Methodology 03

Data Validation & Assurance

This step ensures that all data collected is validated for completeness, consistency, accuracy, traceability, and reliability before it is used in scoring. Robust validation and assurance safeguard the integrity of HPI results.

Methodology
Validation Score
91.6
/100
Excellent
Assurance
4.9 points
vs last assessment
Priority
Critical
Quality Control
Overview Checks Components Controls Dashboard Improvement Actions
Pillar
Completeness 92.4 /100
Excellent
Pillar
Consistency 90.8 /100
Excellent
Pillar
Accuracy 91.2 /100
Excellent
Pillar
Traceability 89.9 /100
Very Good
Pillar
Audit Readiness 90.6 /100
Excellent
What It Covers
  • Completeness checks
  • Outlier review and detection
  • Data reconciliation
  • Source verification
  • Audit trails and traceability
  • Exception handling
  • Manual review processes
building
Core Sections
Completeness
Completeness
Accuracy
Accuracy
Consistency
Consistency
Reconciliation
Reconciliation
Audit Trail
Audit Trail
Exception Review
Exception Review
Key Elements
  • Tolerance rules and thresholds
  • Validation workflows
  • Approval gates
  • Data ownership and accountability
  • Correction logs and reprocessing
  • Assurance summary reports
Risk Signals
  • Duplicate records detected
  • Mismatched or conflicting values
  • Unresolved data exceptions
  • Incomplete audit trails
  • Late validation or re-submission
  • Weak data governance
Dashboard Preview
Validation Score Trend (12M)
91.6
4.9
Exceptions Resolved (12M)
326
12%
Validation Timeliness (12M)
93%
5%
Exceptions by Type
  • Data Quality 44%
  • Missing Data 24%
  • Outliers 18%
  • Mismatches 9%
  • Others 5%
Improvement Actions
  • Strengthen validation rules and business logic
  • Enhance data stewardship and accountability
  • Expand automated checks and controls
  • Implement regular review and revalidation cycles
  • Improve exception management and escalation
  • Maintain robust assurance documentation
Pillar
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