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Descriptive analytics is used to better understand changes that have taken place within an organization, with the help of data. It describes these changes by uncovering relationships between different variables. Datacation helps organizations to graphically display these relationships and patterns with the help of business intelligence programs.
Subsequently, the question can be asked why these events took place in order to better understand the effects. This way, challenges and relationships between different aspects of an organization become more transparent, leading to more informed decisions. We apply descriptive analytics in various ways. Some examples of applications are:
Predictive analytics focuses, as the name suggests, on predicting situations in the future. It investigates patterns in data to identify risks and opportunities. This way, different analyses can be performed using different techniques. In general, we distinguish between two types of techniques: Regression Techniques and Machine Learning techniques.
Considering Regression Techniques, a mathematical equation is developed representing the interaction between different variables. By means of this equation, the interaction between these variables in the future is predicted.
In view of Machine Learning techniques, algorithms are designed that improve themselves through repetition. By automatically adding new data, the algorithm learns directly from its (in)correct predictions. This way, the algorithm is getting better at recognizing patterns in large databases. Thereafter, by using these patterns, it is able to make better predictions about new data. We apply predictive analytics in the following ways: