1. Steps taken to clean the data will include identifying and handling missing or incomplete values, addressing outliers, correcting errors, and ensuring consistency in formatting.
2. Application of data transformation methods such as converting data types, scaling numerical values, or encoding categorical variables.
3. The cleaned dataset will be well-organized, with consistent formatting, and free from errors that could introduce bias or mislead analysis. It should also include any new variables or features created during the cleaning process.
4. Depending on the organization's standards, the cleaned dataset may be saved in a specific format (e.g., CSV, Excel, or a database) for easy sharing and future use.
5. Providing a summary or documentation outlining the overall quality of the data after the cleaning process. This may include statistics on the percentage of missing values, information on the handling of outliers, and any additional notes on data quality.