Many data scientists claim that around80% of their time is spent on data preprocessing, and for good reasons, as collecting, annotating, and formatting data are crucial tasks in machine learning. This article will help you understand the importance of these tasks, as well as learn methods and tips from other researchers. Below, we will highlight academic papers from reputable universities and research teams on various training data topics. The topics include the importance of human annotators, how to create large datasets in a relatively short time, ways to securely handle training data that may include private information, and more. This paper presents a firsthand account of how annotator quality can greatly affect your training data, and in turn, the accuracy of your model. In this sentiment classification project, researchers from the Jožef Stefan Institute analyze a large dataset of sentiment-annotated tweets in multiple languages. Interestingly, the findings of the project state that there was no statistically major difference between the performance of the top classification models. Instead, the quality of the human annotators was the larger factor that determined the accuracy of the model.