
Billions of dollars have been invested in the rise of data science and machine learning as mainstream disciplines in the world of business, one of the most exciting tech trends of the last (and next) decade.
In the enterprise, many of the applications of data science and machine learning ultimately produce a prediction: which customers are the most likely to buy? Or churn? Which transactions are most likely to be fraudulent? What part of town is likely to place the most food deliveries tomorrow afternoon?
However, powerful though it may be, there is one thing machine learning generally doesn’t tell you: once you have a prediction, what do you do with it? For example, once you have predicted high demand for food delivery in a certain part of town, how do you decide which delivery team member to dispatch where and when, to optimize for efficiency and maximize revenue and customer satisfaction?
Enter decision science. While the term has not crossed over to mainstream consciousness like its data science cousin, decision science has been around for decades. Also often known as Operations Research, it encompasses a variety of advanced analytical methods and quantitative models to help with decision-making and efficiency, including simulation, mathematical optimization, queuing theory, etc.
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