Stream: Vale
Time: 15:15 - 16:00
Organizations are discovering that AI initiatives fail for the same reasons analytics and modernization projects struggled before them: data arrives too late, data definitions drift over time, core system data remains difficult to access, and nobody fully understands how information moves across the enterprise. This session explores the practical data challenges that emerge when organizations attempt to operationalize AI using data from mainframe, distributed, and cloud environments. Through real-world examples, attendees will learn: • Why stale data creates hidden risk for AI and analytics initiatives • How latency, trust, and explainability are connected • Why data pipelines become increasingly difficult to scale • How change data capture and event-driven architectures help reduce operational friction • What organizations are doing to create a stronger foundation for AI without rebuilding everything
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Tommy is a Chief Technologist with 30 years of experience in the IT industry. He is passionate about building strong relationships with customers, partners, and alliances to deliver innovative, outcome-driven solutions across the region. By bridging technology and business needs, he focuses on exploring emerging trends and creating value through strategic collaboration.
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