Lilly Tech Systems
Enterprise Data Strategy for AI
Build the data foundations that power successful AI initiatives. Learn how to assess your data landscape, design scalable architectures, ensure quality, and establish governance — the essential pillars of any enterprise AI program.
Your Learning Path
Follow these lessons in order to build a complete enterprise data strategy for AI.
1. Introduction
Why data strategy is the foundation of every successful AI program and what makes enterprise data unique.
2. Data Assessment
Audit your current data landscape, identify gaps, and evaluate AI readiness across the organization.
3. Architecture
Design data architectures that support ML pipelines, real-time inference, and scalable AI workloads.
4. Quality
Implement data quality frameworks, validation pipelines, and monitoring for AI-grade data.
5. Governance
Establish data governance policies, access controls, lineage tracking, and compliance for AI systems.
6. Best Practices
Proven patterns, common pitfalls, and organizational strategies for enterprise data success.
What You'll Learn
By the end of this course, you'll be able to:
Assess Data Readiness
Evaluate your organization's data maturity and identify gaps blocking AI adoption.
Design AI Architectures
Build data platforms that support ML training, feature engineering, and real-time inference.
Ensure Data Quality
Implement validation, monitoring, and remediation pipelines for AI-grade data.
Govern Data at Scale
Establish policies, controls, and compliance frameworks for enterprise AI data.