Data Quality Evaluation

Definition ∞ Data Quality Evaluation is the systematic process of assessing the accuracy, completeness, consistency, timeliness, and validity of data sets. This evaluation determines if data meets specified standards and is fit for its intended use, such as informing financial decisions or training machine learning models. It involves employing various metrics and techniques to identify errors, redundancies, or omissions. High-quality data is essential for reliable analysis and decision-making.
Context ∞ For digital assets and blockchain applications, data quality evaluation is critical, especially concerning oracles that feed external information onto the chain. The integrity of decentralized finance (DeFi) protocols and smart contracts relies heavily on the accuracy and reliability of the data they consume. Flawed data can lead to incorrect contract executions, financial exploits, or market manipulation. Rigorous evaluation ensures the trustworthiness of on-chain operations.