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Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

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.

This chapter uses multiple research methods, including quantitative science mapping analysis (SciMat) and a qualitative literature review, to provide insight into the academic debate unfolding at the intersection of big data and business processes. SciMat analysis based on keyword co-occurrence enabled identifying 12 of the most productive research themes, as reflected in a poll of 301 articles about big data and business processes. The three most important themes are: firm performance, Industry 4.0, and innovation. The traditional literature review on firm performance indicated that big data analytics (BDA) positively influence business process performance and have a beneficial impact on a firm’s performance, that is, the role of big data is viewed as critical in the context of Industry 4.0 because it enhances productivity and improves business processes. The benefits of BDA can be achieved only if the organizational obstacles related to planning, workforce attitude, and alignment with strategy are overcome. Moreover, big data is perceived as a significant source of innovation in an organization and can be conceptualized with the use of a resource-based view (RBV) of the firm. BDA positively influence business processes, which is strengthened by adequate implementation and openness to innovation.

Introduction: The insurance sector provides security to society by pooling resources to manage risks. Insurers’ improved ability to analyse risks by examining vast amounts of granular data has considerably refined this technique. Compiling and analysing the fine data sets is now transformed into the ‘Big Data’ technique. The introduction of big data analytics (BDA) is transforming the insurance industry and the role data plays in insurance.

Purpose: This chapter will attempt to examine the applications and role of big data in the insurance sector and how big data affects the different insurance segments like health insurance, property and casualty, and travel insurance. This chapter will also describe the disruptive impact of big data on the insurance market.

Methodology: Systematic research is carried out by analysing case studies and literature studies, emphasising how BDA is revolutionary for the insurance market. For this purpose, various articles and studies on BDA in the insurance market are selected and studied.

Findings: The execution of big data is continuously increasing in the insurance sector. The performance of big data in the insurance market results in cost reduction, better access to insurance services, and more fraud detection that benefits the customers and stakeholders. Therefore, big data has revolutionised the insurance market and assisted insurers in targeting customers more precisely.

By the end of this chapter, you should be able to demonstrate an understanding of:

The importance of big data in the information revolution.

The resource-based view of the firm and dynamic capabilities as they relate to big data.

The use of big data in marketing decisions.

Consumer security concerns over the storage and processing of big data.

The circular economy (CE) model is seen as an alternative model to the linear economy models, which seem to be reaching their physical limits. The CE business model aims to reuse materials and decrease the need for virgin materials. This requires the implementation of a reverse supply chain, close collaboration between actors, as well as well-organized logistics. For this reason, the CE companies have typically high demand for digitalized processes and the utilization of data on both operational and business development dimensions. Also the utilization of big data collected from the companies’ business environment can provide new opportunities for business development in CE. Despite the fact that utilization of data collected from the business environment and operations enables data-driven approaches for various decision-making functions in companies, many companies still struggle to figure out how to use analytics to take advantage of their data. In the small- and medium-sized enterprises (SMEs), in particular, the managers are facing difficulties with ever-increasing amounts of data and sophisticated analytics. Indeed, prior research identified several kinds of barriers to the effective utilization of data in SMEs. Still, research on data-driven decision-making remains scarce in CE context. This chapter presents a case study consisting of seven cases, all representing SMEs operating in the field of CE in Finland. In the case study, the barriers and practical challenges for data-driven decision-making in CE SMEs are investigated. Based on the case study results, this chapter proposes that utilization of data, lack of resources, lack of capabilities, and regulation are the main barriers to data-driven decision-making in CE SMEs.

The objective of the research is to explore the importance of online reputation management through some core concepts: technologies and entrepreneurship. Specifically, the research will explore how in a tourism ecosystem context, it is strategically relevant through the use of Big Data Analytics (BDA) to manage and improve online reputation management. An emphasis will also be placed on the concept of entrepreneurship and dynamic capabilities. Finally, the research also explores empowerment issue to shed some considerations on the development of tourists' online reviews.

Internet of things (IoT) and Big Data, which are among the pioneers of Industry 4.0 technologies, have gained great importance in recent years. Within the scope of Industry 4.0, organizations are trying to undertake digital transformation by adapting these two important technologies to their business processes. Undoubtedly, while this transformation provides great advantages for organizations in terms of management, organization, and marketing, it also carries disadvantages such as difficulties and complexity regarding the privacy of the collected data and systems. However, IoT and Big Data Analytics play a role as restructuring factors for products, services, and especially business processes. This study discusses the impact of IoT and Big Data Analytics on the digital transformation of organizations from the perspective of corporate culture, marketing, and management. Simultaneously, the effects of the COVID-19 epidemic that the world has experienced recently, on the business of institutions, are also discussed. By adopting IoT and Big Data Analytics, the attitudes, benefits, and challenges of the institutions that are or are not willing to realize digital transformation during the epidemic process are examined, and a projection is tried to be made to the post-COVID-19 period. While the study specifically highlights the positive effects of IoT and Big Data Analytics on the business, it sheds light on available opportunities and provides useful implications for managers and marketers.

An increased understanding of the capabilities needed for HR Analytics and how to build synergies from these capabilities is of practical and academic importance. Using the lens of Systems Theory, an explorative case study is performed in a multinational food distribution company that is building its HR Analytics Capabilities. In this study, the synergistic enablers and mechanisms have been examined in practice for the domain of HR Analytics and the BA Capabilities involved (clustered into Technology, Governance, Analytic Practices and Processes, People and Culture). Examples of (in)compatibilities, integration efforts, mechanisms and synergistic outcomes are given from the case organization. This study provides insights on how in practice the interaction between BA Capabilities can lead to synergistic relationships and synergistic outcomes and through what mechanisms and enablers this is being facilitated. The study contributes to HR Analytics and IS literature in terms of the use of synergistic enablers and mechanisms in practice.