Data Forward: AI and Data Management News

Merv Adrian, former Gartner analyst, BARC Fellow and CEO of IT Market Strategy joins Data Forward to discuss how organizations can prepare their data for AI innovation. Drawing on insights from a comprehensive survey of 335 AI leaders, Merv shares what distinguishes "High Readiness" companies, the critical role of data observability in ensuring trustworthy AI, and the essential steps to address obstacles like skill shortages and budget.

Ashwin Rajeeva, Co-founder and CTO of Acceldata, shares his expertise on next-generation data management architectures. He discusses the shift to modern, cloud-native frameworks and the critical balance between structured and unstructured data management. Ashwin emphasizes the need for enterprises to adopt hybrid strategies while upskilling their workforce to navigate evolving challenges. He also highlights the importance of data observability and reassesses traditional metrics of data quality in the context of AI-generated content.

Malcome Hawker, Chief Data Officer at Profisee and host of the CDO Matters podcast, offers transformative insights on data management. He emphasizes building customer-centric data organizations and the crucial role product managers play. The conversation covers shifting towards enabling governance practices and using advanced technologies for data observability. Malcome also explores the significance of active metadata and the future of data contracts, including the prospects of blockchain in data monetization.

Industry luminary Ray Wang joins Data Forward to discuss how the breathtaking evolution in technology and innovation is impacting data management and AI.

Rohit Choudhary, CEO and expert in enterprise data and AI trends, shares invaluable insights on transforming data challenges into opportunities. He discusses the critical shift in the roles of Chief Data Officers and data engineers toward proactive data management. Choudhary elaborates on the cloud transformation journey and the necessity for robust data observability. He highlights emerging AI use cases, particularly in healthcare, and the urgent need for data leaders to embrace AI fundamentals for future success.