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My vote goes to "Linear Optimization." I think the biggest problem with "linear programming" is that you have to explain the name before you can explain the method. ------------------------------ R. Daniel Gimenez Applied Data Science Uruguay | Chile ------------------------------

A problem-formulation and decision-modeling environment. Tools change, but learning how to translate messy business questions into structured analytical or optimization models builds durable analytical thinking-and makes any future tool more effective. ------------------------------ R. Daniel Gimenez ...

Thanks, Bruce - model creep is a very helpful way to describe this. The timeline you mention resonates with what I've seen as well, where expectations and use cases evolve faster than the original operating design. Your point about simplicity and resilience across the entire modeling complex is especially ...

Thanks for pointing to the INFORMS Analytics Framework - it aligns well with what I've seen in applied settings. The distinction you highlight between starting from a business problem versus jumping straight to an analytics problem is particularly important. Many deployment and change-management issues ...

Over the last few years, we've seen rapid advances in machine learning and analytics models, yet many initiatives struggle to deliver sustained business value. In applied settings, the main bottlenecks often appear after model development: orchestration, data governance, monitoring, and alignment with ...

Hi everyone, My name is R. Daniel Gimenez. I graduated in Applied Data Science, have a prior background in Business Administration, and I am currently completing an MBA in Business Intelligence & Big Data. My interests lie at the intersection of analytics, data governance, and the operationalization ...