Collecting and storing information is one primary function for advancement operations. But the next steps towards sound computational philanthropy practices would be making sense of the ocean of knowledge and turning that into actionable insights. For this extension of our introductory session, we will explore some good practices in data organization from the perspective of building in-house computational philanthropy products such as predictive model and other types of scores. We will also explore some basic to more complex practices for predictive modeling, which are foundational to understanding machine learning and AI.