Predictive Analytics

Most reporting is based on reviewing current and recent performance, but managers and boards also want to know what is likely to happen in the future in order to plan strategy or take corrective action as soon as possible. Analytics cannot predict all the external shocks to a system, but they can do a good job on anticipating future performance based on previous patterns of behaviour.

Predictive analytics extracts information from historic data and uses them to predict trends and behaviour patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the amount of previous data, the depth of data analysis and the quality of assumptions. We tend to use the R language to run linear and logistic regression and time series models. 

Typical assignments have been:

• Computing a predicted cost of social housing, based on an analysis of all the major registered housing providers using the latest Value for Money data

• Anticipating the final value of a core KPI at the end of the financial year