Cognitive data analysis ( CDA) automates and adds cognitive processes to data analysis so that the business user or data analyst can gain insights from advanced analytics. CDA is especially important in the age of big data, where the data is so complex, and includes both structured and unstructured data, that it is impossible to manually examine all possible combinations. As a cognitive computing system, CDA does not simply take over the entire process. Instead, CDA interacts with the user and learns from the interactions. This chapter reviews IBM Corporation's (IBM SPSS Modeler CRISP-DM guide, 2011) Cross Industry Standard Process for Data Mining ( CRISP-DM) as a precursor of CDA. Then, continuing to develop the ideas set forth in Shyr and Spisic's ({''}Automated data analysis for Big Data.{''} WIREs Comp Stats 6: 359-366, 2014), this chapter defines a new three-stage CDA process. Each stage (Data Preparation, Automated Modeling, and Application of Results) is discussed in detail. The Data Preparation stage alleviates or eliminates the data preparation burden from the user by including smart technologies such as natural language query and metadata discovery. This stage prepares the data for specific and appropriate analyses in the Automated Modeling stage, which performs descriptive as well as predictive analytics and presents the user with starting points and recommendations for exploration. Finally, the Application of Results stage considers the user's purpose, which may be to directly gain insights for smarter decisions and better business outcomes or to deploy the predictive models in an operational system.