Data cleaning is an essential part of the data analysis process, as it ensures data accuracy, consistency and reliability. A data cleaning checklist establishes standards and guidelines for achieving high quality data that can be used with confidence. It reduces errors due to incorrect or missing values by identifying them in advance and correcting the issues before processing begins. Having a data cleaning checklist also helps ensure that data collected follows the same set of criteria and can easily be compared against different sources. Inaccurate records can lead to skewed results when analyzing big data sets, so it’s important to adhere to a strict list of standards when collecting information. Additionally, a well-structured list encourages teams to identify potential problems before they even start working on the project, which could save time and resources down the line.