Business Context
DataCorp, a leading provider of AI-driven analytics solutions, is expanding its portfolio to include predictive models for various industries. As the company scales, ensuring data governance has become crucial to maintain compliance with regulations (like GDPR) and to optimize model performance across diverse datasets.
Dataset Description
| Feature Group | Count | Examples |
|---|
| Data Quality Metrics | 5 | completeness, consistency, accuracy, timeliness, validity |
| Compliance Flags | 3 | GDPR_compliance, data_access_rights, data_retention_policy |
| Performance Metrics | 4 | model_accuracy, model_latency, resource_usage, data_drift |
- Size: 10,000 records of AI project data, 12 features
- Target: N/A (focus on governance practices)
- Class balance: N/A
- Missing data: 10% missing in compliance flags, 5% in performance metrics
Success Criteria
- Develop a comprehensive data governance framework that ensures compliance and optimizes model performance.
- Document best practices for data quality assessment, access rights management, and performance monitoring.
- Create a report that outlines potential risks associated with poor data governance.
Constraints
- The framework should be adaptable to various AI project types and scalable as the company grows.
- Must comply with industry regulations and standards.
- Should not significantly increase operational overhead.
Deliverables
- A detailed data governance framework document.
- A presentation outlining key governance practices and their importance.
- A risk assessment report related to data governance failures.
