Jeney Kleeman Noland Arbaugh’s life changed in a fraction of a second in June 2016. He was a 22-year-old student, working at a kids’ summer camp in upstate New York, when he went swimming in a lake. He can’t tell me exactly what happened, but thinks one of his friends must have accidentally struck him very hard in the side of his head as they ran into the water and plunged beneath the surface. When he woke up face down in the water, unable to move or breathe, Noland immediately knew he was paralysed. But he didn’t panic. He felt…

Suri Nuthalapati Key Takeaways Reliable data is the cornerstone of successful AI-driven decision-making and innovation. Data governance, modern architectures and automation ensure data quality and reliability for AI. AI-powered insights depend on breaking silos, mitigating bias and prioritizing data ethics. As AI transforms various industries, its effectiveness hinges on a single, vital factor: reliable data. Without a solid data foundation, even the most sophisticated AI systems can struggle to deliver results. Data is the lifeblood of AI. Machine learning models, predictive analytics and other AI-driven tools rely on accurate, timely and relevant data to function effectively. Poor-quality data can lead…