Decoding Big Data Analytics for Emerging Business Through Data-Intensive Applications and Business Intelligence: A Review on Analytics Applications and Theoretical Aspects

Decoding Big Data Analytics for Emerging Business Through Data-Intensive Applications and Business Intelligence: A Review on Analytics Applications and Theoretical Aspects

Vinay Kellengere Shankarnarayan (Dayananda Sagar College of Engineering, India)
Copyright: © 2020 |Pages: 15
DOI: 10.4018/978-1-5225-9750-6.ch004

Abstract

In recent years, big data have gained massive popularity among researchers, decision analysts, and data architects in any enterprise. Big data had been just another way of saying analytics. In today's world, the company's capital lies with big data. Think of worlds huge companies. The value they offer comes from their data, which they analyze for their proactive benefits. This chapter showcases the insight of big data and its tools and techniques the companies have adopted to deal with data problems. The authors also focus on framework and methodologies to handle the massive data in order to make more accurate and precise decisions. The chapter begins with the current organizational scenario and what is meant by big data. Next, it draws out various challenges faced by organizations. The authors also observe big data business models and different frameworks available and how it has been categorized and finally the conclusion discusses the challenges and what is the future perspective of this research area.
Chapter Preview
Top

Introduction To Big World

It’s helpful to have some historical background on big data. Here’s definition, big data are data that contains a greater variety arriving in increasing volumes and with ever-higher velocity. It is known as the three V’s(Savitz, 2013). According to McAfee, &Brynjolfsson (2012), in their article mentioned Data-driven decisions are much better decisions it’s as simple as that. Using big data enables managers to decide based on evidence rather than intuition. During 2012 itself, about 2.5 Exabyte of data continually are created each day, and that number is doubling every 40 months or so. More data cross the internet every second than were stored in the entire internet just 20 years ago. Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and '70s when the world of data was just getting started, with the first data centers and the development of the relational database.

Pal (2016) stated that, during 2005, people just began to learn the importance of data and how much data users generated through online services. Hadoop (An Open Source application as a service, created mainly to analyze bigger data sets) developed. Apparently (Alsghaier, etal., 2017) described that the big data are now mainstream, so we have to take it seriously and manage it professionally with the help of schema-less NoSQL databases to analyze unstructured data sets.

According to Chen, etal. (2014), there are opportunities that are related to data analysis in many organizations. It has generated an essential interest in business intelligence, which sometimes points to the techniques and technologies that help to produce a better understanding of the market and also make decisions wrong time, also concluded that the most significant value from big data when workers are free to explore their analyses. Establishing this type of environment leads the IT work team to change from serving into enabling models.

Figure 1.

Hypothetical classification of big data challenges

978-1-5225-9750-6.ch004.f01
Top

Research Scope

Big Data discipline is still evolving and not yet wholly established. Earlier RDBMS are used to store only structured data sets. However, today, nearly 80% of the data fetched is in an unstructured format, thus making it impossible to store data in RDBMS. Hadoop is used to store all structured, unstructured, and hybrid data sets, as shown in figure 2.

Figure 2.

Types of big data

978-1-5225-9750-6.ch004.f02

Polato, et.al. (2014) already conducted research studies on Apache Hadoop, Plödereder, et.al.(2014) focused mainly on Big Data in Logistics, Jamil, et.al. (2015) concentrated on Data Veracity for digital news portal, Abdellatif, et.al. (2011) on Software analytics, this paper, we try to widen the scope of theirreviews by investigating further and assess different challenges of big data and the showcase methods used to overcome those challenges. Through this research work provides insights into Big Data, there is a lack of a methodical approach to understanding the Business intelligence and Big Data analytics in business. Thus, this article acts as a framework of reference.

Complete Chapter List

Search this Book:
Reset