Before this latest wave of AI and machine learning interest and hype, organizations that had data-centric project needs also looked for methodologies that suited their goals. Emerging from roots in data mining and data analytics, some of these methodologies had at its core an iterative cycle focused on data discovery, preparation, modeling, evaluation, and delivery. One of the earliest of these developed is simply known as Knowledge Discovery in Databases (KDD). However, just like waterfall methodologies, KDD is in some ways too rigid or abstract to deal with continuously evolving models.
Cofactor is a large, structured listing of people, places, and things. Here you can find the description of each topic.