ABSTRACT
Since the emergence of business intelligence in decision-making, efforts to automate decision-making capabilities relating to data preparation, data analysis, and data visualization have continued with varying degrees of success. Much of these activities have been independently performed, lacking an architecture for integrated development and implementation. Such decision-making environment, besides remaining largely manual, is also prone to individual biases. As the need to process data volumes is increasing exponentially and sourced cross-functionally, decision-making is increasing in complexity. Further, as the dimensionality of data is increasing owing to the number of variables driving an outcome or best action, it is becoming either impossible or impractical to gain valuable insights in decision-making using existing analytics approaches. This has led to biased, inferior, and untimely decision-making. The primary objective of this open research challenge is to explore the promising area of Augmented Analytics in big data that has the promise to harness features and capabilities of both Machine Learning and Natural Language Processing to offer an integrated toolbox that enables executing search-based analytics and conversational analytics in unison. In this chapter, a framework and methodology for Augmented Analytics is described that has its roots in machine learning and natural language processing fields of research. It unifies capabilities of machine language algorithms and natural language processing to offer a new and innovative paradigm of augmented analytics in decision-making.
