A new method called Data-driven Stochastic Robust Optimization (DDSRO) has been developed to help make decisions when there is uncertainty in big datasets. By using machine learning techniques, the method can handle different types of uncertainty in the data. The approach involves solving two optimization problems to find the best solution that works well under different conditions. This method has been tested on process network design and planning, showing that it can be useful in real-world situations.