Data Science and Analytics

Analytics is the science of examining raw data with the purpose of drawing conclusions about that information and using it for decision-making. Before the formal written language, there were pictures which shared ideas, plans, and history. Most of the knowledge that we have of our ancestors is from these pictures drawn on caves or monuments. In today’s world, 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. This chapter also looks at the pairing of visualization tools with different measurements of data.

The multimedia data are also known as interactive data. The multimedia is progressively turning into the “greatest big data” which are the most imperative and important hotspot for bits of knowledge and data. The multimedia data also provide incredible open door for the multimedia computing in the big data centric as a functioning disciplinary research field. As per current technological usage in terms of Internet or smart devices, the data manipulate in the form of digital. Massive multimedia data have been produced in the different forms like text, image, video, and audio which is shared among vast number of people. The multimedia data are real-time unstructured, heterogeneous, and multimodal. It has vast scope to mine model, learn, and analyze the service provided by multimedia. Of course, some primarily level challenges need to be addressed like analysis, storage, retrieval, and data processing. The most complicated thing in multimedia big data (MMBD) analytics is that the computer cannot understand higher level of semantics. The quality of experience (QoE) is the most evolving part of MMBD which are directly intended with storage and performance. MMBD are highly resource intensive. They often require dedicated processing capabilities in terms of graphical processing unit (GPU). An advance-level storage-related mechanism is also needed for efficient parallel processing, transmission, and presentation. Generally, non-multimedia data are always forming in text which is normally understood by machine. The multimedia data always in the form of videos are easily understood by human compared to textual data, but it is more complex task to make it understandable to machines. The MMBD performs the task by converting the human language to computer language in an efficient manner. This chapter is also introducing salient features of MMBD. The main aim of this chapter is to cover the fundamentals for MMBD computing and feasibility study. The chapter explores the technical problems and challenges to be addressed. It also focuses on methodologies and approaches that are available from the perspectives of MMBD computing life cycle. The chapter may be beneficial for the readers to understand the features, importance and application of MMBD.

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, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (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. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.

Life is changing very fast, and its impact is observed in food pattern, consumer behavior and its ultimate market. In these broad outlines, 300 customers were studied in Anand districts of Gujarat. The restaurants and parlors that were studied were franchise models of Gujarat state only. Here in this study, six franchise food retailers were purposively selected. Data were analyzed through cluster analysis techniques. 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.

This chapter has been seminal work of Dr K.S.S. Iyer, which has taken time to develop, for over the last 56 years to be presented here. The method in advance predictive analytics has developed, from his several other applications, in predictive modeling by using the stochastic point process technique. In the chapter on advance predictive analytics, Dr Iyer is collecting his approaches and generalizing it in this chapter. In this chapter, two of the techniques of stochastic point process known as Product Density and Random point process used in modelling problems in High energy particles and cancer, are redefined to suit problems currently in demand in IoT and customer equity in marketing (Iyer, Patil, & Chetlapalli, 2014b). This formulation arises from these techniques being used in different fields like energy requirement in Internet of Things (IoT) devices, growth of cancer cells, cosmic rays’ study, to customer equity and many more approaches.

A paradigm shift is 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.

A major chunk of rural people live on agriculture and other allied activities viz animal husbandry, dairying and fisheries, etc. Rural development constitutes of lot of big data related to rural employment which has driven this study to address a research question that what is the application of big data in rural development with special reference to the world’s largest public works and wage employment generating poverty alleviation program – Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA)? The concepts of MGNREGA are novel and innovative though the program continues to suffer from various rigidities depicted from the data. This drives us to the objectives of our research. The objective of the study is to explore literature and big data on rural development with special reference to MGNREGA, explore the upcoming challenges in rural employment with special reference to MGNREGA, identify gaps in existing literature and pave out future research direction. The present study paves various ways for future research directions for academicians, researchers and policy maker.

The implementation of digital technologies shifts the way firms manage their supply chains with the objective of obtaining closer relationships with their partners. The main improvement is that each partner can access others’ information in real time. This gives rise to the concept of digital supply chain where interconnectedness is the link. Applying digital technologies has reported innumerable benefits, and despite the fact that only a few firms make full use of them, they have become a very promising future trend.

This study aims to review the literature on supply chain and digital technologies in relation to the different benefits that each of these technologies provides in the different stages of the supply chain. Eventually, this will provide a guide to determine and select those technologies that best suit the needs of a firm according to their characteristics within the supply chain.