Integrating predictive analytics has already proven its worth in a wide range of industries. In addition to high-tech scientific companies, the health care industry also uses this method for predicting the progression of diseases. A prominent area of application is also the energy sector, where the intelligent power grid of the future is known as the 'smart grid'. In this case, power consumption can be predicted using stored customer behavioral patterns (smart customer data) in order to precisely regulate the required supply of wind and hydroelectric power.
So-called predictive maintenance can be used as an additional example. In this process, the current data is fed into a constantly running machine to predict future use and the resulting wear. Weak spots within the production chain can be identified and rectified quickly in order to prevent a loss in production.
The best way to use predictive analytics is to use a wide range of data packets that are as extensive and pure as possible. All data packets are then integrated into the analysis. The more data is available (and from as many areas as possible) the more precise the result will be. Most companies are turning to synergistic effects by adding predictive analytics to their existing business intelligence structure. The most popular tools for using predictive analytics include:
- Alpine Data Labs
- Alteryx
- Angoss Knowledge STUDIO
- BIRT Analytics
- IBM SPSS Statistics and IBM SPSS Modeler
- KXEN Modeler
- Mathematica
- MATLAB
Prescriptive analytics can be defined as the next step in data analysis. This method is where predictive analytics reaches its obvious limit: using information to predict the way things will develop in order to steer the future course of a trend. In other words, envisaged scenarios are easier to implement and at a certain step in the development, action can be taken to navigate trends in a different direction. This approach is made possible by analytical structures based on complex models and random MC simulations. Just like with predictive analytics, the more comprehensive and reliable the variables used to draw the desired data, the more accurate and informative the results will be.