Of course, analyzing data without knowledge of the field is not a serious way to proceed. This is okay to show how some algorithms work, how to make use of them, and to exercise. However, for real-life applications, be sure that you know the topic well, or else consult experts for help. The Cross Industry Standard Process for Data Mining (CRISP-DM, Shearer, 2000) underlines the importance of field knowledge. The steps of the process are depicted as follows:

The Cross Industry Standard Process for Data Mining

As stressed upon in the preceding diagram, field knowledge (here called Business Understanding) informs and is informed by data understanding. The understanding of the data then informs how the data has to be prepared. The next step is data modeling, which can also lead to further data preparation. Data models have to be evaluated, and this evaluation can be informed by field knowledge (this is also stressed upon in the diagram), which is also updated through the data mining process. Finally, if the evaluation is satisfactory, the models are deployed for prediction. This book will focus on the data modeling and evaluation stages.