Ensuring Reliable Freight Data Quality

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This document outlines the importance of reliable freight data quality, emphasizing that correct formulas cannot compensate for incomplete data. It discusses five key dimensions of data quality: completeness, accuracy, consistency, representativeness, and traceability. Practical assessment methods are introduced, including the Emissions Data Quality & Gap Analyzer, which evaluates records using a consistent rating scale. Additionally, it highlights the need for evidence and controls, focusing...