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The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business value model to explain BDA potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs.

The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the BDA current and potential benefits and business value creation path for big data academic and learning analytics success in HEIs.

The pressure of compliance with all legal and regulatory requirements and competition had pushed HEIs hard to adopt BDA tools. However, the study found out that application of risk and security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, the results provide new insights to higher education administrators on ways to create BDA capabilities for HEIs transformation and suggest an empirical foundation that can lead to more thorough analysis of BDA implementation.

A distinctive theoretical contribution of this study is its conceptualization of understanding business value from BDA in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of BDA and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area.

The article's objective is to analyze the consequent factors of Big Data Analytics Capability for firms in the competitive scenario, using different analytical models.

The research had a quantitative approach, using a survey of data from firms located in the state of São Paulo – Brazil. Structural Equation Modeling (SEM) was used to validate the model.

The results reveal that all hypotheses were accepted. Business value was the construct that had the most explanatory power in the model. It is necessary to invest more in analytical tools, as well as people trained in the analysis of these models, in addition to a change of mindset, which will dictate the bias of the firm's strategic decision-making. The Big Data analysis is evident from firms' growing investments, particularly those that operate in complex and fast-paced environments.

The proposed theoretical model makes it possible to verify firms' analytical structure and whether they are better positioned to analyze customer data and information in real-time, generate insights and implement solutions to maintain and improve their market position.

The contribution of this article is to present a proposal to expand the research models in the literature that analyzed the direct and indirect relationship between “Big Data Analytics Capability” and “Product Innovation Performance”.

This research attempts to highlight the concept of big data analytics in predictive maintenance for maintenance management of government buildings in Malaysia.

This study uses several empirical analyses such as vector autoregression (VAR), vector error correction model (VECM), ARMA model and Granger causality to analyse predictive maintenance by using big data analytics concept.

The results indicate that there are strong correlations among these variables, which indicate reciprocal predictive maintenance of maintenance management job function. The findings also showed that there are significant needs of application of big data analytics for maintenance management in Putrajaya, Malaysia, to ensure the efficient maintenance of government buildings.

The conducted case study has demonstrated the empirical perspective which streamlines with the big data analytics' concept in maintenance, especially for analytics' support with appropriate empirical methodology

Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data.

An extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field.

The study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences.

The paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful.

The paper tries to highlight the current trends and facets of data mining.

Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM.

Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique.

Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective.

These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM.

The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.

The purpose of this paper is to analyze the inadequacies of current business education in the tackling of the educational challenges inherent to the advent of a data-driven business world. It presents an analysis of the implications of digitization and more specifically big data analytics (BDA) and data science (DS) on organizations with a special emphasis on decision-making processes and the function of managers. It argues that business schools and other educational institutions have well responded to the need to train future data scientists but have rather disregarded the question of effectively preparing future managers for the new data-driven business era.

The approach involves analysis and review of the literature.

The development of analytics skills shall not pertain to data scientists only, it must rather become an organizational cultural component shared among all employees and more specifically among decision makers: managers. In the data-driven business era, managers turn into manager-scientists who shall possess skills at the crossroad of data management, analytical/modeling techniques and tools, and business. However, the multidisciplinary nature of big data analytics and data science (BDADS) seems to collide with the dominant “functional silo design” that characterizes business schools. The scope and breadth of the radical digitally enabled change, the author are facing, may necessitate a global questioning about the nature and structure of business education.

For the sake of transparency and clarity, academia and the industry must join forces to standardize the meaning of the terms surrounding big data. BDA/DS training programs, courses, and curricula shall be organized in such a way that students shall interact with an array of specialists providing them a broad enough picture of the big data landscape. The multidisciplinary nature of analytics and DS necessitates to revisit pedagogical models by developing experiential learning and implementing a spiral-shaped pedagogical approach. The attention of scholars is needed as there exists an array of unexplored research territories. This investigation will help bridge the gap between education and the industry.

The findings will help practitioners understand the educational challenges triggered by the advent of the data-driven business era. The implications will also help develop effective trainings and pedagogical strategies that are better suited to prepare future professionals for the new data-driven business world.

By demonstrating how the advent of a data-driven business era is impacting the function and role of managers, the paper initiates a debate revolving around the question about how business schools and higher education shall evolve to better tackle the educational challenges associated with BDADS training. Elements of response and recommendations are then provided.

This paper aims to solve the problems of big data analytics for prediction including volume, veracity and velocity by improving the prediction result to an acceptable level and in the shortest possible time.

This paper is divided into two parts. The first one is to improve the result of the prediction. In this part, two ideas are proposed: the double pruning enhanced random forest algorithm and extracting a shared learning base from the stratified random sampling method to obtain a representative learning base of all original data. The second part proposes to design a distributed architecture supported by new technologies solutions, which in turn works in a coherent and efficient way with the sampling strategy under the supervision of the Map-Reduce algorithm.

The representative learning base obtained by the integration of two learning bases, the partial base and the shared base, presents an excellent representation of the original data set and gives very good results of the Big Data predictive analytics. Furthermore, these results were supported by the improved random forests supervised learning method, which played a key role in this context.

All companies are concerned, especially those with large amounts of information and want to screen them to improve their knowledge for the customer and optimize their campaigns.

Introduction: New ideas and concepts of big data have emerged in recent years in response to the astounding growth of data in many industries. Furthermore, the phenomenal increase in the use of the internet and social media has added enormous amounts of data to conventional data processing systems. Still, it has also created challenges for traditional data processing.

Purpose: A significant characteristic of the insurance sector is critically dependent on information. This sector generates a great deal of structured and unstructured data, which traditional data processing techniques cannot handle. As compared to conventional insurance data processing and decision-making requirements, this lesson shows an analysis of data technology’s value additions.

Research methodology: The author assesses the primary use of cases for data in the insurance industry via a case study analysis. From the perspective of the insurance sector, this chapter examines the concepts, technologies, and tools of big data. A few analytical reviews by the insurance company are also provided, which justified several gains gained either through inefficient processing of massive, diverse data sets or by supporting better decisions.

Findings: This chapter demonstrates the importance of adopting new business models that allow insurers to move beyond understand and protect and become more predictive and preventative by using the tools and technologies of big data technology.