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Companies can increase their profit in two ways: reduce costs or sell more. According to Business Insider, several well-known retailers, including Payless ShoeSource, The Limited and RadioShack, have recently gone [...]

There is an emerging market at the gate: the Internet of Things (IoT), which is expected to generate $14 trillion revenue in the next decade [2]. The IoT refers to [...]

Many e-learning service systems fail. This is particularly true for those sponsored by joint industry/university consortia where substantial economic investments are required up-front. This article provides an industry/university consortia reference model validated through experiences with the 8-year-old Teradata University Network. The reference model addresses basic and advanced sustainability capabilities that integrate partner, application, faculty, student, and e-learning service system issues. Our validation of the reference model includes a mapping to the literature regarding recent advances in e-learning service system infrastructures and supported capabilities. One outcome of that validation step is a comprehensive set of capability assessment questions to be used by consortia.

This is a case study of the implementation of a data and analytics-enabled Mission Control at one of the largest healthcare service providers in the state of Washington. Using data analytics and artificial intelligence, CHI-Franciscan (one of the largest healthcare organizations in state of Washington) is able to coordinate patient care more effectively and efficiently, improving safety for all its patients. This case study demonstrates tangible evidence from quantitative and qualitative analysis for return on investment for such a large project. [ABSTRACT FROM AUTHOR]

This paper examines the usage and effectiveness of analytics and information technology solutions to the micro-level and macro-level health-related challenges that emerged with the SARS-CoV-2 virus. Case studies are discussed in order to paint a portrait of the development, evolution, and effectiveness of health-related analytics tools and information technologies in the context of the response to COVID-19. By considering and synthesizing examples of what worked very well, what worked moderately well, the paper develops a lucid framework for classifying the effectiveness of different analytics and IT artifacts as they pertain to the coronavirus pandemic. The framework is proposed to have a great deal of utility not only for helping scientists and decision-makers to document and understand the current state of analytics and IT as they relate to COVID-19, but also as a forward-looking guide that can support managerial, medical, scientific, and political decision-making in preparation for future pandemics. [ABSTRACT FROM AUTHOR]