Data errors are inaccuracies, mistakes, or omissions in a dataset that can result from various sources such as human errors, data corruption, miscommunication, or system failures. These errors can range from simple typographical mistakes to more complex issues like incorrect data mapping or faulty data entry. The presence of data errors undermines the quality and validity of the data, affecting the accuracy of analyses and any subsequent decision-making processes. For example, in financial systems, a missing transaction or a misreported figure can lead to significant financial discrepancies. Identifying and correcting these errors is critical for maintaining the integrity of datasets.

https://en.wikipedia.org/wiki/Data_integrity

A common type of data error is a data entry error, which occurs when a person enters incorrect or inconsistent information into a system. This might happen due to typographical mistakes, misunderstanding of data fields, or an improper format. For instance, entering “NY” instead of “New York” or mixing up dates can lead to errors in categorization or aggregation. To address data entry errors, organizations implement validation checks, user input restrictions, and automated error-detection algorithms. Regular audits and human review can also help catch these errors before they have a significant impact on data analysis.

Another type of data error involves errors arising from data processing, such as when data is manipulated or transferred incorrectly during cleaning, transformation, or storage processes. These errors may occur if automated processes are incorrectly configured or fail to account for certain edge cases in the data. For instance, a failed ETL (Extract, Transform, Load) process might inadvertently lead to missing or incorrect data in the destination database. The use of robust data validation mechanisms and careful monitoring during processing stages can mitigate the occurrence of data processing errors.