Data Quality¶
Overview¶
Data Quality refers to the fitness of data for its intended use, ensuring accuracy, completeness, consistency, timeliness, and reliability.
Maintaining high-quality data is essential for decision-making, regulatory compliance, operational efficiency, and analytics within Nissan North America (NNA).
This section defines the dimensions, metrics, governance processes, and tools necessary to manage data quality effectively.
Purpose¶
- Ensure data is accurate, consistent, complete, and timely across all business domains.
- Reduce errors, inefficiencies, and duplication in operational and analytical processes.
- Support regulatory compliance, auditability, and reporting accuracy.
- Provide a foundation for trustworthy data used in analytics, AI, and strategic decisions.
Data Quality Dimensions¶
| Dimension | Description | Example Metrics |
|---|---|---|
| Accuracy | Data correctly represents real-world entities or events. | % of correct customer addresses, % of valid VINs |
| Completeness | All required data elements are populated. | % of missing values in critical fields, % of mandatory fields filled |
| Consistency | Data is uniform across systems and datasets. | % of matching customer IDs across ERP and CRM |
| Timeliness | Data is available when needed for business processes. | Latency between transaction creation and reporting availability |
| Uniqueness | No duplicate records exist. | % of duplicate dealer codes or customer IDs |
| Validity | Data adheres to defined formats, rules, and standards. | % of fields conforming to predefined regex or enumeration values |
| Integrity | Relationships between data elements are maintained. | % of orphaned foreign key references, referential integrity checks |
Data Quality Metrics & KPIs¶
- Domain-level KPIs: Monitor quality metrics for Customer, Vehicle, Dealer, Product, Finance, and Supply Chain.
- Critical Data Elements (CDEs): Identify key attributes for each domain with defined thresholds.
- Scorecards & Dashboards: Track data quality trends over time, highlighting areas of concern.
Example KPI table:
| Domain | CDE | Metric | Threshold | Owner |
|---|---|---|---|---|
| Customer | Customer ID | Accuracy | 99% | Customer Data Owner |
| Vehicle | VIN | Completeness | 100% | Vehicle Data Steward |
| Dealer | Dealer Code | Uniqueness | 100% | Dealer Domain Owner |
Data Quality Management Process¶
- Define Metrics & Rules: Establish standards for data quality across domains.
- Assess & Profile Data: Evaluate data against quality dimensions and thresholds.
- Identify Issues & Root Causes: Detect anomalies, duplicates, missing data, and inconsistencies.
- Remediate & Monitor: Correct issues, validate fixes, and monitor ongoing quality.
- Report & Escalate: Communicate quality trends and issues to stakeholders.
- Continuous Improvement: Adjust rules, processes, and automation based on findings.
Roles & Responsibilities¶
| Role | Responsibilities |
|---|---|
| Data Owner | Defines critical data elements, approves quality rules, and oversees remediation. |
| Data Steward | Monitors data quality, identifies issues, and implements corrective actions. |
| IT / Data Engineering | Supports automation, profiling, and reporting tools. |
| Governance Council | Reviews quality trends, sets enterprise standards, and approves escalations. |
Tools & Technologies¶
- Data profiling and validation tools: Informatica, Talend, Ataccama, or custom scripts.
- Data quality dashboards: Visualize KPIs and track remediation efforts.
- Automated alerts: Notify stakeholders of quality deviations or rule violations.
- Integration with catalog and lineage: Connect quality metrics to assets and processes.
Visual Representation¶
flowchart TB
A[Data Sources] --> B[Profiling & Validation]
B --> C[Data Quality Dashboard]
C --> D[Data Steward / Owner]
D --> E[Remediation & Monitoring]
E --> B