Big Data Analytics: A Necessary Roadmap for Enterprises

Big Data Analytics: A Necessary Roadmap for Enterprises

Iman Raeesi Vanani (Allameh Tabataba'i University, Iran) and Faezeh Mohammadipour (Allameh Tabataba'i University, Iran)
DOI: 10.4018/978-1-5225-5137-9.ch002

Abstract

The idea that we can get value from data has been discussed, but the main challenge is to use data effectively in order to facilitate smarter and better decision making and surpass our competitors. The change leaders in organization are now dealing with big data from both within and outside the enterprise, including structured and unstructured data, machine data, online and mobile data to supplement their organizational data pool and provide and facilitate the way through which the businesses can compete and operate successfully. Companies that invest in big data can have a distinct advantage over their competitors. Therefore, in this chapter, the concepts of big data analytics along with the relevant description of different categorization, capabilities, challenges are firstly explained, and then big data analytics techniques and methods are introduced and discussed to make the readers familiar with the way big data is applied in the enterprises.
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Introduction

Big Data has been a buzzword in the last decade and it refers to the dynamic, large and disparate volumes of data being created by people, tools and machines. Big Data Analytics is also the process of examining Big Data to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more informed business decisions. Big Data requires new, innovative and scalable technology to collect, host and analytically process the vast amount of data in order to create real-time business insights that relate to consumers, risk, profit, performance, productivity management and enhanced shareholder value (Erevelles et al., 2016). It includes information obtained from social media, internet-enabled devices (including smartphones and tablets), machine data, video and voice recordings and the continued preservation and logging of structured and unstructured data. Moreover, Big Data nowadays is characterized by the 5 following “V” (Lee, 2017):

  • Volume: Volume refers to the vast amount of data being created every second in comparison with the traditional data sources. In this regards, we can think of all email, twitter messages, photos, video clips, sensor data that we produce and share every second. On Facebook alone we send 10 billion messages per day, click the “like” button 4.5 billion times and upload and upload 350 million new pictures every day.

  • Variety: Variety refers to data comes from different sources and is being created by machines as well as people. In the past, we only use the structured data in the rational databases but currently 80% of data is unstructured data such as photos and videos that cannot be stored in the tables.

  • Velocity: Velocity refers to speed of data which is being generated extremely fast; a process that never stops even while we sleep. For example, it takes millisecond for a trading system to analyze social media networks to pick up the signals that trigger decisions to buy or sell shares.

  • Veracity: Veracity refers to Big Data that is sourced from many different places; as a result, you need to test the veracity/ quality of the data.

  • Value: It is well and good to have access to Big Data but if we cannot to drive value from data, it becomes absolutely useless. Therefore, it is the most important “V” in Big Data and we should have the clear understanding of the costs and benefits of Big Data before starting any related initiatives in this area.

Besides from different characteristics of Big Data, the type of data is also important. Therefore, two types of data are found in the area of Big Data: structured data and unstructured data. Structured data refers to information that existed in a relational database and is searchable by simple, straightforward search engine algorithms or other search operations; whereas unstructured data is essentially the opposite and usually refers to information that doesn't reside in a traditional row-column database and it often include text and multimedia content (Baars et al., 2008). Related examples to unstructured data include e-mail messages, word processing documents, videos, photos, audio files, presentations, webpages and many other kinds of business documents.

Nowadays, Big Data has found many applications in different areas such as Smart Grid case, E-health, Internet of Things (IOT), Public Utilities, Transportation and logistics, Political Services and Government Monitoring (Oussous et al., 2017). Therefore, it leads to change the necessary elements for many organizations that just consider people, process and technology as a triangle which should be considered for equipping themselves against both rapid changes and competitors but currently the role of Big Data converts the mentioned triangle into a rectangle one and therefore a holistic view of the four respective elements are required. In other words, being able to incorporate data into business routines, strategy and the whole operations must be a need and data should be considered simultaneously with process, people and technology to have a comprehensive attitude towards the whole organization.

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