Jithindas Ramadas

Poor upstream quality sabotages AI efforts, compliance, and customer experience; reactive fixes simply inflate cost and time resources. Equally, bad quality downstream flow hinders data team efforts by forcing endless cleaning loops, ad-hoc pipeline patches, and manual fixes every time a report or model misfires. Before organisations can even explore safe and efficient use of AI, data architecture and quality need to get in check. Proactive, contract-level standards are essential, and only CDOs that get their data in line will unlock true value. 

•  Learn how to embed uniform data-quality service level agreements into every vendor and internal business unit contract.
• Use critical-data-element catalogues to assign clear ownership and accountability.
• Discover how to automate quality tests, replace manual sampling, and streamline quality.