Session Outline
1. Data: The Foundation of AI Success
- Why AI projects fail: the data factor
- Data quality dimensions explained
- The relationship between data, analytics, and AI
- Your role in organisational data quality
2. Understanding Your Data
- Types of data: structured, unstructured, and everything between
- Where organisational data lives
- Data ownership and stewardship
- Common data quality issues and their impact
3. Hands-On: Exploring a Dataset
- Introduction to exploratory data analysis
- Identifying patterns and anomalies
- Spotting quality issues in practice
- Tools and techniques you can use
4. Data Preparation Essentials
- Cleaning and transformation basics
- Handling missing and inconsistent data
- Documentation and metadata
- Preparing data for AI applications
5. Data Governance Fundamentals
- Why governance matters
- Policies and standards in practice
- Privacy and security considerations
- Building a data-aware culture
6. Action Planning
- Assess data quality in your area
- Identify improvement opportunities
- Develop personal commitments
- Share plans with the group
Outcomes for Participants
- Understand why data quality determines AI success
- Assess data readiness for AI applications
- Apply basic data exploration techniques
- Contribute to organisational data quality improvement