Data Quality
The Data Quality dashboard allows you to assess the completeness and consistency of data collected in the system.
Access
Menu: Monitoring & Evaluation → Data Quality
Overview
The dashboard displays quality indicators for the main system entities, enabling rapid identification of gaps and anomalies.
Main Statistics
Summary Cards
| Indicator | Description |
|---|---|
| Overall Score | Average quality score across all entities |
| Households | Household form completeness score |
| Beneficiaries | Beneficiary form completeness score |
| Anomalies | Total number of detected anomalies |
Score Color Coding
| Score | Color | Interpretation |
|---|---|---|
| ≥ 80% | Green | Good quality |
| 60-79% | Orange | Average quality, improvements needed |
| < 60% | Red | Insufficient quality, action required |
Filters
Filter by Region
Select a region to see only that area's data:
- Oio
- Bafatá
:::tip Tip Analyze each region separately to identify areas needing more attention. :::
Refresh
Click the Refresh button to reload data in real time.
Member Statistics
This section displays metrics about household members:
| Indicator | Description |
|---|---|
| Total Members | Total number of registered members |
| Households with Members | Households with at least one member |
| Households without Members | Households with no registered member (anomaly) |
| Average Members/Household | Average ratio |
:::warning Attention Households without members represent an anomaly to correct. Each household should have at least one member (the household head). :::
Scores by Category
Completeness scores are calculated by field category:
Evaluated Categories
| Category | Description |
|---|---|
| Identification | Household code, location |
| Household Head | Name, gender, age, contact |
| Composition | Size, age groups |
| Housing | Type, materials, equipment |
| Geolocation | GPS coordinates |
Score Calculation
For each category:
- 100% = All required and recommended fields are filled
- Partial = Some fields are empty
- 0% = No fields filled
Anomalies
Anomaly Types
| Type | Severity | Description |
|---|---|---|
missing_fields | Medium | Required fields not filled |
invalid_data | High | Inconsistent or invalid data |
duplicate | High | Potential duplicates detected |
orphan_record | Critical | Record without required relations |
out_of_range | Medium | Values outside acceptable limits |
Severity Levels
| Severity | Color | Priority |
|---|---|---|
| Critical | Red | Immediate action |
| High | Orange | Priority action |
| Medium | Blue | To fix |
| Low | Gray | To improve |
Anomaly Distribution
Two visualizations are available:
- By severity: Number of anomalies by criticality level
- By type: Distribution by problem type
Anomaly List
The detailed table lists individual anomalies:
Columns
| Column | Description |
|---|---|
| Severity | Criticality level |
| Type | Anomaly category |
| Entity | Code of concerned household/beneficiary |
| Message | Problem description |
Filtering
The first 20 anomalies are displayed. To see more:
- Export data to Excel
- Use region filters
Use Cases
Identify Low Quality Areas
- Access the Data Quality dashboard
- Compare overall scores of different regions
- Identify the region with the lowest score
- Analyze problematic categories
- Plan corrective actions
Fix Critical Anomalies
- View the anomaly list
- Filter by "Critical" severity
- For each anomaly:
- Note the code of the concerned entity
- Access the household/beneficiary form
- Correct the erroneous data
- Refresh to verify correction
Improve Completeness Score
- Identify categories with lowest scores
- For each category:
- List missing fields
- Plan supplementary collection
- Update concerned forms
- Track score evolution
Check Households Without Members
- Check the "Households without Members" statistic
- If > 0, export the anomaly list
- Identify concerned households
- Add missing members
- Verify each household has at least the household head
Best Practices
Regular Monitoring
- Check the dashboard at least once a week
- Track score evolution over time
- React quickly to critical anomalies
Prioritization
- Address critical anomalies first
- Focus on low-score categories
- Target most problematic regions
Prevention
- Train enumerators on required fields
- Validate data before import
- Use quality controls during data entry
Integration with Other Modules
Households
Household-related anomalies can be corrected directly from the Household Management module.
Beneficiaries
Beneficiary anomalies are accessible via the Beneficiaries module.
Reports
Quality indicators feed into Reports and DLI calculations.