Big Data and Advance Analytics: Architecture, Techniques, Applications, and Challenges

Big Data and Advance Analytics: Architecture, Techniques, Applications, and Challenges

Surabhi Verma (National Institute of Industrial Engineering, Mumbai, India)
Copyright: © 2017 |Pages: 27
DOI: 10.4018/IJBAN.2017100102
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Abstract

The insights that firms gain from big data analytics (BDA) in real time is used to direct, automate and optimize the decision making to successfully achieve their organizational goals. Data management (DM) and advance analytics (AA) tools and techniques are some of the key contributors to making BDA possible. This paper aims to investigate the characteristics of BD, processes of data management, AA techniques, applications across sectors and issues that are related to their effective implementation and management within broader context of BDA. A range of recently published literature on the characteristics of BD, DM processes, AA techniques are reviewed to explore their current state, applications, issues and challenges learned from their practice. The finding discusses different characteristics of BD, a framework for BDA using data management processes and AA techniques. It also discusses the opportunities/applications and challenges managers dealing with these technologies face for gaining competitive advantages in businesses. The study findings are intended to assist academicians and managers in effectively quantifying the data available in an organization into BD by understanding its properties, understanding the emerging technologies, applications and issues behind BDA implementation.
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Introduction

In today’s highly competitive markets enterprises are differentiated by their ability to make timely, accurate, and effective decisions at all levels – strategic, tactical, and operational – to address different business processes and performance (Tien, 2013). Increasingly, companies in almost every industries around the world are analyzing Big Data (BD) (both structured and unstructured) to create and capture value through real-time decision-making (Amankwah-Amoah, 2015). BD is defined as high volume, high velocity or/and high variety information assets which require new forms of processing to enable enhanced insight discovery, decision-making, and process optimization (Gartner, 2001; Kshetri, 2014). More specifically, a data set is called BD if it is difficult to capture, store, analyze and visualize using existing technologies in enterprises (Halaweh, 2015). To capitalize advantages from BD, a firm needs Advanced Analytics (AA) techniques and technology to capture, curate, analyze and visualize ever-growing data sets (Gandomi and Haider, 2015). This integration of BD with AA is collectively known as Big Data Analytics (BDA) (Mcneely and Hahm, 2014). BDA is used to describe the data sets that is so large (from terabytes to exabytes) and complex (from social media data to the sensor and mobile data) that they require advanced data storage, management, analytical and visualization techniques and technologies (Chen et al. 2012). It helps in combining historical data with present events and projected future actions (Chen and Zhang, 2014). To uncover the value from BD, it needs to be tapped, analyzed and used for decision making (Kimble and Milolidakis, 2015).

BDA is the subject of attention for business managers, researchers, and government, and to some extent, challenge (Bihl et al., 2016). The exponential rise of BDA in very short period left firms unprepared to handle BD (Bendler et al., 2014). In the past, the rise of new technologies concepts was first discussed in technical and academic publications. The fast evolution of BDA technologies and extremely competitive business environment left little time for the discourse to develop and mature in the academic domain (Bhimani, 2015). There are several articles, industrial reports and books on BDA, including Big Data@work, but not enough fundamental discourse in academic publications (Kwon et al., 2015). This lagging of academic literature in BDA context implies that a coherent understanding of this emerging technology and its applications in different areas is yet to be developed. For instance, there is little consensus on the fundamental question of what quantify BD (Gu and Zhang, 2015), which type of data and AA techniques is suitable in a particular context (Tan et al., 2015) and how the information extracted after analysis of BD could be used across industries and for public welfare by the government (Vera-Baquero et al., 2015). Thus, there is need to extend the academic literature of BD and AA concepts, techniques, benefits, and challenges.

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