Hack RadarThe Real-Time AI Enterprise: Powered by Clean, Connected, and Trusted Data
- in-person
- paid
singapore conference on building trustworthy AI systems through clean data pipelines and real-time data foundations. in-person in october, aimed at leaders in finance, public sector, and enterprises. tickets are £650+, so this is more exec-facing than a student sprint, but worth knowing about if you're serious about data governance in AI.
the full listing
Turning Continuous Data Foundations into Responsible AI Action AI outcomes are only as trustworthy as the data foundation behind them. Real-time decisioning demands clean, continuous, and fully governed data streams. Real-time intelligence requires real-time trust. Enabling AI agents to act autonomously requires absolute confidence in data lineage, security, and contextual accuracy. “The real challenge is no longer getting more data into AI. It is ensuring that the data reaching AI is accurate, current, trusted and ready for action.” -OpenGov Asia Singapore’s digital economy is increasingly built around connected businesses, digital services and data-driven operations. The scale of information moving across these ecosystems is growing rapidly. For example, the Singapore Trade Data Exchange (SGTraDex) saw its monthly transactions increase from 200,000 to 4.2 million in just two years, an 18-fold increase. However, for leaders across public sector agencies, financial institutions, and Singapore enterprises, speed without integrity is a risk. Deploying AI to automate financial risk assessment, respond to citizen requests, or manage supply chain disruptions requires guaranteed data quality. When bad data enters real-time AI pipelines, incorrect actions happen at scale. From Static Data to Continuous Data Foundations A true real-time AI enterprise moves beyond static repositories to continuous, clean data streams. To shorten the distance between signal, insight, and automated action, organisations must ensure that every piece of data moving through the enterprise is verified, current, and contextualised. The Executive Challenge: High-Stakes Demand High Trust Emerging AI agents promise unprecedented operational autonomy, but an agent is only as competent as its context. This presents a critical leadership imperative: How do you modernise data infrastructure to feed real-time AI while maintaining strict governance, auditability, data residency, and resilience? Key Takeaways: CONNECT real-time data and events with AI systems ENABLE intelligent agents to respond to changing conditions ORCHESTRATE multi-agent workflows across complex environments INTEGRATE AI with existing government systems and applications SCALE event-driven AI for mission-critical operations STRENGTHEN reliability, resilience and operational continuity IMPROVE visibility, traceability and auditability TRANSFORM reactive processes into proactive, intelligent services Who Should Attend: Chief Analytics Officers Chief Data Officers Chief Digital Officers Chief Information Officers Chief Technology Officers Chief Transformation Officers Director and Heads of Applications Directors and Heads of Data Directors and Heads of Analytics and Info Management Directors and Heads of Cloud Architecture Directors and Head of Data Science
source: Eventbrite