The data in an accounting system has inadvertently created gaps in the values for certain invoice IDs during data entry. Which type of data cleansing does the scenario present?

Duplication refers to instances where the same data appears multiple times within a dataset, potentially leading to inconsistencies and inaccuracies. While managing duplicates is important for data integrity, this scenario does not involve repeated entries but rather missing data, making this choice irrelevant.

Removing gaps or extra spaces typically pertains to eliminating unnecessary whitespace within data entries. Although this process is essential for ensuring clean data formatting, the scenario specifically highlights missing invoice IDs rather than formatting issues within existing entries.

Data formatting involves adjusting the presentation of data to meet specific standards, such as date formats or numerical precision. While formatting is a critical aspect of data cleansing, it does not address the core issue of missing values as presented in this scenario, which focuses on blank fields.