Taking a timely theme and looking at the way in which fraXses is helping organisations to move from the “rear mirror” view of static reporting, swiftly through the current ability to deliver real-time, streaming data analysis and into the predictive and prescriptive analytics.  

Descriptive analytics: means looking at historic data, ranging from 1 minute ago to years ago. It can be compared as looking in the rear mirror while driving.  Simply described as: “What has happened?”  Common examples of descriptive analytics are management reports providing information regarding sales, customers, operations, finance and to find correlations between the various variables.  Netflix for example uses descriptive analytics to find correlations among different movies that subscribers rent and to improve their recommendation engine they used historic sales and customer data.

Predictive analytics:  Predictive analytics provides organisations with actionable insights based on data. It provides an estimation regarding the likelihood of a future outcome. In order to do this, a variety of techniques are used, such as machine learning, data mining, modelling and game theory. Predictive analytics can for example help to identify any risks or opportunities in the future. “What could happen?”  An example of predictive analytics is forecasting the demand for a certain region or customer segment and to adjust production based on the forecast.  

Prescriptive analytics: uses a combination of many different techniques and tools such as mathematical sciences, business rule algorithms, machine learning and computational modelling techniques as well as many different data sets ranging from historical and transactional data to public and social data sets. Prescriptive analytics attempts to see what the effect of future decisions will be in order to adjust the decisions before they are actually made.  What should we do?”