SayPro analysts to clean and validate data.

SayPro Analysts: Data Cleaning and Validation Responsibilities

1. Purpose

To ensure that all data collected from SayPro projects is accurate, complete, and consistent before analysis and reporting, supporting informed decision-making.

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2. Key Responsibilities

• Data Cleaning:
  • Identify and correct errors, inconsistencies, and duplicates in raw datasets.
  • Handle missing data by using appropriate imputation methods or flagging gaps.
  • Standardize data formats, units, and coding schemes across datasets.
  • Remove outliers or investigate anomalies that may distort analysis.
• Data Validation:
  • Cross-check data against source documents and original collection forms.
  • Verify logical consistency (e.g., date sequences, valid value ranges).
  • Confirm that data aligns with predefined indicators and reporting templates.
  • Conduct spot checks and random audits for quality assurance.
  • Collaborate with field teams to resolve discrepancies or unclear entries.

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3. Data Cleaning Process

Step	Description
1. Initial Review	Scan data for missing fields, typographical errors, or unusual values.
2. Duplicate Removal	Identify and remove repeated records to avoid data inflation.
3. Handling Missing Data	Decide on deletion, imputation, or flagging based on context and volume.
4. Standardization	Convert data into consistent formats (e.g., date formats, categorical labels).
5. Outlier Analysis	Use statistical methods to detect outliers and assess validity.
6. Documentation	Record all cleaning actions in a data cleaning log for transparency.

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4. Data Validation Techniques

• Range Checks: Confirm that numerical values fall within expected ranges.
• Consistency Checks: Ensure related fields have coherent values (e.g., end date after start date).
• Cross-Referencing: Compare reported data against baseline or previous reports.
• Logic Tests: Verify logical conditions (e.g., participants’ age matches program eligibility).
• Feedback Loop: Engage with data collectors for clarification and corrections.

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5. Tools and Software

• Excel functions and filters for preliminary cleaning.
• Statistical software (SPSS, STATA, R) for deeper validation.
• Data management platforms integrated with SayPro’s M&E system (e.g., Power BI, KoBoToolbox).
• Custom scripts or macros to automate repetitive cleaning tasks.

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6. Reporting

• Prepare a Data Cleaning and Validation Report summarizing:
  • Issues detected and corrective actions taken.
  • Data quality metrics (e.g., % missing data, error rates).
  • Recommendations for improving future data collection.

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7. Collaboration

• Work closely with Monitoring & Evaluation Officers, Regional Coordinators, and IT Support to ensure smooth data flow and quality.
• Provide training or feedback to data collectors to reduce errors at source.