Which option describes a best practice when cleaning data?

Coming to an agreement on what "clean" data looks like is essential in data cleaning processes, as it establishes a clear baseline and understanding among all stakeholders involved in the data management workflow. This consensus helps ensure that everyone involved in the project—data engineers, analysts, and stakeholders—has the same expectations regarding the criteria and standards for data cleanliness.

This agreement might involve defining acceptable ranges for numerical values, determining how to handle missing data, or setting rules for categorizing and formatting data. By agreeing on these definitions upfront, teams can work more efficiently and avoid confusion or misinterpretation later in the project. A well-defined understanding of cleanliness can also guide the development of automated data validation and cleaning processes, ultimately improving data quality and reliability.

While other options touch upon aspects of data management, they do not tackle the foundational issue of establishing a clear standard for what constitutes clean data, which is vital for effective and collaborative data cleaning efforts.