This chapter responds to the growing importance of business analytics on "big data" in managerial decision-making, by providing a comprehensive primer on analyzing compensation data. All aspects of compensation analytics are covered, starting with data acquisition, types of data, and formulation of a business question that can be informed by data analysis. A detailed, hands-on treatment of data cleaning is provided, equipping readers to prepare data for analysis by detecting and fixing data problems. Descriptive statistics are reviewed, and their utility in data cleaning explicated. Graphical methods are used in examples to detect and trim outliers. The basics of linear regression analysis are covered, with an emphasis on application and interpreting results in the context of the business question(s) posed. One section covers the question of whether or not the pay measure (as a dependent variable) should be transformed via a logarithm, and the implications of that choice for interpreting the results are explained. Precision of regression estimates is covered via an intuitive, non-technical treatment of standard errors. An appendix covers nonlinear relationships among variables.