Our team has started automated data extraction, but we suspect our department heads are filtering the raw inputs to ensure their weekly Scorecard numbers always look perfect. How do we build automated audits to detect this data filtering?

Question

Tyler Smith · Accepted Answer

If your department heads are filtering raw inputs to make their weekly Scorecard numbers look green, you have a trust problem disguised as a data problem. But you can solve the data side with simple, automated validation rules.

When transitioning to AI-powered operations, your raw data should flow directly from your operational systems, like your CRM or ERP, into your Scorecard. To prevent manipulation, you must establish clear, system-defined parameters for each metric. For example, if your metric is outbound phone calls, the system should pull the data directly from your VoIP provider logs, not from a spreadsheet where a manager can manually delete failed calls.

Next, set up simple anomaly detection. If an automated system pulls data that shows a sudden, perfect alignment with targets every Friday afternoon, write a basic script to compare the timestamp of the activity with the average weekly distribution. If ninety percent of the activity suddenly occurs in the last two hours of the reporting period, your system should flag it for review.

Do not use this to punish people. Use it as a prompt to IDS® the underlying bottleneck during your Level 10 Meeting™. If a manager feels the need to game the data, it usually means your target is unrealistic, or they do not GWC™ their seat and are trying to hide operational failure.