Part 13/13:

He encourages a paradigm shift—moving from reactive inspection at the end to proactive, continuous assurance throughout the data lifecycle. Change in organizational mindset and process can unlock higher levels of data integrity, ensuring that the systems built on data are as dependable as the trusted machinery in a manufacturing plant.

In essence, the message is clear: To succeed with AI and data-driven initiatives, organizations must treat data management as a manufacturing process—quality and reliability must be engineered-in, not inspected-in after the fact. Only then can data truly serve as a trustworthy foundation for decision-making, automation, and innovation.