Teresa D Harrison

In this chapter, we discuss the current gap between academic training and the needs within the business community, the potential for this gap to widen, and the role joint work between academic and industry experts can play in bridging this gap. We highlight the particular case of business analytics, calling attention to the current business landscape and the need for strong training of future employees, grounded in both rigorous theoretical background and links to the practical applications. The chapter concludes by emphasizing the particular contributions of each chapter and making a case for this type of work to be among the first of many steps in creating more meaningful dialogue between higher education and business practitioners.

Project management constitutes a powerful lever as organizations face increasing pressure to manage projects to budget, on time, and deliver more insights, in less time and with rapidly increasing amounts of data. This is critical especially in business analytics, with more than75% of organizations planning big data investments over the next several years. But the manipulation of massive amounts of data presents challenges - budgetary, time constraints, execution, proper manager skillsets, and such like. These challenges have cramped recent project rollouts, as only 37% of organizations have deployed big data projects in the past year; this suggests that filling the gap between data and insight remains a substantial hurdle as well as evolving need of project management for such projects. This chapter offers real-world examples of how project management professionals tackle big data challenges in a rapidly evolving, data-rich environment. Simultaneously, it establishes a bridge between business and academia as they both recognize the joint necessity to develop highly trained project managers to utilize the powerful and cutting edge analytical tools available to create value.

This chapter discusses how changes in the business and technological landscape affect marketing practice in industry and academic research. One difference is that marketers have turned to new media and digital marketing tools to understand better their customers and how they interact with their brand. Another development has been the steady convergence between published marketing theory in the academic literature and practitioner research. The chapter also draws attention to how new media marketing and analytics has fostered new insights about the customer journey, such as the creation of the loyalty loop and the need for alignment in marketing strategy. The implications for analytics education are also examined in the chapter with recommendations for curricula shifts and training as they relate to higher demand for and a shortage of qualified graduates. The chapter concludes with a case study, "Einstein Health System: Erectile Dysfunction," which provides a straightforward illustration of the potential benefits for an organization to align their analytical methods with their marketing strategy.

Cognitive computing is a form of problem-solving that incorporates machine learning, big data, data mining, natural language processing, machine vision, robotics, and other strands of artificial intelligence. Cognitive computing solutions can be used as sole or partial solutions to augment decision-making. The financial services industry is in a state of transformation, driven by the convergence of rapid changes in financial service technologies (fintech) - including cognitive computing, the digitization of the consumer, the emergence of younger investors (millennials), increased regulatory scrutiny (DOL regulation), and continued fee compression for products and services. Cognitive computing offers a disruptive opportunity in the financial services industry by not only empowering the financial intermediary but also by delivering increased engagement and value to the consumer. This study examines how the use of cognitive computing to improve financial advice can provide value for the financial intermediary and the end consumer. For the intermediary, the study will assess how cognitive computing can augment and supercharge the expertise of the financial advisor, enabling the advisor to deliver improved advice. For the consumer, the study will assess how cognitive computing can deliver high-quality, accurate advice comparable to that of a human advisor.