Which of the following is NOT a component of a predictive analytics model?

In the context of predictive analytics, the primary components generally involved in creating and implementing a model include model building, validation, and deployment. Model building refers to the process of developing the predictive model using historical data and various algorithms. Validation is crucial as it involves assessing the model's performance and ensuring that it accurately predicts outcomes on unseen data. Deployment is the final step where the model is implemented in a real-world setting to make predictions based on new incoming data.

While data analysis is an essential precursor to creating a predictive model, it is not itself categorized as a component of the modeling process. Instead, data analysis typically forms the groundwork for understanding the data and preparing it for model building, influencing the quality and effectiveness of the models that follow. Thus, it does not fit within the framework of model components in the same way as building, validating, and deploying a model do.