Data equity is an emerging framework for responsible data science. However, its core concepts, including fairness, representativeness, and information bias, remain largely abstract and general, lacking the mathematical specificity needed for practical implementation. In this paper, we demonstrate how statisticians can operationalize data equity by translating its tenets into precise, testable formulations tailored to a given problem. Using the well-documented case of differential measurement error across racial groups in pulse oximetry, we first adopt an oracle approach, tracing how a single upstream violation of information bias compounds through the analytic pipeline into treatment disparities, fairness violations, and adverse health outcomes. We then demonstrate the inverse: starting from an observed outcome disparity, the data equity framework provides a principled structure for systematically identifying its statistical sources. Our exposition reveals that data equity, prediction equity, and decision equity are distinct requirements with distinct evaluation and policy needs--a nuance that highlights both the unique role of statisticians in the era of artificial intelligence as well as the necessity of interdisciplinary collaboration.

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