€23.21 – €26.07
1. Executive Summary
Purpose: This report assesses the quality of customer data to identify gaps, ensure compliance with organizational standards, and support decision-making.
Key Findings:
- Overall Data Quality Score: [Insert %]
- Critical Issues Identified: [Briefly mention top 2-3 issues].
2. Scope and Methodology
Scope:
- Data Categories: Personal Information (e.g., names, email addresses), Transaction Data, Contact Information.
- Systems Reviewed: [Insert system names, e.g., CRM, ERP].
Methodology:
- Data profiling conducted using [Insert tools, e.g., Talend, Informatica].
- Quality metrics analyzed: Accuracy, Completeness, Consistency, Timeliness, Uniqueness.
3. Data Quality Metrics
| Metric | Definition | Target (%) | Achieved (%) | Notes |
|---|---|---|---|---|
| Accuracy | % of data accurately reflecting real-world entities. | 95% | [Insert %] | [Highlight issues, e.g., invalid addresses]. |
| Completeness | % of mandatory fields populated. | 98% | [Insert %] | [E.g., missing phone numbers in 200 records]. |
| Consistency | % of data uniform across systems. | 97% | [Insert %] | [E.g., mismatch in email format between systems]. |
| Timeliness | % of data updated within the defined period. | 90% | [Insert %] | [E.g., outdated contact details for 10% of records]. |
| Uniqueness | % of records free from duplicates. | 100% | [Insert %] | [E.g., 50 duplicate entries found]. |
4. Detailed Observations
4.1 Accuracy
- Issue: 15% of addresses contain invalid or outdated information.
- Recommendation: Implement address validation tools.
4.2 Completeness
- Issue: 5% of email fields are empty in critical customer segments.
- Recommendation: Enforce mandatory fields in data entry systems.
4.3 Consistency
- Issue: Inconsistent date formats observed between CRM and billing systems.
- Recommendation: Standardize data formats across platforms.
5. Compliance Review
Compliance Metrics:
- GDPR Compliance: [Yes/No/Partial]
- Data Retention Policies: [Compliant/Non-Compliant]
Comments: [Insert comments on adherence to privacy regulations or areas for improvement].
6. Data Quality Improvement Plan
- Short-Term Actions:
- Conduct a data cleanup exercise to address duplicates and missing values.
- Roll out training for data entry staff.
- Long-Term Actions:
- Implement automated validation and standardization tools.
- Integrate periodic data quality audits into governance workflows.
7. Conclusion
The current state of customer data quality highlights several areas requiring improvement, particularly in [list major issues]. Implementing the outlined improvement plan will enhance data reliability and compliance, supporting better business outcomes.
8. Appendix
- Data Sources: [List systems or databases reviewed].
- Tools Used: [List profiling and reporting tools].
- Definitions: [Explain metrics or terms, e.g., “Accuracy: The degree to which data correctly represents real-world entities.”]
