Advanced Analytics – Issues and Challenges in the Global Environment

Modern businesses require a suite of appropriate analytical tools – both quantitative and qualitative – that go beyond data mining, because of issues with scalability, parallelizability, and numeric vs. symbolic representation that may well affect analytic utility and the results of an analytic. However, there is limited formal or structured guidance for new and complex problem spaces providing criteria for what analytics to use and how they are to be cascaded or integrated to obtain useful results and generate a range of alternate explanations of what is happening now, what is likely to happen soon, and what could happen in the long term. In this paper, we describe sixteen classes of analytics in which we extend previous work by Kaisler and Cioffi-Revilla [7]. We examine some issues and implementation challenges for analytics in the global business environment. We suggest several applications of these analytics to modern business problems and draw several conclusions that lead to further research.