It's true that organizations are getting more out of their data than ever before (as investing more into their data, too), empowering data science and business intelligence teams to explore, visualize, and gain insights into unprecedented amounts of data. Two other things are also true: More is being expected from such teams to differentiate themselves from their competition and that all too often these analytics teams end their projects too soon – failing to consider the decisions their analyses inform. That’s where Decision Analytics comes in, and particularly, Mathematical Optimization.
March 4, 2022
Date: Mar 23, 2022
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It's true that organizations are getting more out of their data than ever before (as investing more into their data, too), empowering data science and business intelligence teams to explore, visualize, and gain insights into unprecedented amounts of data. Two other things are also true: More is being expected from such teams to differentiate themselves from their competition and that all too often these analytics teams end their projects too soon – failing to consider the decisions their analyses inform. That’s where Decision Analytics comes in, and particularly, Mathematical Optimization.
Key topics include:
What are Decision Analytics, really?
What makes a good decision and what are some of the key concepts used in decision making?
What are some examples and common approaches used in Decision Analytics?
With today’s decisions becoming more and more complex, what are approaches are best suited for these problems and what are the associated advantages and disadvantages?
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