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Data Quality

Data quality refers to the fitness of data for its intended use across dimensions including accuracy, completeness, consistency, timeliness, and validity. High-quality data accurately represents the real-world entities it describes, is complete with no critical gaps, is consistent across different systems and representations, and is sufficiently current for the decisions it supports. Data quality is not an absolute standard — it is defined relative to the specific purposes for which data will be used.

Why This Matters

Poor data quality is the most common root cause of unreliable management reporting. When source data is inaccurate, incomplete, or inconsistently defined, no amount of reporting infrastructure investment can produce trustworthy outputs — garbage in, garbage out. Maintaining high data quality requires continuous attention: proactive validation controls, clear ownership for quality issues, and regular data quality assessments to detect and correct problems before they propagate into management reports.

Where This Fits

This term sits within the Data Governance & AI Readiness area of Performance & Control.

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