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In the past decade, library literature has witnessed a spate of studies documenting different aspects of Collaborative Virtual Reference Services (CVRS) and a significant amount of valuable information is spread across numerous individual reports. With the support of the Institute for Museum and Library Services, the authors of this chapter undertook a synergistic effort to examine these studies and identify the popular governance models as well as shared challenges and benefits. They conducted a supplementary survey of librarians with personal experience working in CVRS. The authors found that while collaborative structures are myriad, many utilize similar staffing and management strategies. Benefits of CVRS include shared staffing responsibilities, the extension of service hours, professional and community development, access to specialists, and mitigating the risks of a new service, while challenges include answering local questions, cultural differences, and software and technology problems. The literature on CVRS primarily focuses on single collaborations. While these in-depth examinations are valuable, they cannot provide a “big picture” of how libraries may work together to provide a service. As budgets shrink and ICT-facilitated connections grow, collaboration is an option to which many libraries are turning to for the provision of reference as well as other services. The quality of such collaborations may be improved by considering the lessons presented in this chapter, resulting in better service.

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.