redictive analytics uses the data to predict what is likely to happen in the future based on historical data that can help drive strategic decisions (Richardson et al., 2023). Predictive analysis can be conducted manually or using machine-learning algorithms. Either way, historical data is used to make assumptions about the future. One of the methods under predictive analytics that the company can use is regression analysis, which can determine the relationship between two variables, often called single linear regression, or three or more variables, also called multiple regression (Richardson et al., 2023). The relationship between variables is written as a mathematical equation that can help predict the outcome should one variable change. An example of a situation where it would be appropriate to use predictive analytics is behavioral targeting in marketing. Consumer data is abundant and leveraged to create content, advertisements, and strategies to reach better potential customers where they are. You engage in predictive analytics by examining historical behavioral data and using it to predict what will happen in the future. Predictive analytics can be applied in marketing to forecast sales trends at various times of the year and plan campaigns accordingly. Predictive analytics can become unethical when it can cross the real privacy risk in light of the predictive project. For example, if the researcher targets pregnant women to send coupons, it delivers to the daughter of a father who does not want his daughter to get pregnant because this girl is still in school. Mismanaged access to this information can have huge implications on someone's life. This is the generation of new data of the indirect discovery of unvolunteered truths about people.