Actian Data Observability : Monitoring Data : Data Quality Score
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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)
S_C = Completeness percentage
Directly uses the percentage of populated required fields.
Validity (S_V)
S_V = Validity percentage
Directly uses the percentage of records passing business rules.
Freshness (S_F)
If No Freshness Incidents: S_F = 100
If Freshness Incidents Exist: S_F = 0 (or configured penalty score, e.g., 80)
Integrity/Incidents (S_Inc)
S_Inc = MAX(0, 100 × (1 - (I_current / I_max)))

Where:
- I_current = Number of open incidents
- I_max = Maximum tolerable incident threshold (default: 20)
Constraints:
  • If I_current ≥ I_max, then S_Inc = 0
  • If I_current = 0, then S_Inc = 100
Final DQ Score Formula
DQ Score = ((S_C × W_C) + (S_V × W_V) + (S_F × W_F) + (S_Inc × W_Inc)) / W_Total

Where:
- S = Normalized dimension score (0-100)
- W = Dimension weight
- W_Total = W_C + W_V + W_F + W_Inc
Output: Value between 0 and 100
Default Weights
Data Observability provides industry-standard default weights that prioritize data accuracy and fundamental usability:
Dimension
Default Weight
Rationale
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
Dimension Weights
The system allows dynamic, per-dataset configuration of dimension weights:
  1. Navigate to your dataset settings
  2. Select Data Quality Score Configuration
  3. Adjust weights to match business priorities
  4. Document justification for non-default weights
Note: 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
Score Range
Quality Level
Recommended Action
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
Last modified date: 02/20/2026