Developing predictive models from historical data is a tough job. With so many parameters in hand, one needs to have in-depth domain knowledge regarding which parameters are supposed to be considered. Sometimes, a small linear model when created and applied on the given data set of a product provides stunning prediction results. One can make these predictions complex by adding so many parameters based on the given context. However, if you have to do all this manually means is a real tough job. What if data mining technology helps you to do this; means create models based on the data that you submit.
In other words, the predictive analytics create different models linked with different success rates and displays all the results in a dashboard [in an ascending or descending order based on their result positivity]. All you have to do is to test the model with another similar data set and validate the prediction. Hurray! It is that simple!
However, for adding a new parameter to the existing data, replacement of one set of parameters with another set, etc., requires manual intervention.
Predictive analytics create models based on the fed data; it cannot add new parameters on its own. As a consumer of predictive analytics models, in this scenario, your skill set lies in predicting the best model for your business data. In other words, you don’t have to create a model [predictive analytics create them for you based on the data that you feed it!] but you have to select the best one that reflects your business from the generated models.
Building models automatically can be done using predictive analytics but when it comes to integrating those models with your existing business seamlessly and makes them perform entirely depends up on you.
Are you game?