I love predictive analytics. I mean, who wouldn't want to develop an application that could help you make smart business decisions, sell more stuff, make customers happy, and avert disasters. Predictive analytics can do all that, but it is not easy. In fact, it can range from being impossible to hard depending on:
Causative data. The lifeblood of predictive analytics is data. Data can come from internal systems such as customer transactions or manufacturing defect data. It is often appropriate to include data from external sources such as industry market data, social networks, or statistics. Contrary to popular technology beliefs, it does not always need to be big data. It is far more important that the data contain variables that can be used to predict an effect. Having said that, the more data you have, the better chance you have of finding cause and effect. Big data no guarantee of success.