Why do we use modeling for predictive analytics?

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Why do we use modeling for predictive analytics?

Real-world problems affect businesses, big and small due to the uncertainty and complexity of the environment. The need for companies and organizations to solve these problems, safely and efficiently have always been relevant and a huge challenge. However, computational modeling through simulation has somehow made this challenge easier to apprehend. It provides an important method of analysis through simulating real world scenarios, which are easily verifiable and simple to communicate and understand. Across industries and disciplines, simulation modeling provides valuable solutions by giving clear insights into complex systems.
Simulation modeling provides a safe way to test and explore different “what-if” scenarios in our attempt to mitigate risks. This scenario testing allows us the opportunity to make wrong decisions when need be just to test worse case scenarios while planning for best case scenarios, in a safe virtual environment as we plan for the real-world. This is important, especially when the economy can be volatile and unpredictable as well as competition becoming more fierce. We aim to make the right decisions in simulations before making real-world changes. It is much more cost effective to run tests than making mistakes in the real-world.

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