Predictive analytics is about the future
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.
Predictive analytics can be used in all departments, from predicting customer behaviour in sales and marketing, to forecasting demand for operations or determining risk profiles for finance. A very well-know application of predictive analytics is credit scoring used by financial services to determine the likelihood of customers making future credit payments on time. Determining such a risk profile requires a vast amount of data, including pubic and social data.
Another example of predictive analytics is forecasting the demand for a certain region or customer segment and to adjust production based on the forecast. This is quite a common analysis and it takes into account many different data sets, from open, weather, data for example, to sales data and social media data.
Historical and transactional data are used to identify patterns and statistical models and algorithms are used to capture relationships in various data sets. Predictive analytics has really taken of in the big data era and there are many tools available for organisations to predict future outcomes. With predictive analytics it is important to have as much data as possible. More data means better predictions.