The central idea of this research is to overcome the limitations of traditional mathematical optimization methods in tapping into the potential of data to produce high-quality optimization guarantees. This shortcoming will be addressed in this research by proposing data-dependent optimality guarantees that outperform their traditional counterparts. The emergence of such guarantees can be a game-changer, enabling confident decision-making, and machines that know how reliable their inferences are.