1. Initial Data Assessment:

Identify any potential data quality issues, such as missing values, duplicates, outliers, inconsistent formats, and errors.

2. Data Cleaning:

Address missing data and remove duplicates.

3. Data Validation:

Cross-check data with external sources and check for data integrity.

4. Documentation:

Maintain detailed documentation of the cleaning and validation process, including the steps taken, methods used, and any transformations applied.

5. Data Quality Assurance:

Implement quality control checks to ensure that data remains clean and valid over time. Offer ongoing data monitoring and validation services to help the client maintain data quality.