Global stakeholders have expressed interest in increasing the use of data analytics throughout the audit process. While data analytics offer great promise in identifying audit-relevant information, auditors may not uniformly incorporate this information into their decision making. This study examines whether conclusions from two data analytic inputs, the type of data analytical model (anomaly vs. predictive) and type of data analyzed (financial vs. nonfinancial), result in different auditors' decisions. Findings suggest that conclusions from data analytical models and data analyzed jointly impact budgeted audit hours. Specifically, when financial data is analyzed auditors increase budgeted audit hours more when predictive models are used than when anomaly models are used. The opposite occurs when nonfinancial data is analyzed, auditors increase budgeted audit hours more when anomaly models are used compared to predictive models. These findings provide initial evidence that data analytics with different inputs do not uniformly impact auditors' judgments.

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