ABSTRACT
Analyticity is the ability for continuous iterative exploration and investigation of past performance, based on data and statistical methods, to gain insight and drive planning for the future. Analytics can be used for improving performance, driving sustainable growth through innovation, speeding up response time to market and environmental changes, and anticipating and planning for change while managing and balancing risk. These benefits are achieved through a framework that deploys automated data analysis within the business context. The paradigm shift is from intuition-driven decision making to data-driven, computer-assisted decision making that takes advantage of large amounts of data or data from multiple sources. The chapter describes the various kinds of analytics like descriptive, predictive and prescriptive analytics. The chapter then provides an overview of the data science and related techniques. This chapter’s appendix describes aspects related to analyticity: data mining aims to extract knowledge and insight through the analysis of large amounts of data using sophisticated modeling techniques; it converts data into knowledge and actionable information. Data mining models consist of a set of rules, equations, or complex functions that can be used to identify useful data patterns, understand, and predict behaviors. Data mining is a process that uses a variety of data analysis methods to discover the unknown, unexpected, interesting, and relevant patterns and relationships in data that may be used to make valid and accurate predictions. In general, there are two methods of data analysis: supervised and unsupervised.
