ABSTRACT
In order to obtain accurate and quick analytical results, there should be a structured approach to perform analytics so that they can be scaled to get repeatable and consistent results. This chapter discusses the importance of such a process to ensure that both data quality (DQ) and analytics quality levels are met in order to enable one to perform high-quality analytics and make appropriate decisions. It also discusses the importance and various aspects of purposeful analytics, including why the concept is so important in the data-driven world to achieve robust quality in analytics and in decision-making activities. The chapter explains how to measure the robust quality of analytics execution by using the analytics robust quality index (RQI), which takes into account both analytics quality and DQ aspects. It also evaluates the effectiveness of using analytical models including artificial intelligence and machine learning techniques and the investments made into them.
