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Many corporations are improving the capa­bilities of traditional Business Intelligence (BI) systems to permit superior moni­toring, measurement, and management of business perfor­mance.

Internet-based systems generate huge amounts of data, which allow us to better understand how people interact on markets or in social media. Many new types of information systems draw on the availability of data about user behavior or sensor data about the environment. Such information systems adapt to the users and provide better ways to coordinate. Let’s pick a few examples to make the point. Recommender systems are probably among the most well-known types of analytics-based information systems. They collect data about user preferences to then provide tailor-made recommendations for books, movies, or other products. By now, there is a large body of literature about mathematical methods such as matrix factorization or collaborative filtering, which allow for effective recommendations. At the same time, there is a growing behavioral literature analyzing the impact of these systems on human decision making. Thus, the topic addresses both, design and user behavior, a combination that was always at the core of information systems research.

Interactive marketing is another field which heavily draws on analytics. Real-time bidding (RTB) is a means by which display advertising is bought and sold on a per-impression basis via auction. With real-time bidding, advertising buyers bid on an impression and, if the bid is won, the buyer’s ad is instantly displayed on the publisher’s site. This is the fastest growing segment in the digital advertising market and it combines predictive models to better estimate the preferences and tastes of users and the bidding in a highly automated fashion. The topic addresses a wealth of problems ranging from distributed systems to auction theory, machine learning, and, last but not least, data privacy! Also, the Internet-of-Things has led to many new applications which require data analysis, distributed systems, and optimization to go hand in hand in ever growing applications. We have all seen case studies on smart mobility solutions, intelligent ports and transportation systems, or smart home solutions where sensors communicate and coordinate with humans in real-time, often with a substantial increase in economic efficiency. Consider for example condition-based maintenance making use of the analysis of sensors data: maintenance is performed when analytical techniques suggest that the system is going to fail or that performance is deteriorating.

• Present main idea(s) and best practices, on how to improve the capa­bilities of traditional Business Intelligence (BI) systems to permit superior moni­toring, measurement, and management of business perfor­mance.
• What is a reflection or critique of the themes presented in improving traditional business intelligence?
• What are ways to apply BI/Data science and Data analytics when asked by your employer to present a data analytics solution, how will you present the case?