# Big Data Analytic Framework for Organizational Leverage
## 1 Introduction  
Big data have seen exponential growth over the past few decades, changing the information availability landscape and everything it interacts with. The big data concept was primarily introduced in the 1990s [1] but it was not until the early 21 st century when it had its revolutionary breakthrough, evolving from decision support and business intelligence (BI) systems [2]. Now, big data refers to huge datasets comprising structured, semi-structured, and unstructured data with challenges in storage, analysis, and visualization for more processing [3]. Data are being generated at phenomenal rates. Recent research reports that only 5 exabytes of data were generated by humans when the concept of big data first gained emphasis [4]. Currently, this many data can be created in a day or two. In 2010, the world generated over 1 zettabytes of data, which was expanded to 2.7 zettabytes in 2012. This number was predicted to double every two years. Being as humongous as it is, big data have overwhelmed traditional data mining tools and now requires a different approach, which has led to the development of big data analytics (BDA). This is the process of sieving through massive and complex data streams using novel technologies such as business intelligence, cloud computing, and machine learning to disclose hidden correlations and patterns and presenting this information to business users in real time for effective decision-making [3]. The data are captured from different online platforms, such as from e-commerce activities, social media interactions, organizational and individual web sites, or from day-to-day information exchanges made by users. These data streams can also be generated from sensor-based data exchanges, such as Internet-of-Things (IoT) devices, radio-frequency identification (RFID) readers, microphones, cameras, mobile devices, and similar web-enabled equipment [5]. In recent decades, the emphasis on BDA has been increasing in organizations. It is considered to have changed the way in which organizations develop and grow [6]. The recent literature indicates that organizations that leverage BDA have been able to realize a significant return on investment by rendering customer focused services [7]. A recent case study by Teradata [8] reported that a British multinational telecommunications conglomerate trading firm, Vodafone New Zealand, was able to develop more finely targeted campaigns that had a higher chance of success by using big data to accurately predict the traits of their customers. In another renowned case of Continental Airlines [9], use of data warehousing and real-time BI helped to improve several performance metrics, resulting in its turnaround, moving it from 10 th place to become the first ranked airline. Although BDA pays off in some organizations, it has achieved little impact for others due to its innate difficulty in process development. As the literature has revealed [3; 10; 11], the application of BDA faces multiple challenges. First, it is difficult to set up a robust physical infrastructure which is based on a distributed model that can physically store the big data. A sophisticated architecture with thousands of nodes and multiple processors needs to be created (e.g., Hadoop framework), which comes at a substantial cost. This hardware infrastructure investment issue greatly increases the barrier to entry for organizations with less margin of investment to implement BDA [10]. Aside from this, organizations also face the challenge of hiring personnel who are trained in business analytics and implementation methods and know how to extract information that will have potential to improve business decision-making [12]. These challenges are especially apparent in developing countries as the vast majority of infrastructures and trained personnel usually reside in developed countries [10]. Even developed countries face a workforce shortage in this area.