Predictive Modelling

At A Glance – Predictive Modelling

Making the most of data & testing futures

By now, the importance of analysing data is clear. Without it, organisations, research teams and companies are essentially blindfolded. With quality data insights, it’s possible to understand why something has happened, as well as how it may happen again in future. But how does this actually work? By using predictive modelling, the process of testing and validating statistical models, data analysts can predict the probability of an outcome in any given industry. When used for commercial applications, predictive modelling is also called predictive analytics.

Predictive modelling works by collecting data from variables that could affect results. Once a model has been created and verified, its results can be presented as spreadsheets, score charts, tables, and scale diagrams called nomograms. Although the influx of Big Data could be seen to enhance predictive modelling, using more information doesn’t necessarily make the process more accurate. In fact, using small sections of a dataset (‘sampling’) can provide indicators of greater trends within the data in far less time.

While the machine learning techniques behind the process may be complicated, their merit is undeniable. In customer relations management (CRM), for example, predictive models can be used to work out why customers have acted in a certain way and how they may act in future. From a marketing perspective, this knowledge is vital. Outside of retail, the number of other applications are endless. Predictive modelling has been used to predict outcomes in fraud prevention, security management, change management, engineering, weather forecasting and city planning. In short, mapping the possible result of any action is gold dust for any organisation.