Building a Data Strategy for AI Adoption

Align data management with your AI vision and business needs.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course guides participants through developing a comprehensive data strategy tailored to support Generative AI initiatives. It covers aligning data governance, architecture, and management practices with organizational AI goals. The focus is on ensuring that data assets are leveraged effectively to drive AI innovation while maintaining compliance, security, and ethical standards.

Learning Objectives

Participants will be able to:

• Define a data strategy that supports AI-driven business objectives

• Align data governance policies with AI adoption requirements

• Identify key data sources and architecture needs for GenAI

• Integrate data management best practices for scalability and compliance

• Foster cross-functional collaboration between data and AI teams

Target Audience

• Data strategists and architects

• AI program managers and business leaders

• Data governance and compliance officers

• IT and data infrastructure professionals

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures and case studies

• Strategy development workshops

• Group discussions on data governance and ethics

• Real-world AI adoption scenario exercises

Materials Provided

• Data strategy frameworks and templates

• Sample policy documents and governance models

• AI readiness assessment tools

• Case study summaries

Outcomes

• Clear, actionable data strategy aligned with AI ambitions

• Improved coordination between data and AI initiatives

• Enhanced understanding of governance and compliance impacts

• Practical skills to implement and maintain a scalable data strategy

Outline / Content

Day 1: Foundations of AI-Driven Data Strategy

• Understanding AI business objectives and data needs

• Key components of a data strategy for AI adoption

Day 2: Data Governance and Compliance Alignment

• Designing governance frameworks for AI use cases

• Privacy, security, and ethical considerations

Day 3: Data Architecture and Integration

• Identifying critical data sources and infrastructure

• Ensuring data accessibility and quality for GenAI

Day 4: Implementing and Sustaining the Strategy

• Cross-team collaboration and stakeholder engagement

• Monitoring, adapting, and scaling the data strategy