Prelims

Five Vs of Big Data

While the term “big data” is relatively new, the act of gathering and storing large amounts of data is characterized by five Vs (Jeble, Kumari, & Patil, 2016):

• Volume – Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.

• Velocity – Data stream in at an unprecedented speed and must be dealt with in a timely manner. Radio Frequency Identification (RFID) tags, sensors and smart metering are driving the need to deal with torrents of data in near real time.

• Variety – Data come in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.

• At Statistical Analysis System (SAS), two additional dimensions are considered when it comes to big data:

• Veracity – In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.

• Value – Today’s data come from multiple sources, which make them difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or data can quickly spiral out of control for social and economic outcomes.

Big data and business analytics are conceived to provide a platform for academicians and practitioners to identify and explore the solutions to various problems in society, environment and industry using advance analytic tools. Business analytics is the process of converting data into insights (Xavier, Srinivasan, & Thamizhvanan, 2011). It is “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” With the increase in the availability of data, analytics has now become a major element in both the top line and the bottom line of any organization. However, the rate of absorption of analytics in decision-making is slow. This is due to the fact that there are several ambiguities in the definition and scope of analytics (Jeble, Kumari, Venkatesh, & Singh, 2019). An effective use of analytics must grow with time and experience in most individuals. There is much more in analytics besides descriptive data collection and reporting. By 2025, there will be an increasing need for more data analytics to be involved in business. Effective performance management analytics is an integration of IT-based solutions, management accounting applications and analytical methods. Therefore, descriptive, predictive and prescriptive analytics are essential for any business.

Use of analytics in better decision-making has evolved since past. In the late 1960s, technology-based analytics had been the base of the decision support system (Jeble, Kumari, & Patil, 2018). Later, in 1987, scanner panel data were used to analyze decision-making in retail shops which was further followed by OLAP, a software analytical tool. In 1990, Enterprise Resource Planning system became the prime use for analytics in company. This led to the evolution of analytics using internet, e-commerce, mobiles, sensors and software analytical tools. With time big data predictive analytics has been used in decision-making in different streams. Analytics in big data has been useful in improving the visibility and coordination (Dubey et al., 2018). Businesses can have better decision-making capability with the use and better understanding of data analytics (Agrawal, 2014).

Why is Big Data Important?

The importance of big data doesn’t revolve around the availability of data but the purpose of data. One can take data from any source and analyze them to find answers that enable (1) cost reductions, (2) time reductions, (3) new product development and optimized offerings and (4) smart decision-making. When big data are combined with high-powered analytics, managerial decisions can be performed such as

• Determining root causes of failures, issues and defects in near real time.

• Generating sustainable solutions for any stream.

• Recalculating entire risk portfolios in minutes.

• Detecting fraudulent behavior before it affects any organization.

Application of Big Data and Business Analytics

Big data have been in use by government institutes for forecasting weather patterns, discovering seismic activities that predict earthquakes and preparing descriptive reports. It has been in use by the economists to stimulate economic growth. Big data mining is the patterns in the data that are normally not looked by the users. These unlooked data also lead to several important information which can make decision-making smooth.

Big data are used in health insurance for predicting customer dissatisfaction through speech to text data from call center recordings (Devenport & Dyche, 2013). Several retail banks have focused on exploiting big data at times of financial crisis for doing a better job.

LinkedIn has used big data and data scientists to develop product features and product offerings. This has helped the consumers as well as the companies to make decisions about the product.

Google has constantly developed new products and services that have big data algorithms for search. Most of the companies are master’s in developing standard reports and multidimensional reports through big data analytics. In many companies, big data are directly focused on products, services and customers. Senior managers have used predictive analytics as the next step in data analytics.

Big data not only allow knowledge discovery efforts but also need to promote them. The sooner the business executives understand the value of knowledge discovery the better competitor they become. This can lead to high-level innovations and high rewards.

Big Data and Business Analytics for Decision-Making

In the current era, world is challenged with demanding customers, high competition, short product life cycles, rising costs of labor and materials, unemployment and unsustainability. Globalization is making it even more challenging as blurring boundaries among countries create level playing field for selling products and services across the globe. Firms need to make operational, tactical and strategic decisions based on available information. In addition to traditional decision support systems, big data provide additional tools to arrive at decisions.

Big data can provide valuable competitive intelligence (Jeong et al., 2016), help in dramatic cost reductions, substantial improvements and development of sustainable goals for the world.

Objectives

Data science and business analytics will bring together researchers, engineers and practitioners and encompass wide and diverse topics of application in almost every field. It will also invite the participation of scholars, analysts and data scientists to present their ideas, concepts and proof of works indicating application of big data and business analytics.

