Ep. 6: The foundation of AI: keeping your data clean & synchronized

The conversation delves into the critical role of data quality in AI success, highlighting the challenges, readiness, and synchronization of data across systems. It also explores the balance between speed and data quality, as well as the integration of AI within IT architecture. The conversation delves into the challenges of data integration, the importance of continuous data cleansing, and the need for embedding data quality in organizational processes. It emphasizes the significance of starting small, scaling, and building trust in AI implementation. Additionally, it highlights the role of PIM and MDM as part of the AI stack.TakeawaysData quality is crucial for AI successIntegration and synchronization of data across systems is essential Data quality is crucialStart small and scalePIM and MDM are part of the AI stackChapters00:00 The Foundation of AI and Data Quality08:23 Data Readiness for AI15:22 Data Quality and AI Output24:14 Balancing Speed and Data Quality30:14 Integration Challenges36:00 Data Quality and Stakeholder Engagement42:04 Starting Small and Scaling51:09 PIM and MDM as Part of the AI Stack