Skip to main content

Function Dashboard

The Function Dashboard provides organisational transparency and accountability for data quality across the enterprise.

Every Source Message within CryspIQ® is assigned:

  • A Data Steward
  • A Business Function
  • Data Quality Rules
  • Ownership responsibilities

This allows organisations to clearly understand where data quality issues originate, who is responsible and how individual business functions are performing.

The dashboard provides visibility into:

  • Data Quality exceptions
  • Business function performance
  • Data Steward accountability
  • DAMA quality dimensions
  • Organisational trends
  • Areas requiring remediation

Overview​

CryspIQ® follows a fundamental principle:

info

Data must be fixed at source.

CryspIQ® does not clean, modify or repair source data during processing.

When incoming data fails a Data Quality Rule:

  1. The record is identified.
  2. The record is loaded into the Data Quality Exemption table.
  3. The issue becomes visible in the dashboard.
  4. The responsible Data Steward investigates.
  5. The source system is corrected.
  6. The corrected record is re-pushed.
  7. The exemption is automatically resolved.

This approach ensures accountability remains with the data owner.

Quality Monitoring Process​

Push Data
      ↓
Data Quality Rules
      ↓
Pass?
      ↓

   YES
    ↓
 Continue Processing

   NO
    ↓
DQ Exemption Table
    ↓
Function Dashboard
    ↓
Data Steward Investigation
    ↓
Fix at Source
    ↓
Re-Push Data
    ↓
Exemption Resolved

Why the Function Dashboard Exists​

Many organisations struggle to answer:

  • Which business area owns the issue?
  • Who is responsible?
  • How serious is the problem?
  • Is quality improving or declining?
  • Which functions require support?

The Function Dashboard makes ownership visible.

This drives:

  • Accountability
  • Transparency
  • Better governance
  • Continuous improvement

Dashboard Overview​

The Function Dashboard provides several views.

Function League Table​

Provides a ranking of business functions based on data quality performance.

This creates organisational transparency by allowing functions to compare performance.

Example:

RankBusiness FunctionQuality Score
1Finance98%
2Human Resources96%
3Procurement92%
4Operations89%
5Sales85%

The objective is not competition.

The objective is visibility and accountability.

Data Quality Heatmap​

The Heatmap provides a visual representation of data quality issues across the organisation.

The heatmap aligns to the DAMA Data Quality Dimensions.

Areas with higher issue volumes are highlighted and can be investigated further.

This allows administrators and business leaders to quickly identify:

  • High-risk areas
  • Emerging trends
  • Persistent issues
  • Governance weaknesses

DAMA Data Quality Dimensions​

CryspIQ® categorises Data Quality Rules against DAMA dimensions.

DimensionDescription
CompletenessRequired data exists
ValidityData conforms to business rules
AccuracyData correctly represents reality
ConsistencyData is consistent across systems
TimelinessData is available when required
UniquenessDuplicate records are avoided
IntegrityRelationships between data are maintained

The Heatmap helps organisations understand which dimensions are creating the most issues.

Business Function Ownership​

Every Source Message is assigned to a business function.

Examples include:

Finance
Human Resources
Operations
Sales
Marketing
Procurement
Customer Service

This ownership model ensures accountability for data quality remains with the business.

Data Steward Accountability​

Each Source Message is also assigned a Data Steward.

The Data Steward is responsible for:

  • Monitoring quality issues
  • Investigating root causes
  • Working with source system owners
  • Coordinating remediation activities
  • Ensuring issues are resolved

The dashboard provides visibility into steward performance and outstanding issues.

Data Quality Exemptions​

When a record fails a Data Quality Rule, it is recorded in the Data Quality Exemption table.

Examples include:

Missing Customer Name
Invalid Date of Birth
Invalid Product Classification
Duplicate Customer Record
Invalid Cost Centre

The exemption remains visible until the issue is resolved.

Fix at Source Principle​

CryspIQ® does not allow data cleansing during processing.

This is a core governance principle.

Incorrect Approach​

Poor Source Data
      ↓
Clean During Processing
      ↓
Load

This hides the underlying issue.

CryspIQ® Approach​

Poor Source Data
      ↓
Identify Issue
      ↓
DQ Exemption
      ↓
Fix Source System
      ↓
Re-Push Data
      ↓
Load

This creates long-term improvements in enterprise data quality.

Automatic Resolution​

When corrected data arrives:

  1. CryspIQ® identifies the original exemption.
  2. The exemption is marked as resolved.
  3. The issue drops from dashboard reporting.
  4. Quality metrics automatically improve.

No manual intervention is required.

This creates a self-healing governance process.

Investigating Data Quality Issues​

When issues appear in the dashboard:

Step 1 – Review the Business Function​

Identify which function owns the data.

Step 2 – Review the Assigned Data Steward​

Determine who is responsible for remediation.

Step 3 – Review the Failed Rule​

Identify the specific Data Quality Rule that failed.

Step 4 – Review the Source Message​

Determine which source process introduced the issue.

Step 5 – Identify the Root Cause​

Common causes include:

  • Missing mandatory fields
  • Invalid values
  • Incomplete records
  • Source system defects
  • Process failures

Step 6 – Fix the Data at Source​

Work with the source system owner to correct the issue.

Step 7 – Re-Push the Corrected Data​

Once corrected, push the data again and monitor resolution.

Common Root Causes​

Incomplete Records​

Mandatory information is missing.

Examples:

Date of Birth
Gender
Country
Business Function
Product Classification

Invalid Data​

Data does not conform to expected formats.

Examples:

Invalid Email Address
Invalid Phone Number
Invalid Date Format

Duplicate Records​

Multiple records represent the same business object.

Missing Context​

Required contextual information has not yet arrived.

These issues may also appear within the Parking Lot dashboard.

Monitoring Best Practices​

Daily​

Review:

  • New exemptions
  • High-risk functions
  • Critical rule failures

Weekly​

Review:

  • Function rankings
  • Steward performance
  • Quality trends

Monthly​

Review:

  • DAMA Heatmap trends
  • Governance performance
  • Continuous improvement opportunities

Benefits of the Function Dashboard​

The Function Dashboard helps organisations:

  • Improve accountability
  • Increase transparency
  • Strengthen governance
  • Measure stewardship performance
  • Identify systemic issues
  • Improve data quality culture
  • Support regulatory compliance
  • Build trust in reporting and analytics

Next Steps​

  1. Review business function performance.
  2. Investigate Data Quality exemptions.
  3. Work with Data Stewards.
  4. Fix issues at source.
  5. Re-push corrected data.
  6. Monitor automatic resolution.
  7. Review DAMA quality trends.

The Function Dashboard provides the transparency and accountability required to build a sustainable enterprise data quality culture and continuously improve data quality across the organisation.