Application of Big Data and Business Analytics

The major component of agriculture production includes the type of seed, soil, climatic conditions, irrigation pattern, fertilizer, weed control, and technology used. Soil is one of the prime elements in modern times for agriculture. Soil is also one of the primary and important factors for crop production. The available soil nutrient status and external applications of fertilizers decide the growth of crop productivity (Annoymous, 2017). The upcoming research question that needs to be addressed is What is the application of soil data on soil health management for sustaining agriculture? Driven by the need, the aim of the present study is (a) to explore the soil parameters of a district, (b) compare the values with the standards, and (c) pave a way for mapping the crops with suitability of soil health. This study will not only be beneficial for the district to take appropriate steps to improve the soil health but also would help in understanding the causal relationship among soil health parameters, cropping pattern, and crop productivity.

Heavy metals play a crucial role in the economic development of any nation. Industries utilizing heavy metals, consequently, emanate a large volume of metal-containing liquid effluents. Since metals are non-renewable and finite resources, their judicious and sustainable use is the key. Hazardous metal-laden water poses threat to human health and ecology. Apart from metals, these industrial effluents also consist of toxic chemicals. Conventional physical–chemical techniques are not efficient enough as it consumes energy and are, therefore, not cost effective.

It is known that biomaterials namely microorganisms, plants, and agricultural biomass have the competence to bind metals, in some cases, selectively, from aqueous medium. This phenomenon is termed as “metal biosorption.” Biosorption has immense potential of becoming an effective alternative over conventional methods. The authors in the present chapter have used secondary data from their previous research work and attempted to develop few strategic models through their feasibility studies for metal sustainability.

Land bank distribution in India is a complex subject and with the family size reduction, the average land holding is going down. Maintenance of the land records in physical format is very difficult. There are thousands of court disputes for different land parcels and managing the same is becoming a complex task. The aim of the chapter is to design a conceptual framework with electronic landbank records in dematerialized form, so that they can be easily maintained and traded. Associated benefits of implementing such system are also discussed. Authors have used secondary data published in previous research work in the area of Geographical Information System (GIS) and business analytics to analyze the prospects of land bank dematerialization and its possible applications. The chapter focuses on the need of regulatory support and associated IT infrastructure to put the plan in action. If implemented, this change can help India to transform its land management process and will also enable to explore commercial utilization of agricultural land and urban land plots for planned development.

This chapter analyses potentials of including online search volume data in modeling the demand series of consumer products. Forecasting future demand for products of a company represents one of the important parts of planning and conducting business in general. Thus, the purpose of this chapter is twofold. The first purpose is to give a critical overview of the existing research on the topic of forecasting and nowcasting demand and consumption. The other purpose is to fill the gap in the literature by empirically comparing several approaches of modeling and forecasting demand and consumption on real data. Results of the empirical analysis show that including online search volume data can enhance modeling and forecasting of demand series, especially in times of economic downturns. Thus, it is advised to use such an approach in modeling of consumer demand in a business so that better business performance in terms of profits could be obtained.

Food Industries have to cater a plethora of consumers having variety of tastes. For sustaining in such environment companies create their unique selling point and big data helps them to analyze market situation for such purpose. In this book chapter, the supply chain of fruits and vegetables and the post-harvest losses encountered at each stage in absence of data analytics is discussed. This can be an opportunity for the food industries to reduce food loss and gain better returns on investment by going for a digital transformation. Companies combine big data with technologies like machine learning and artificial intelligence to get faster and more personalized experiences. This chapter includes comparative case studies of food and retail sector for better understanding of the outcome.

The aim of this chapter is to develop a strong research base for the academia and the industry to understand the importance of data analytics in International Trade. This chapter focuses on the case of cotton trade from India and explores different methodologies developed by the World Bank and International Trade Center to analyze the Big Data available on export and import. Through Big Data analysis, this chapter attempts to find out the export performance, market demand, export potential, and attractive markets for Indian cotton. This chapter also explores the trade competitiveness of Indian cotton over the years. The data through appropriate analysis can address some simple yet complicated questions in trade like what export potential the commodity holds, if the commodity is competitive or not in international market, what are new markets to look up to, and other similar questions. In other words, this information could make huge difference in decision-making of traders and policymakers directly, and farmers indirectly.

Aadhaar card is an innovative step taken by the Government of India to facilitate smooth functioning of government welfare programs among the needy citizens of this country. This chapter deals about the Aadhaar Project of the Central Government, its features, its impact on the welfare schemes of government, etc. Second, it also deals with the challenges and loopholes associated with the Aadhaar scheme which eventually led to the case of Justice K. S. Puttaswamy and Another v. Union of India [Writ Petition (Civil) No. 494 of 2012]. At last, the chapter deals with the potential challenges which the Aadhaar scheme may face even after it has been declared constitutional by the Apex Court in the case of Justice K. S. Puttaswamy and Another v. Union of India [Writ Petition (Civil) No. 494 of 2012].

Customer segmentation is an important research area that helps organizations to improve their services according to customer needs. With the increased information that shows customer attitudes, it is much easier and also more necessary than before to analyze customer responses on different campaigns. Recency, frequency, and monetary (RFM) analysis allows us to segment customers according to their common features. In this chapter, customer segmentation and RFM analysis are explained first, then a real case application of RFM analysis on customer segmentation for a Fuel company is presented. At the end of the application part, possible strategies for the company are generated.