When an AI initiative faces risk due to potential inaccuracies indata aggregation, PMI-CPMAI–aligned practice says the very first action is tounderstand the data characteristicsbefore taking any corrective measures. This includes clarifying data sources, aggregation logic, granularity, formats, lineage, and quality dimensions (completeness, consistency, accuracy, timeliness, and validity). By doing so, the project manager and data team can determinewhereandwhyaggregation errors are arising, and whether they stem from upstream systems, ETL/ELT pipelines, joining logic, or business rules.
PMI’s AI data lifecycle guidance stresses that you cannot reliably “fix” freshness, delete records, or visualize results until you have a structured understanding of the data landscape and its transformation steps. Jumping to deletion (option B) can worsen bias or information loss, and focusing only on freshness (option A) or visualization (option D) treats symptoms rather than root cause.
Therefore, the correctfirststep in mitigating this type of risk is tounderstand the data characteristics(option C), which then informs targeted remediation actions, improved aggregation logic, and robust data quality controls aligned with the AI solution’s objectives and risk appetite.
