This blog post focuses on the first phase of our Data Science Process: Question Formation and Data Analysis. In this phase, we iterate multiple times through question formation, data collection, and exploration. Initial questions are likely to be of low fidelity. Through the process of data exploration, the questions gain fidelity and drive toward business value.

This is an introduction to a series of blog posts describing the process of creating and operating data models in support of your AI/Machine Learning (ML) programs. It is structured to ensure that you can deliver actual business value.