4 Reasons Why Data Scientists Should Add Mathematical Optimization to Their Analytics Toolbox

It has been said that “data is the new oil” — and it’s true that data is indeed a valuable commodity in today’s business world. Most companies have the capability to collect and process huge quantities of data, but few are actually able to utilize their data to generate insights and predictions and make decisions.

To extract the maximum business value out of their data (by leveraging it to make optimal decisions), companies must have the right advanced analytics technologies.

Today’s data scientists need to have a full analytics toolbox at their disposal. But which tools do they actually need?

In addition to machine learning, visualization, heuristics, and other common tools, mathematical optimization is becoming an essential technology for more and more data scientists.

Indeed, mathematical optimization is a powerful AI technology that should be included in every data scientist’s analytics toolbox. Read this new management paper to find out why.