Avoiding errors through completeness checks

Avoiding errors through completeness checks

As more companies move towards digitization, the importance of accuracy in data handling is increasingly critical. Minor errors can lead to significant financial losses, negatively impact credibility and make it difficult to identify and correct errors. One effective strategy for detecting errors in data entry is through completeness checks.

What are completeness checks?

A completeness check is the process of verifying the presence and correctness of all required fields in a given dataset. It helps to ensure that all essential information is captured accurately. Completeness checks can also identify any discrepancies and missing data that may lead to errors.

Completeness checks can be performed at various stages of data collection, from data entry to post-processing. They can be manual or automated, depending on the complexity of the data. Manual checks are suitable for smaller datasets, while automated checks are useful for large datasets.

Why are completeness checks important?

Completeness checks are a critical aspect of data verification. It helps to identify errors and missing data that could compromise the quality of the data. The following are some reasons why completing checks are essential for data verification:

  • Accuracy: Completeness checks verify that all data entered is accurate and up-to-date.
  • Efficiency: Data is verified quickly and easily, reducing the time, effort and resources needed to correct errors.
  • Consistency: Completeness checks ensure that all data is collected using the same guidelines and formats ensuring consistency.
  • Financial savings: The cost of correcting errors is higher than detecting errors early, so completeness checks can save organizations significant amounts of money.

Types of completeness checks

Completeness checks can be manual or automated, and each type of assessment has its unique benefits and limitations. Below are some of the commonly used completeness checks.

1. Totality check

The totality check ensures that all appropriate fields in a dataset are completed before the data is analyzed. It is a manual check that involves reviewing each field in the dataset to ensure that all required fields are present and contain data.

2. Range check

The range check verifies that all values entered in a particular field fall within the acceptable range. For example, if a field captures ages, the range check verifies that all ages captured are correct and within the expected range of values.

3. Format check

The format check ensures that all data entered is in the correct format and that it follows the required conventions. For example, if a field is supposed to capture dates, the format check verifies that all dates are entered correctly and follow the required format.

4. Unique identifier check

The unique identifier check verifies that every record in a dataset has a unique identifier. This check ensures that there are no duplicates or overlapping information.

5. Cross-field check

The cross-field check verifies that the data entered in one field matches the data entered in another related field. For example, if a field captures age and gender, the cross-field check verifies that the gender entered matches the age.


Completeness checks are crucial in the process of validating data. They help to ensure that all information captured is accurate, complete and consistent. Completeness checks are simple and can be performed manually or automated. The benefits of implementing completeness checks are enormous, ranging from increased accuracy to financial savings. Overall, completeness checks provide an effective strategy to reduce data entry errors and ensure data quality.