# Big Data Analytic Framework for Organizational Leverage
## 1 Introduction
For example, the US, a country well-known for its technical advancements, is estimated to have had a shortage of 160,000 big data professionals by 2018 [13]. Therefore, organizations will have to train their own workforce to possess the necessary IT administration and application skill sets, which will further increase the cost of implementing BDA. Those who are experienced with BDA also face challenges, the main one being data communication. As a network-based application, BDA has an even higher communication cost in comparison to the processing cost. Therefore, a key challenge of BDA application is in minimizing the communication cost, keeping bandwidth and latency at a satisfactory level [14]. Another challenge that big data workforces typically face is security, as cyber attackers may tamper with the data under exchange or shut down servers with operating system attacks [11]. In big data management, the sheer amount of data entering BDA each minute becomes unaccountable. The server being shut down for even the slightest period can be detrimental to organizations. Driven by these aspects, this paper examines the role of BDA in different organizations to develop an in-depth understanding of its capabilities and potential benefits. The existing research largely looks at the importance of big data analytics, its challenges, and the process of implementation [4; 10; 11; 15]. Thus, this paper is motivated to explore the capabilities of BDA and its potential benefits by answering the following research question: how can organizations evaluate big data to achieve their strategic business objectives? To address this research question, two key objectives of this research are (1) to identify the capabilities of big data analytics, and (2) to propose a theoretical framework that organizations can potentially implement to leverage big data. Case studies were conducted to examine how existing organizations leverage benefits through the BDA process. Primary data were collected using semi-structured interviews from six data analysts or associated technologists of three large companies (two participants from each company) who engage in frequent use of big data analytic tools. Questions were asked to understand the process of BDA implementation based on their experiences and how organizations source, process, analyze, and visualize big data to achieve their strategic business goals. Key findings identified four main components of BDA--system coordination, data sourcing, big data application service, and end users--each having a major significance in the BDA process to produce business insights. Findings from this research can assist in developing an architectural BDA framework of significance to both academic researchers and business professionals in identifying the major process activities and outcomes. The study insights inform researchers and practitioners about the effectiveness of BDA and the implementation process. The structure of this paper is as follows. First, we provide a background of big data analytics, its challenges, and the research intent of this study. Then, we review the published literature detailing present and future expectations. Next, we discuss the importance of BDA in different industry settings and the development of its capabilities and characteristics in the present context. The research methodology is described next, which explains the three cases and the theoretical framework used in this study. This is followed by the content analysis of the data collected from the different organizations and discussion of the findings, which lead to further development of the theoretical framework. Finally, the conclusions, limitations, and future research directions are presented.