Data Quality Deep Dive (Power BI)
The Data Quality Deep Dive Dashboard provides organisation-wide visibility into data quality issues and enables users to investigate the underlying causes of exceptions identified within CryspIQ®.
While the Function Dashboard and Steward Dashboard provide transparency and accountability, the Deep Dive Dashboard enables detailed analysis of the individual records contributing to data quality issues.
The dashboard allows users to:
- Drill into data quality issues
- Investigate root causes
- Analyse trends over time
- Review failed records
- Review failed rules
- Identify recurring issues
- Monitor remediation activities
- Support governance and audit requirements
Overview
The Data Quality Deep Dive Dashboard is delivered through Microsoft Power BI and connects directly to CryspIQ® Data Quality reporting datasets.
The dashboard provides a single enterprise-wide view of:
- Data Quality Exceptions
- DAMA Quality Dimensions
- Source Messages
- Business Functions
- Data Stewards
- Failed Records
- Quality Trends
This enables organisations to move beyond simply identifying issues and begin understanding why they occur.
Quality Monitoring Framework
CryspIQ® follows a simple governance process:
Source Data
↓
Data Quality Rules
↓
DQ Exemption
↓
Dashboard Visibility
↓
Root Cause Analysis
↓
Fix at Source
↓
Re-Push Data
↓
Automatic Resolution
The Deep Dive Dashboard focuses on the investigation and analysis stages.
Why Use the Deep Dive Dashboard?
Summary dashboards answer:
- What is wrong?
- Who owns the issue?
- Which function is impacted?
The Deep Dive Dashboard answers:
- Why did the issue occur?
- Which records failed?
- Which rules failed?
- What patterns are emerging?
- What needs to be fixed?
Dashboard Overview
The Deep Dive Dashboard typically contains several reporting areas.
Executive Summary
Provides an organisation-wide view of:
- Total Exceptions
- Open Exceptions
- Resolved Exceptions
- Quality Scores
- Trend Indicators
This provides a high-level health check of enterprise data quality.
Function Analysis
Allows users to analyse quality performance by business function.
Examples include:
Finance
Human Resources
Operations
Sales
Marketing
Procurement
Customer Service
Users can compare quality performance across the organisation.
Steward Analysis
Displays quality issues by Data Steward.
This provides visibility into:
- Steward workloads
- Resolution performance
- Open issues
- Source Message ownership
DAMA Quality Analysis
Issues are categorised according to DAMA Data Quality Dimensions.
| Dimension | Description |
|---|---|
| Completeness | Required data exists |
| Validity | Data conforms to business rules |
| Accuracy | Data reflects reality |
| Consistency | Data is aligned across systems |
| Timeliness | Data is available when required |
| Uniqueness | Duplicate records are avoided |
| Integrity | Relationships remain intact |
Users can quickly identify which dimensions are creating the greatest impact.
Drill Through Capability
One of the most powerful features of the dashboard is drill-through analysis.
Users can start at a summary level and progressively drill into the underlying data.
Example Journey
Organisation
↓
Business Function
↓
Data Steward
↓
Source Message
↓
Failed Rule
↓
Individual Record
This enables rapid investigation of issues without requiring technical skills or direct database access.
Record-Level Analysis
Users can review individual exceptions and investigate:
- Source System
- Source Message
- Business Function
- Data Steward
- Failed Rule
- Failed Value
- Date Identified
- Resolution Status
This provides complete visibility into why a record failed.
Root Cause Investigation
The dashboard is designed to support root cause analysis.
Typical investigation questions include:
Are issues concentrated within a specific function?
Example:
Operations = 65%
Finance = 15%
HR = 10%
Sales = 10%
Are issues linked to a specific Source Message?
Example:
Customer Master
Employee Master
Product Master
Sales Transactions
Are specific Data Quality Rules failing repeatedly?
Example:
Missing Customer Name
Invalid Email Format
Duplicate Product Code
Missing Date of Birth
Are issues linked to a specific DAMA Dimension?
Example:
Completeness = 60%
Accuracy = 20%
Validity = 15%
Consistency = 5%
This helps prioritise remediation efforts.
Transparency and Governance
The Deep Dive Dashboard is designed to provide transparency across the organisation.
All users see:
- Business Function performance
- Steward ownership
- Quality trends
- Issue volumes
This visibility helps:
- Encourage accountability
- Promote collaboration
- Support governance programs
- Improve data quality culture
Exporting Data
Users can export results for further analysis.
Common export scenarios include:
- Management reporting
- Audit reviews
- Governance meetings
- Regulatory investigations
- Continuous improvement programs
Supported exports include:
- CSV
- Excel
- Power BI datasets
Common Investigation Scenarios
Repeated Completeness Issues
Investigate:
- Missing mandatory fields
- Incomplete source records
- Data entry processes
Duplicate Records
Investigate:
- Source system controls
- Integration processes
- Business procedures
Invalid Values
Investigate:
- Validation controls
- Data entry standards
- Upstream process defects
Missing Contextual Data
Investigate:
- Parking Lot activity
- Master Data processes
- Source Message completeness
Monitoring Best Practices
Daily
Review:
- New exceptions
- Critical failures
- High-volume issues
Weekly
Review:
- Function performance
- Steward performance
- Rule failure trends
Monthly
Review:
- DAMA quality trends
- Governance performance
- Continuous improvement opportunities
Benefits of the Deep Dive Dashboard
The Data Quality Deep Dive Dashboard helps organisations:
- Understand root causes
- Improve transparency
- Support governance initiatives
- Increase accountability
- Accelerate issue resolution
- Improve stewardship effectiveness
- Strengthen regulatory compliance
- Improve trust in reporting and analytics
Most importantly, it helps organisations move from simply identifying issues to understanding and eliminating the causes of poor data quality.
Related Guides
Next Steps
- Review organisation-wide quality performance.
- Identify high-risk business functions.
- Investigate recurring quality issues.
- Drill into affected Source Messages.
- Analyse failed records.
- Fix issues at source.
- Monitor improvements over time.
The Data Quality Deep Dive Dashboard provides the detailed analytical capability required to understand, investigate and continuously improve enterprise data quality across the organisation.