ABSTRACT
Reporting all available data has little value because it overwhelms the organisation with information that is not actionable. A more productive approach to processing the data deluge is to show those processes where something interesting has occurred. A range of techniques is available to detect anomalies in the data. Reporting outliers and anomalies focuses the organisation's attention by raising questions and motivating action. This chapter discusses finding the most exciting points in your data to create actionable reports. This chapter also shows how to streamline code with functions and develop a leak detection tool to use with digital metering data. The learning objectives for this session are:
Apply statistical methods to detect outliers
Find anomalies in a time series
Develop R functions to streamline your code
Write a function to detect leaks from digital metering data
