4 Reasons Why Data Scientists Should Add Mathematical Optimization to Their Analytics Toolbox
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.
With a full set of analytics tools including mathematical optimization, data scientists can maximize the business value of their data–by using it make accurate predictions and optimal decisions
Indeed, mathematical optimization is a powerful prescriptive analytics technology that should be included in every data scientist’s analytics toolbox. Read this new management paper to find out why.
Why Should You Use Optimization?
The Next Step for Enterprises: Optimization Transformation
In this article, Gurobi Technical Fellow and VP Dr. Gregory Glockner details how organizations are applying mathematical optimization, a powerful prescriptive analytics technology, to power digital transformation, decision optimization, and competitive advantage.
Mathematical optimization is a well-established, essential technological tool in the financial services industry. For over 50 years, mathematical optimization technologies have been used by leading companies across the financial services ecosystem (including institutional and consumer banks, wealth management firms, hedge funds, insurance providers, and fintech players) to:
- Address a wide variety of complex business problems including portfolio optimization, cash management, trade settlement, and asset-liability management.
- Make optimal, data-driven decisions that deliver improved operational efficiency, profitability, and regulatory compliance as well as reduced risk and costs.