ABSTRACT
The word “analytics” has been bandied about in sports circles with many examples used to describe routine data analyses. In other areas, such as criminology, banking and medicine, there is a better understanding of the distinction between data analysis and data analytics. The chapter uses a framework based on data quality, timescales of analyses, types of information processing, transparency of data processing and reporting methods to distinguish between data analytics and data analysis. The similarities and distinctions of how analytics and routine analyses are used in decision making are also discussed. One such distinction is the integrated use of different methods within analytics approaches. The chapter also gives introductory guidance on when analytics can be used beneficially; analytics is not always the answer and, in many situations, routine data analysis may be more effective. The rationale for integrating computerised data analysis tasks with decision making in a flexible and interactive process is developed while referring to example applications.