Target Audience

The primary target audience of this book includes researchers, academicians and data scientist from a variety of disciplines interested in analyzing and application of big data analytics. A secondary target audience consists of data analysts, students and scholars pursuing advanced study in big data.

Organization of this Book

This book is organized into eight chapters. A brief description of each of the chapters follows:

Chapter 1 authored by Aarti Mehta Sharma identifies that analytics is the science of examining raw data with the purpose of drawing conclusions about that information and using it for decision-making. It also looks at the pairing of visualization tools with different measurements of data. Before the formal written language, there were pictures which shared ideas, plans and history. Visualizations in the form of bar charts, scatter plots or dashboards are essential tools in business intelligence as they help managers to absorb information and take apt decisions quickly. Dashboards in particular are very helpful for managers as multiple charts and graphs giving the latest information about sales, returns, market share, etc. keep them up to date on the latest developments in the company. There are a number of visualization software in the market which are easy to learn and communicate the analyzed data in an easily understood form; the leading ones being Tableau, QlikView, etc. with each one having its positives.

Chapter 2 authored by Hiral R. Patel, Ajay M. Patel and Satyen M. Parikh takes the fundamentals for multimedia big data computing, feasibility study and salient features of multimedia big data and explore the technical problems and challenges to be addressed. It focuses on methodologies and approaches that are available from the perspectives of multimedia big data computing life cycle. Multimedia data are real-time unstructured, heterogeneous and multimodal as per the qualitative requirement. It has vast scope to mine model, learn and analyze the service provided by multimedia. An advance-level storage-related mechanism is also needed for efficient parallel processing, transmission and presentation. The multimedia data in form of videos are easily understood by human compared to textual data, but it’s more complex task to make it understandable to machines.

Chapter 3 authored by Gauri Rajendra Virkar and Supriya Sunil Shinde reviews that predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections, thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, KNIME, Orange, RapidMiner, WEKA, etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making.

Chapter 4 authored by Shakti Ranjan Panigrahy reviews the information of 300 customers in Anand districts of Gujarat, India. Cluster analysis techniques were presented to analyze the data. At the end, it was found high quality, better service, convenient location, presentation of food in parlor and restaurants and zero time delivery are playing key roles for getting customers for the food.

Chapter 5 authored by K.S.S. Iyer and Madhavi Damle presents an analysis of the method in advance predictive analytics. Varied or different domains are put together to establish a fine-tuned technique of “Random Point Process” (RPP) and “Product Density” (PD), two techniques in stochastic modeling, which can be used for advanced predictive analytics. This formulation arises from these techniques being used in different fields like energy requirement in IoT devices, growth of cancer cells, cosmic rays’ study, to customer equity and many more approaches.

Chapter 6 authored by Samir Yerpude addresses a paradigm shift observed in the last decade where transactional marketing is taken over by relationship marketing. Customer relationship management (CRM) has been an integral part of a business strategy in the current era. CRM integrates product sales, product marketing and, most importantly, customer service in a seamless manner to generate value for the organization as well as for its customers in short a win–win situation. Profoundly, CRM needs to be a part of the top management agenda and driven top-down instead of an IT initiative. Industrial revolution 4.0 is characterized by cyber-physical systems. Internet of Things (IoT) is the digital technology for the present and future. IoT primarily aids in gathering real-time data and transmitting the same over the internet to a central repository for consuming the same in business models. Real-time customer data analytics can be performed by customer-centric organizations to enhance CRM.

Chapter 7 authored by K. K. Tripathy and Sneha Kumari addresses that Rural Development constitutes of lot of big data related to rural employment with special reference to the world’s largest public works and wage employment generating poverty alleviation programme – Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA)The concepts of MGNREGA are novel and innovative though the programme continues to suffer from various rigidities depicted from the data. This drives us to the objectives of our research. The study has explored literature and big data on rural development with special reference to MNREGA, the upcoming challenges in rural employment with special reference to MGNREGA, identify gaps in existing literature and pave out future research direction for academicians, researchers and policy maker.

Chapter 8 authored by Jorge Tarifa-Fernandez, Almudena Martínez Aguilera and José Felipe Jiménez-Guerrero concludes and presents the critical importance and value of digital technologies which have led companies to remain competitive. Despite the benefits, the transformation of a firm into a digital-based one requires it to choose a set of digital technologies. In this sense, there may be multiple combinations of digital technologies by means of which firms can obtain their digitalization. Thus, each firm should decide which combination best fits its needs.

Dr Sneha Kumari

Assistant Professor, Vaikunth Mehta National Institute of Cooperative Management, Pune, India

Dr K. K. Tripathy

I.E.S., Director, Vaikunth Mehta National Institute of Cooperative Management, Pune, India

Dr Vidya Kumbhar

Assistant Professor, Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune, India