AI projects often fail due to poor data quality. Tom Barber explores why data integrity is crucial for AI success and how to avoid costly mistakes that lead to unreliable results.

Episode Notes

Key Topics Covered

• The importance of data integrity in AI projects
• Why 'garbage in, garbage out' is critical for LLM success
• Common mistakes leading to expensive AI failures
• How to structure data for better AI results
• The relationship between data engineering and AI effectiveness

Main Points

• Companies are spending $40-50k monthly on AI with poor results due to data quality issues
• Structured data with repeating patterns improves LLM coherence
• Taking time to organize data upfront saves costs and improves reliability long-term
• Data accuracy, completeness, and structure are prerequisites for successful AI implementation

Host Background

• Tom Barber brings data engineering expertise to AI discussions
• Experience in business intelligence and data platform engineering

Action Items for Listeners

• Audit your current data quality before implementing AI
• Map out existing data structures and identify improvement opportunities
• Consider data integrity as a prerequisite, not an afterthought

Have thoughts or questions? Leave them in the comments - Tom reads every one!

Chapters

• 0:00 - Introduction & Setting the Scene
• 0:19 - The Problem: AI Project Failures
• 0:51 - Data Engineering Background & Expertise
• 1:23 - The Garbage In, Garbage Out Principle
• 2:03 - The Cost of Poor Data Quality
• 2:42 - Strategic Approach to AI Implementation
• 4:25 - Action Steps & Wrap-up