Common Elements of Data Quality in the Age of AI

In the era of AI, data quality has become a critical factor for successful implementations. A study by MIT Sloan Management Review found that 85% of executives believe AI will offer significant competitive advantages, yet only 39% have an AI strategy in place. This gap highlights the importance of foundational elements like data quality. Jessica Talisman’s focus on taxonomies, thesauri, and metadata quality aligns with best practices in information architecture for AI. Her emphasis on structured data for AI systems shows the importance of well-organized data in improving machine learning model performance. Watch the session to learn her recommendations and tactics for continuous improvement and incremental addressing of metadata challenges, reflecting the iterative nature of AI development and deployment.