PYMNTS.com January 26, 2026
For decades, the prevailing view in artificial intelligence (AI) and analytics has been “more data is better.” Larger datasets are often associated with improved model accuracy and stronger performance across unpredictable scenarios. This assumption has driven enterprises to invest heavily in data acquisition, and the computing power required to process ever-expanding volumes of information.
Rethinking Data
MIT researchers asked a different question: What is the minimum amount of data required to guarantee an optimal decision? Their work focuses on structured decision-making problems under uncertainty, where outcomes depend on unknown parameters such as costs, demand or risk factors. Instead of treating data as something to be maximized, the researchers treat it as something that can be mathematically bounded.
The framework characterizes...
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