Information Management / Project 03
Humanitarian Data Quality System
A structured framework for identifying, tracking and resolving data quality issues in humanitarian reporting workflows.
Overview
A professional-work portfolio entry, described at a generic level to protect organizational information.
A data quality framework can combine validation checks, issue tracking and reporting so teams can identify and follow up on problems systematically.
The problem
Why this work matters
Inconsistent or incomplete operational records can affect analysis, grant reporting and confidence in decisions.
Approach
From need to system
A data quality framework can combine validation checks, issue tracking and reporting so teams can identify and follow up on problems systematically.
Information management and data quality work connected to humanitarian databases and grant reporting workflows.
Architecture
How the pieces fit
- Production datasets remain within their authorized environment
- Validation rules check for defined data quality issues
- Findings and follow-up are tracked in a controlled process
- Aggregate reporting communicates patterns without exposing records
Capabilities
Features & focus
- Excel-based data quality checks
- Validation rules and automated checks
- Data quality findings and logs
- Issue tracking and monitoring
- Data quality reporting
Data flow
Authorized operational data is checked against validation rules
↓Potential findings are recorded for review
↓Responsible teams investigate and resolve issues
↓Aggregated quality reporting supports monitoring
Visual documentation
Screenshots
No public screenshots are available yet. Future visuals will use sanitized or mock-data examples.
Challenges
- Making validation rules understandable and actionable
- Protecting sensitive records and organizational systems
- Connecting quality findings to practical follow-up
Lessons learned
- Data quality is an ongoing process, not a one-time cleaning exercise.
- Demonstrations should use mock or sanitized data, never confidential records.
Next steps
Roadmap
- Develop a fully sanitized demonstration dataset
- Document example validation patterns
- Show a generic reporting workflow without organizational identifiers