Grade the Data Before You Trust the ETA
Assess whether input data is fit for AI ETA decisions before acting on predictions.
The move: inspect the data decision boundary before you inspect the model score. Transportation data is assembled from telematics, TMS events, carrier updates, warehouse scans, weather feeds, appointment calendars, and customer promises. The AI output may be a single ETA, but the evidence behind it is a chain. Data-quality review asks whether that chain is accurate, complete, timely, valid, consistent, and unique enough for the decision at hand. Timeliness Can the operation still act on this prediction, or is the input lag longer than the decision window? Completeness Are the missing fields concentrated in a lane, carrier, facility, or shipment…
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