By Kurt Stockinger (ZHAW)
As part of “Zürich meets San Francisco – A Festival Of Two Cities”, the ZHAW Datalab co-organized the event Data Science and Beyond: Technical, Economic and Societal Challenges, which took place at the campus of San José State University (SJSU) – in the heart of Silicon Valley. One interesting fact about SJSU is that it has the highest number of graduates among all US universities that get jobs either at Apple or Cisco.
By Kurt Stockinger (ZHAW)
The final results of an interdisciplinary study funded by „TA Swiss“ on „Quantified Self“ with participation of the Datalab have been published. The study was performed by three ZHAW departments (School of Health Professions, School of Management and Law, School of Engineering) in cooperation with the Institute for Futures Studies and Technology Assessment, Berlin. The focus of the Datalab was on legal and Big Data aspects of quantified self.
The results are available in various forms:
Enjoy reading and maybe you get encouraged to “quantify yourself” a bit better 😉
As part of a recent project called Big Data Query Processing we have evaluated complex query workloads using modern Big Data systems. In particular, we have performed benchmarks of Cloudera Impala using a business intelligence use case provided by an industry partner. The results can be found on the following blog post hosted by Cloudera:
In business, especially marketing, it is often necessary to perform customer selection. The typical task involves defining a set of customers for upselling, cross-selling or retention actions. Traditionally, the selection criterion is the positive response probability. While this identifies customers who are the most likely to respond, it does not necessarily provide the optimal solution from which the business profits the most.
Over the last years, the concept of customer lifetime value (CLV) has attracted increasing attention. It suggests rating and selecting the customers by the present value of all future revenues that are attributed to their relationship. Hence, the focus lies on customer quality, rather than customer quantity. While this may sound logical, it provides a huge analytical challenge: the CLV is driven by the future behavior of a customer, which of course is not known in advance. Predictive analytics can and needs to be used for modeling customer dynamics, and often big data is involved.
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