Data Quality Score
Overview
The Data Quality (DQ) Score is a normalized, business-relevant measure of data health that provides a single indicator of a dataset's fitness for use. Expressed as a percentage from 0% to 100%, the DQ Score helps organizations quickly assess and monitor the overall quality of their data assets.
Purpose
The DQ Score methodology ensures:
Consistency - Standardized measurement across all data assets
Normalization - Comparable scores regardless of data volume or complexity
Business Relevance - Weighted dimensions that reflect organizational priorities
Actionability - Clear identification of data quality issues requiring attention
Core Dimensions
The DQ Score is calculated as a weighted average of four fundamental data quality dimensions:
Completeness
Measures the extent to which required data fields are populated.
Metric: Percentage of required fields containing values Score Range: 0-100%
Validity
Measures compliance with defined business rules and data constraints.
Metric: Percentage of records passing validation rules Score Range: 0-100%
Freshness (Timeliness)
Measures whether data is up-to-date and meets timeliness requirements.
Metric: Binary indicator of freshness incidents Score Range: 0 or 100
Integrity (Incident Health)
Measures operational stability through the volume of open data quality incidents.
Metric: Count of open incidents relative to threshold Score Range: 0-100
Score Calculation
Normalization Process
All source metrics are normalized to scores between 0 and 100 before being integrated into the final DQ Score calculation.
Completeness (S_C)
Directly uses the percentage of populated required fields.
Validity (S_V)
Directly uses the percentage of records passing business rules.
Freshness (S_F)
Integrity/Incidents (S_Inc)
Constraints:
If I_current ≥ I_max, then S_Inc = 0
If I_current = 0, then S_Inc = 100
Final DQ Score Formula
Output: Value between 0 and 100
Default Weights
Telmai provides industry-standard default weights that prioritize data accuracy and fundamental usability:
Validity
40%
Highest priority - measures compliance with critical business rules
Completeness
30%
Second priority - measures availability of required information
Integrity (Incidents)
20%
High priority penalty - reflects operational stability and issue volume
Freshness
10%
Contextual priority - importance varies by use case
TOTAL
100%
Simplifies calculation denominator
Weight Customization
Weights can be adjusted per dataset to reflect specific business requirements:
Real-time systems: Increase Freshness weight (e.g., 25-30%)
Analytical systems: Prioritize Completeness and Validity
Mission-critical systems: Increase Integrity/Incidents weight
Configuration
UI-based configuration for DQ Score is coming soon. Configuration is currently available via API only.
Configuration is managed via the DQ Score APIs. The following parameters can be set per dataset:
Dimension Weights
Weights can be configured dynamically per dataset. All four weights must sum to 100.
Incident Threshold (I_max)
Configure the maximum tolerable incident threshold per dataset:
Default: 20 open incidents
Low-tolerance assets: 5-10 incidents
High-volume assets: 30-50 incidents
The threshold should reflect:
Dataset criticality
Typical incident volumes
Business impact tolerance
Best Practices
Interpreting DQ Scores
90-100
Excellent
Maintain current practices
75-89
Good
Monitor trends, address minor issues
60-74
Fair
Investigate dimension contributors, plan improvements
0-59
Poor
Immediate attention required, escalate issues
To start using this feature, please refer to DQ Score APIs
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