Data-driven decisions are only as reliable as the quality of the data behind them. When organizations rely on inaccurate, inconsistent, incomplete, invalid, outdated, or duplicate data, outcomes can miss their mark and create operational, financial, or compliance risks. Data quality is measured across six key dimensions: accuracy, timeliness, consistency, completeness, validity, and uniqueness. Each plays a critical role in shaping trustworthy insights. Poor data quality can lead to flawed decisions across industries, demonstrating why maintaining high-quality data is essential for effective and reliable business outcomes.

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