Challenge it addresses
- 5000 employees and hundreds of systems
- Units and people across the enterprise were not in full agreement on the understanding of fundamental entities such as ‘Customer’
- Data mastering systems/SVoTs were not consistently defined across the enterprise; integration decisions were localized rather than following a consistent framework
- It was impossible to deliver predictable results in Change Management
- Impact analysis
- Project sizing and planning
Benefits:
- Enterprise Data Quality Scorecard provided MIS interfaces for insight and oversight
- Causal Analysis Reports helped identify weak areas and causes of data quality issues
- Financial and reputational impact due to low data quality was mitigated
- Business stakeholders could be assured that data will be complete, accurate, timely, and suitable for business decision making, communications, and reporting; they were able to have a forum to report and receive service for data quality issues