Predictive analytics is one of many branches of analytics, which is widely used for business intelligence aids the managers in the decision making process. It is used to make prediction about unknown future events. The technique uses various tools to give prediction, some of which include data mining, statistical analysis, data modelling, machine learning and artificial intelligence. The main purpose of predictive analysis is to use the historic or current data, and make prediction about unknown future trends and events. In order to make prediction about the future events, a number of data mining tools are used, which enable integration of information technology and other business modelling tools. Predictive analysis has the ability to identify and derive meaning out patterns in historic and transactional data. Additionally, this technique captures relationships among various factors at play. Big data interpretation is possible as risk can be assess under a particular set of conditions.
Predictive analysis allows businesses to behave in a more pro-active manner since decision can be made based on anticipated outcomes, since more information is available to make an informed decision. If predictive analysis is effectively used it will certainly reduce the level of risk since the decision are no longer based on hunches or assumptions.
Predictive Analytics Process
The first step involves defining the project, and clearly list down the desired outcomes that the business wants to retrieve from the analysis. The business also needs to identify the sets of data that will be used for the analysis. The next step involves collecting all the relevant data from various sources. Once the data has been complied, data analysis is undertaken, which involves processes such as inspecting, cleaning, transforming and data modelling in order to derive meaning and the desired output. Statistical algorithms also be used to make the representation and understanding of the output easier. Once accurate predictive models are created, the process of deployment can be initiated, which means that day-to-day decision making can be done based on these models.
Application
Predictive analytics is widely used in healthcare operations. It is used to predict what other condition patients are at risk of developing over the course of treatment. The technique is also used by financial institutions to assess the level of risk they could end up exposing them to, if they go-ahead with a particular investment. This technique has also helped thousands of retails cross-sell their products and services by analyzing consumer behavior, trends and customer’s past purchases.
