Integrity
In the context of data science, integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle. This includes the processes of data collection, storage, processing, and analysis. Maintaining data integrity is crucial for ensuring that the insights and decisions derived from the data are trustworthy and valid. Data integrity can be compromised by various factors such as human error, system failures, or malicious attacks. Therefore, data scientists must implement appropriate measures to ensure data integrity, such as data validation, data cleansing, and data encryption. Additionally, data integrity is closely related to data quality, which refers to the overall fitness of data for its intended use.
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