"Design a comprehensive data quality management framework for a large-scale enterprise, detailing technical steps and methodologies for implementation. Outline metrics for measuring data quality and strategies for addressing data inconsistencies. Tailor the approach for advanced users familiar with data management principles and technologies. Include real-world examples and case studies to illustrate key concepts and best practices. Ensure the framework ensures data accuracy, completeness, and consistency across the organization. Recommend tools and technologies for implementation, considering scalability and integration with existing systems.** Address the following components:** ## 1. Data Quality Metrics and Monitoring ## 2. Data Inconsistency Detection and Resolution ## 3. Data Governance and Policy Framework ## 4. Technology and Tool Recommendations ## 5. Implementation Roadmap and Change Management Provide a structured approach with clear, actionable steps and technical specifications."