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This guide offers an overview of how to identify anomalies in marketing research or business data and how to strive to make data free of them. Data is integral to any research, and anomalies, including missing values, inconsistent responses, straight-line responses, and outlier responses, always find their way into datasets. Data anomalies occur for multiple reasons, such as improper research design or respondent disengagement. By freeing data of anomalies, we “clean data” and improve our research output. Every researcher should employ systematic methods to clean their data, including pairwise and listwise deletion, and ceiling and flooring, before beginning data analysis. By learning how to clean data, researchers learn not just how to prepare data for the analysis stage but also how to improve their research designs.