SayPro Data Cleanliness and Integrity Checklist

Data Collection Verification:

• Data Source Validation: Confirm that the data is sourced from reliable and reputable sources (e.g., internal systems, reputable third-party data providers, government databases).
• Consistency Across Sources: Ensure that data from multiple sources is consistent (e.g., compare internal sales data with industry reports to ensure alignment).
• Timeliness of Data: Verify that the data is up-to-date and reflects the most recent available information (e.g., sales data from the current quarter, financial reports from the latest fiscal year).

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2. Data Completeness:

• Missing Data Check: Identify and document any missing values in the dataset. Assess the impact of missing data on analysis.
• Handling Missing Data: Establish a method to address missing data (e.g., imputation, deletion, or leave as missing based on the context).
• Data Coverage: Ensure the dataset covers all relevant time periods, regions, and other critical variables for the analysis.
• Verification of Full Dataset: Confirm that the dataset includes all intended variables and that no key data points have been overlooked.

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3. Data Accuracy:

• Outlier Detection: Check for extreme outliers that may skew the results and determine whether they should be removed or corrected.
• Range Validation: Verify that the values fall within acceptable ranges (e.g., sales figures should not be negative, customer ages should be within a realistic range).
• Duplicate Records: Identify and resolve any duplicate records within the dataset to ensure data integrity.
• Cross-Validation: Cross-check data with independent external or internal sources to verify accuracy (e.g., comparing financial data against accounting records).

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4. Data Consistency:

• Data Format Standardization: Ensure that all data is consistently formatted (e.g., date formats are uniform, currency values are standardized, and numerical data is in consistent units).
• Consistency Across Variables: Check that related variables align logically (e.g., product categories should be consistent across sales and inventory data).
• Consistency in Categories: Ensure that categorical variables are consistent (e.g., product names, region names) without spelling errors or variations in naming.
• Time Period Consistency: Confirm that time-based data (e.g., sales, financials) is consistent and covers the same time period across all relevant datasets.

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5. Data Transformation and Preparation:

• Normalization: Ensure that any necessary data transformations (e.g., normalization of data ranges, currency conversions) have been applied.
• Variable Transformation Check: Verify that any transformations applied to the data (e.g., conversion of categories to numerical data, log transformations) are correctly implemented and documented.
• Data Aggregation: Confirm that aggregated data (e.g., monthly sales totals) accurately reflects the underlying data and does not distort trends.

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6. Data Integrity:

• Error Detection: Check for any errors that may have been introduced during data entry, collection, or transformation (e.g., incorrect coding, manual input errors).
• Data Relationships: Verify that relationships between different datasets (e.g., sales data and customer data) are correctly matched (e.g., customer ID, transaction ID).
• Referential Integrity: Ensure that foreign keys and references across datasets are valid and that no orphan records exist.
• Validation with Historical Data: Cross-check new data against historical data to ensure no significant inconsistencies or unexpected changes.

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7. Data Privacy and Ethical Considerations:

• Compliance with Privacy Laws: Ensure that the data complies with relevant privacy regulations (e.g., GDPR, CCPA) and that personal data is anonymized or secured where necessary.
• Data Security: Ensure that proper security measures are in place to protect sensitive data from unauthorized access or breaches.
• Ethical Handling of Data: Verify that the data is used ethically, with respect to the rights and confidentiality of individuals or organizations involved.

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8. Final Review and Approval:

• Quality Assurance Review: Have the data cleaned and prepared reviewed by a second set of eyes to catch any overlooked issues.
• Approval from Stakeholders: Ensure that the relevant stakeholders (e.g., data analysts, project managers, business owners) have reviewed and approved the dataset for use in analysis.
• Documentation of Data Cleaning Procedures: Ensure that all steps taken to clean and prepare the data are thoroughly documented and accessible for future reference or reproducibility.

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9. Ongoing Monitoring:

• Data Quality Monitoring Plan: Develop a process to periodically review and monitor the quality of the data throughout the analysis and reporting phases.
• Feedback Loop: Establish a mechanism for capturing feedback on data quality from end users or stakeholders, enabling continuous improvement in data accuracy and integrity.