As big data analytics (BDA) has increasingly influenced strategic decision-making, researchers and practitioners have continued debating on the roles of humans versus machines in making decisions. Our multiple-case analysis examines how BDA shapes decision makers’ adaptation of heuristics in response to different dimensions of environmental uncertainty (i.e., complexity versus dynamism). Contrary to prior literature that suggests BDA supplants human heuristics or impedes humans from adapting their heuristics, our findings underscore that BDA shapes the adaptation of heuristics through three distinct modes: alternative-reorienting, cue-patching, and relation-conditioning. Specifically, BDA shapes heuristics adaptation through the hybrid mode of cue-patching and relation-conditioning when environmental complexity is high, and through the alternative-reorienting mode when environmental dynamism is high. However, when environmental complexity and dynamism are both high, the uncertainty in environment may render BDA less effective, and substantial business acumen is required to adapt heuristics further. In addition, our findings reveal a pinning mechanism of BDA, that is, by keeping one component of human heuristics unchanged (e.g., original alternatives), a fixed point of comparison is created for evaluating the changes to other components of the heuristics. This study contributes to the literature by theorizing how BDA shapes heuristics adaptation and adds value to strategic decision-making in uncertain environments.