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This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Effective data security protocols and tools contribute to strong data integrity.
Data Quality Dimensions Data quality dimensions are the criteria that are used to evaluate and measure the quality of data. These include the following: Accuracy indicates how correctly data reflects the real-world entities or events it represents. Datafold is a tool focused on dataobservability and quality.
These practices are vital for maintaining data integrity, enabling collaboration, facilitating reproducibility, and supporting reliable and accurate machine learning model development and deployment. You can define expectations about data quality, track data drift, and monitor changes in data distributions over time.
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