In the business world, there are many factors to consider when making the optimal decision. There are so many data points to consider that it becomes a combinatorial problem. For example, consider when and how to raise room rates across a hotel chain based on locations and current events or how best to optimize airline ticket prices given fluctuating fuel costs, factoring in seasonal conditions and local and/or global events. This flows over into our social and personal lives, as we rightly expect to find the nearest coffee shops located to the nearest public libraries or where to buy the cheapest gas closest to the supermarket that stocks the groceries we need. Decision optimization (DO) is the prescriptive element of the data science lifecycle and is key to delivering artificial intelligence, as machine learning (ML) and DO have somewhat of a symbiotic relationship.