Abstract
It is always good to follow a strategic roadmap. We motivate Data Analytics roadmaps by first developing a business scenario and introducing associated modelling. Here, formal definitions for real and prediction models are provided. These are of foremost importance. We will use them to derive one of the most commonly used measures - the mean squared error (MSE) - to evaluate the quality of a data mining model. Several business applications are mentioned to give an idea of which kind of projects can be built around a simple linear model. The models and quality measures provide us with a solid foundation for the frameworks. This chapter introduces the methodology of knowledge discovery in databases (KDD), which identifies essential steps in the Data Analytics life-cycle process. We discuss KDD with the help of some examples. The different stages of KDD are introduced, such as data preprocessing and data modelling. We explore the cross-industry standard process for data mining (CRISP-DM).