In this chapter, we went through a step-by-step process from data to a holistic view of business, from which we processed a large amount of data on Spark and then built a model to produce a holistic view of the sales team's success for the IFS company.

Specifically, we first selected models as per business needs after we prepared Spark computing and loaded in preprocessed data. Second, we prepared and reduced features. Third, we estimated model coefficients. Fourth, we evaluated the estimated models. Then, we interpreted the analytical results. And finally, we deployed our estimated models.

The preceding process is similar to the process of working with small data. However, in dealing with big data, we need parallel computing, for which Apache Spark is utilized. Also, during the previously described process, Apache Spark makes things easy and fast.

After this chapter, readers will have gained a full understanding of how Apache Spark can be utilized to make our work easier and faster in obtaining a holistic view of business. At the same time, readers should become familiar with the RM4Es modeling processes of processing large amounts of data and developing predictive models and especially become capable of producing their own holistic view of business.