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Data verification

Data verification

Data verification definition

Data verification refers to the process of ensuring that data is accurate, complete, and consistent. It is a critical step in data quality management and involves comparing data against a known and trusted source to check for errors and inconsistencies.

See also: file hash, integrity checking

Methods of data verification:

  • Double entry involves entering data twice and comparing the two entries for consistency. This method is useful in identifying errors such as typos or incorrect data entry.
  • Sampling is useful in identifying patterns and trends in the data and involves selecting a representative sample of data and verifying its accuracy.
  • Field checks include verifying the accuracy of data entered into specific fields or columns.
  • Cross-referencing compares data from multiple sources to ensure consistency and accuracy.

Data verification challenges:

  • Limited scope. It may only verify a sample of data or specific fields, which means that errors in other parts of the data set may go undetected.
  • Human error. Since it involves human judgment, the verification process can lead to errors or biases.
  • Time. It can be a time-consuming process, especially for large data sets.
  • Cost. It may require specialized tools or expertise, which can be expensive to implement.
  • Outdated data. It has to be repeated because the data changes or can become outdated.

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