study discusses various challenges facing big data analytics with a focus on data security, management, characteristics, regulation, and compliances.

The phenomenon of big data analytics researches and implementation have been conducted by various researchers and industries for over a decade. This is due to the vital applications of big data in various areas such as the healthcare system, business decision-making, educational development, network optimization, travel estimation, and financial services. Therefore, quite a number of studies and reviews have been published in big data analytics, implementations and related technologies in recent time. Sing et al. reviewed hardware and software parameters for effective big data analytics developments. Additionally, Hashem et al. presented taxonomy and intersection of cloud computing and big data analytics. However, these studies focused on big data in cloud computing, software and hardware parameters such as data availability, scalability, and data size for implementation of big data analytics. The studies failed to discuss important big data analytics tools, their strengths, and weaknesses. Recently, reviews on big data analytics. Tsai et al. outlined big data analytics approaches in terms of data mining and knowledge discovery. The authors primarily discussed data mining algorithm that can be extended for big data analytics. Nonetheless, challenges, applications, current tools and data sources for big data analytics were not comprehensively discussed. Lanset et al. presented open sources tools for big data analytics, their advantages and drawbacks.

However, the review is narrowed only to tools while other criteria for effective big data implementation were not sufficiently covered. A closely related survey was presented recently by Mohammedi et al. and discussed big data technologies, applications and opens source tools for big data analytics. Conversely, our study differs with their review in many ways. First, the present review provides a broader view by focusing on the recent trends in big data and business analytics development. Second, we discussed platforms, opens source tools, their strengths and weaknesses. Third, this study presents big data success factors for analytic teams, their major functions, and challenges for the implementation of analytics in organizations. Fourth, the current study presents recent data sources and applications for big data and business analytics. Finally, the current review outlines and discusses open research directions in big data and analytics. The review is a timely exploration of big data and business analytics. The major differences between recent reviews and the current study are presented in Table 1 below:

Table 1. Recent review of big data analytics.

The remainder of this paper is organized as follows: Section 2 discusses the recent developments in big data technologies. Section 3 presents big data analytics platforms while Section 4 explores the success factors and challenges of big data implementation. Section 5 outlines the main applications and data sources for big data and business analytics. Section 6 summarizes the study and explores open research directions. Figure 1 outlines the structure of the paper.

Figure 1. Structure of the review paper.