Data imputation holds significant importance in a variety of fields including risk management. Incomplete or missing data can hinder a thorough analysis of risks, making accurate decision-making challenging. By employing imputation techniques to fill in the gaps, risk managers can obtain a more comprehensive and reliable understanding of the underlying risk factors. This, in turn, enables them to make informed decisions and develop effective strategies for risk mitigation. This note introduces the concept Bayesian data imputation. We collect and provide backgrounds needed for Bayesian data imputation when missing data are missing at random. Numerical examples are provided for demonstration.