SayPro Templates to Use: Data Quality Assessment Template: A standardized form for documenting

Here’s a Data Quality Assessment Template that you can use to document data quality checks. This template includes specific criteria and space for findings, ensuring consistency across assessments.

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Data Quality Assessment Template

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1. Assessment Information:

• Assessment Date: [Date of the assessment]
• Assessment Period: [Start and End Dates for the data being assessed]
• Assessor Name: [Name of the person performing the assessment]
• Project/Department: [Project or department for which data is being assessed]

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2. Data Source Information:

• Data Source Name: [Name of the data source (e.g., Database, Spreadsheet, Survey Results, etc.)]
• Data Type: [e.g., Raw data, Cleaned data, Historical data]
• File Format: [e.g., CSV, Excel, Database, JSON, etc.]
• Data Owner/Team: [Name of the team or individual responsible for the data]

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3. Data Quality Criteria:

Criteria	Definition	Assessment (Yes/No/Partial)	Findings	Comments/Actions Required
Accuracy	Data reflects the true value without errors or distortions.	[ ] Yes / [ ] No / [ ] Partial	[Insert findings here]	[Suggestions for improvements]
Completeness	Data contains all necessary fields and entries.	[ ] Yes / [ ] No / [ ] Partial	[Insert findings here]	[Suggestions for improvements]
Consistency	Data values are consistent across different datasets.	[ ] Yes / [ ] No / [ ] Partial	[Insert findings here]	[Suggestions for improvements]
Timeliness	Data is up-to-date and available within the required timeframe.	[ ] Yes / [ ] No / [ ] Partial	[Insert findings here]	[Suggestions for improvements]
Uniqueness	Data does not contain duplicate entries.	[ ] Yes / [ ] No / [ ] Partial	[Insert findings here]	[Suggestions for improvements]
Integrity	Data relationships and references are maintained accurately (e.g., foreign keys).	[ ] Yes / [ ] No / [ ] Partial	[Insert findings here]	[Suggestions for improvements]

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4. Data Quality Scorecard (Optional):

• Overall Data Quality Rating:
  [ ] Excellent (95%+ accurate, complete, and consistent)
  [ ] Good (80%-94% accurate, complete, and consistent)
  [ ] Fair (60%-79% accurate, complete, and consistent)
  [ ] Poor (Below 60% accurate, complete, and consistent)

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5. Summary of Findings:

• Strengths:
  [Highlight the key areas where data quality is good, such as completeness or consistency.]
• Areas for Improvement:
  [Describe any areas that need attention, such as missing data, duplicates, or inconsistencies.]

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6. Recommendations for Improvement:

• [List any recommended actions to improve data quality, such as additional cleaning processes, automated validation, or training for data entry teams.]

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7. Follow-up Actions/Next Steps:

• Assigned To: [Name of person/team responsible for follow-up]
• Due Date: [Date for resolution or next assessment]

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8. Signature:

• Assessor’s Signature:
  [Signature or name of the person conducting the assessment]
• Date:
  [Date when the assessment is concluded]

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This template ensures thorough documentation of the data quality checks and helps track improvements over time. Let me know if you’d like adjustments or a different structure!