Automatically remove all data points that fall outside a specified range or threshold without further analysis, to ensure scalability and compliance.

Manually review each outlier in the dataset to determine its validity, despite the time and resource constraints this may impose.

Use a combination of automated tools for initial outlier detection and manual review for validation, considering the dataset's distribution, the context of the data, and potential causes of outliers, to balance accuracy with scalability and compliance.

Proceed with the analysis ignoring the outliers, assuming they have minimal impact on the overall results, to meet project deadlines.