What is meant by Data check?
"Data validation" refers to the process of checking data for accuracy, consistency, completeness, and correctness. This process ensures that the data captured, processed, or stored within a system meets required standards and is suitable for its intended use. Data validation is a crucial part of data quality assurance, helping to identify and correct errors before data is used for analysis or decision-making.
Typical software functions in the area of "Data Validation":
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Input Data Validation:
- Checking input data against predefined rules or formats (e.g., data format, range checks).
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Consistency Checks:
- Ensuring that data is consistent by cross-referencing with other data sources or fields.
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Error Detection and Correction:
- Identifying and correcting errors or inconsistencies in the data, such as duplicate entries or incomplete records.
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Data Cleaning:
- Automated or manual removal of erroneous or outdated information from the data.
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Integrity Checks:
- Verifying data integrity to ensure that data remains in its original form and unaltered.
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Rule-Based Checks:
- Applying predefined rules and guidelines to verify data quality, such as minimum and maximum values, logical relationships.
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Reporting:
- Generating reports on the results of data validation, including detected errors and corrective actions.
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Logging and Traceability:
- Recording validation and correction processes for future reference and audits.