High-quality, well-managed data is the foundation of effective AI systems. This panel explores practical approaches to preparing data for AI deployment, including strategies for handling inconsistency, monitoring data quality, and using synthetic data to improve system robustness. Andrea Er will share insights from her extensive work designing scalable data pipelines and AI solutions for urban technology platforms, highlighting how thoughtful data architecture supports reliable AI deployment.

Key Discussion Points:

• Choosing Clear Goals, KPI & the Right Tech Stack: How can organisation set well-defined outcomes & implement the right platforms for success
• Avoiding Data Silos & Reducing the Monitoring Workload: What are the best practices to ensure system interoperability & minimise the need for human intervention?
• Optimising Workflows, Standards & Practices: What are the right approaches for companies to build a solid foundation to reduce data risks and enable its business for scalable AI success?