ABSTRACT
This chapter describes how the author can leverage both of the disciplines to meet expectations and new demands. It offers guidance for navigating the issues in the form of key questions and lessons learned. The chapter distinguishes between truly semantic technologies—which have the capabilities—and stochastic technologies—which do not have the capabilities. It applies the quantitative analytical tools to text or language without first developing a deep understanding of the linguistics or the knowledge structures inherent to the text. The chapter encounters the risks in working with analytics include risks of project failure and the risk of a failed or poor business decision. It examines a project whether the goal is a one-time effort or an on-going, enterprise-level operation. It improves the effectiveness of data and text analytics by leveraging knowledge management methods such as knowledge modeling, knowledge representation, and knowledge engineering. The chapter discusses to think more expansively and creatively in defining research agendas and setting business goals.
