SayPro Data Cleanliness and Integrity Checklist

SayPro Data Accuracy

• [ ] Source Verification: Confirm that data is sourced from reliable and reputable sources.
• [ ] Data Entry Validation: Check for errors in data entry, such as typos or incorrect values.
• [ ] Consistency Checks: Ensure that data values are consistent across different datasets (e.g., same format for dates, currency).
• [ ] Cross-Referencing: Compare data against known benchmarks or external datasets to verify accuracy.

SayPro Data Completeness

• [ ] Missing Values Assessment: Identify any missing values in the dataset and assess their impact on analysis.
• [ ] Field Completeness: Ensure that all required fields are populated (e.g., customer ID, transaction date).
• [ ] Data Coverage: Confirm that the dataset covers the necessary time periods and geographic areas relevant to the analysis.

SayPro Data Consistency

• [ ] Standardized Formats: Check that data is in standardized formats (e.g., date formats, numerical formats).
• [ ] Categorical Consistency: Ensure that categorical variables use consistent naming conventions (e.g., “Yes” vs. “Y”).
• [ ] Duplicate Records: Identify and remove any duplicate records in the dataset.

SayPro Data Validity

• [ ] Range Checks: Verify that numerical values fall within expected ranges (e.g., sales figures should not be negative).
• [ ] Logical Consistency: Ensure that data entries make logical sense (e.g., a customer cannot have a purchase date before their registration date).
• [ ] Format Validation: Check that data entries conform to expected formats (e.g., email addresses, phone numbers).

SayPro Data Integrity

• [ ] Referential Integrity: Ensure that relationships between tables (if applicable) are maintained (e.g., foreign keys match primary keys).
• [ ] Audit Trail: Maintain a record of data changes, including who made changes and when.
• [ ] Data Security: Verify that data is stored securely and access is restricted to authorized personnel.

SayPro Data Documentation

• [ ] Metadata Availability: Ensure that metadata is available to describe the data, including definitions and units of measurement.
• [ ] Data Dictionary: Maintain a data dictionary that outlines the structure, fields, and types of data in the dataset.
• [ ] Version Control: Keep track of different versions of the dataset to ensure that the most current version is being used.

SayPro Data Review and Approval

• [ ] Peer Review: Have the dataset reviewed by a colleague or team member for additional verification.
• [ ] Stakeholder Approval: Obtain approval from relevant stakeholders before proceeding with analysis.

SayPro Final Checks

• [ ] Backup Data: Ensure that a backup of the original dataset is created before any cleaning or transformation.
• [ ] Data Cleaning Log: Document any cleaning steps taken, including what changes were made and why.

Conclusion

By following this checklist, SayPro can ensure that the data used in analysis is clean, accurate, and reliable, ultimately leading to more trustworthy insights and conclusions. Regularly reviewing and updating this checklist can help maintain data integrity over time.