Module 525 minutes

Data Strategy for AI

Build data foundation for AI. Ensure quality, accessibility, and governance.

data-strategydata-qualitydata-governance

✓Assess data readiness for AI
✓Improve data quality
✓Enable data accessibility
✓Implement data governance

AI Is Only As Good As Your Data

Before AI strategy, fix your data strategy.

Data Readiness Assessment

Quality: Accurate, complete, consistent?
Volume: Enough data for AI?
Accessibility: Can AI access it?
Governance: Policies and controls?

Data Quality Improvement

Identify quality issues
Implement validation rules
Automate cleaning processes
Monitor quality metrics
Assign data ownership

Data Integration

Break down silos:

Central data platform
APIs for access
Real-time vs. batch
Metadata management

Data Governance

Policies:

Data classification
Access controls
Retention policies
Usage guidelines

Processes:

Data lineage tracking
Change management
Incident response
Audit trails

Synthetic Data Strategy

When real data insufficient:

Generate synthetic data
Maintain privacy
Test scenarios
Augment datasets

Key Takeaways

→Clean, accessible data is prerequisite for AI
→Implement data governance before scaling AI
→Break down data silos systematically
→Assign clear data ownership
→Use synthetic data when privacy concerns exist

Practice Exercises

Apply what you've learned with these practical exercises:

1.Audit data quality for AI use case
2.Map data silos and integration needs
3.Draft data governance policies
4.Create data quality scorecard

Related Guides

→ Ai Privacy Basics

Learning Objectives