Data Analytics Public Sector Summit | Day 1:

Gain insight into today’s sessions so you can get the most out of your conference experience.

• Preview the strategic and technical themes shaping the conference.
• Understand how the tracks fit together.
• Identify the sessions most relevant to you.

Public sector organizations are under pressure to accelerate AI adoption while also navigating evolving regulation, increased public scrutiny, and rising expectations around transparency and accountability. Create a roadmap to: 

• Bolster governance frameworks that enable innovation rather than slow it down. 

• Increase your transparency, explainability, and performance in high-stakes environments. 

• Master organizational alignment across your legal, policy, privacy, and technical teams. 

Deliver quality citizen outcomes by scaling AI with confidence. 

Grow your network by meeting like-minded individuals to share your latest ideas and projects with:

• Enjoy a quick icebreaker, exchange LinkedIn information, and build lasting business relationships.
• Achieve your conference networking goals in a fun and agile fashion.
• Join a community of big data leaders and gain invaluable support.

As government organizations move from AI experimentation towards operational adoption, the challenge is no longer simply proving what AI can do — but creating the conditions to use it responsibly, securely and at scale. Drawing on ESDC’s journey across its departmental AI strategy, AI Register and enterprise data platform, explore why trusted data, effective governance and organizational readiness must evolve alongside AI capability. Walk away with a roadmap to: 

• Strengthen data quality, metadata and governance so AI systems are built on information that is understood, discoverable and trusted. 

• Establish secure access, accountability and transparency frameworks that allow AI adoption to scale while maintaining appropriate oversight and public trust. 

• Build workforce and organizational readiness so responsible AI moves beyond policy and experimentation to become an operational capability across government. 

Move from isolated AI experimentation to trusted enterprise adoption by building the data, governance and organizational foundations required to scale AI responsibly.

• Explore the latest data analytics technology and strategies with our industry-leading sponsors.
• Share your challenges with the biggest innovators in the business.
• Schedule one-to-one meetings for practical advice on your most pressing data, platform, governance, and AI priorities.

Most public sector organizations know they have a data governance gap, but few can point to hard numbers proving what closing the gap is worth. Algonquin College had the answer. Starting from the concept of “governance debt,” the college built a Collibra-powered corporate policy governance program which now saves an estimated 2.5 person-years annually and is quietly becoming the foundation for its next chapter in AI. 

• Learn how a full-lifecycle policy governance program works: linking legislative and ministry changes through to corporate policy and business rules. 

• Using AI to draft policy assessments with a human-in-the-loop model already saves roughly 100 person-days annually. 

• The future with AI and Collibra Maestro is hallucination-resistant AI built on a sound data foundation.  

Government teams are under pressure to share more data faster and with better quality. It’s about shifting away from ad hoc sharing to accountable and reusable delivery. Walk away with a plan to: 

• Amplify your data ownership, service expectations, and accountability. 

• Enhance your critical datasets with quality and usability in mind. 

• Optimize how you reuse data across departments. 

• Excel interoperability and scale by combining governance with product thinking. 

Move from fragmented data sharing to a more reliable and scalable operating model for government data. 

AI models are becoming increasingly interchangeable, but their value depends on the enterprise data and context they can access. The greater challenge lies in connecting legacy databases, spatial and geospatial information, real-time operational systems and sensitive data that cannot leave controlled environments. Make these complex data sources accessible to AI agents without compromising security, governance or operational control. Walk away with a roadmap to: 

• Extend MCP beyond conventional APIs to connect AI agents with legacy databases, GIS, CAD, BIM, IoT and other complex enterprise data sources through governed workflows. 

• Separate access and governance controls from data execution, enabling agents to interact securely with restricted, on-premises and privacy-sensitive information without unnecessarily moving the underlying data. 

• Build interoperable, model-independent MCP capabilities that allow organizations to expose and consume tools across multiple systems while maintaining control over permissions, execution and operational risk. 

Accelerate enterprise AI adoption, unlock the value of previously inaccessible data and reduce integration complexity by securely connecting AI agents to the systems where critical business intelligence resides. 

As public sector organizations move beyond basic chatbots and individual productivity tools, the next challenge is scaling AI agents that can coordinate tasks, execute workflows and support increasingly complex decisions across government. Moving from human-assisted automation to enterprise-wide agentic systems requires more than technology—it demands new operating models, security controls, cost management and workforce capabilities. Explore what it takes to deploy and manage AI agent fleets responsibly at scale. Walk away with a roadmap to: 

• Align your organization’s AI maturity with the appropriate level of agent autonomy, moving from human-assisted workflows towards coordinated, multi-agent systems with clearly defined responsibilities and oversight. 

• Establish secure and accountable agentic operations through automated compliance, access controls, monitoring and governance frameworks that meet public sector requirements. 

• Optimize agent performance and operating costs by matching tasks to appropriate AI models while developing the workforce skills needed to supervise, manage and collaborate with autonomous systems. 

Accelerate public service delivery, reduce operational costs and improve government responsiveness by scaling secure, efficient and accountable AI agents across the enterprise.

Public sector organizations are increasingly exploring AI to improve service delivery, operational efficiency, and citizen outcomes. While many initiatives remain focused on chatbots and content generation, the next frontier is Agentic AI: intelligent systems capable of reasoning, planning, and taking actions within defined governance boundaries. Walk away with a plan to:  

• Execute AI agents that can create measurable value across government operations. 

• Implement key governance, risk, privacy, and compliance considerations. 

• Move from experimentation to enterprise deployment. 

• Balance innovation with responsible AI practices 

Most public sector data is analyzed at the city, municipal or census tract level, but these broad geographic boundaries can overlook the economic and social realities of individual neighbourhoods and commercial streets. Use granular geographic intelligence to inform better local decisions. Walk away with a roadmap to: 

• Combine demographic, economic, business and location-based data to understand the distinct characteristics, opportunities and challenges of individual main streets and commercial districts. 

• Explore lessons from Bloor West Village and Revelstoke to understand how street-level analysis can reveal local patterns that broader municipal and census-level statistics may overlook. 

• Translate granular geographic insights into more targeted economic development strategies, infrastructure investments and community revitalization initiatives. 

Strengthen local economies, improve public investment decisions and build more resilient communities by bringing greater geographic precision to government planning and policy. 

Hospital-acquired conditions including UTIs, delirium, sepsis and pneumonia continue to contribute to longer stays, greater care complexity and poorer patient outcomes. Fraser Health’s Patient Safety Priorities initiative is using AI to identify patients at risk earlier — but its experience also exposed a critical challenge for healthcare AI: even highly accurate predictions create little value if clinicians have to leave their existing workflows to find them. Discover how Fraser Health moved AI from a standalone analytics tool into the clinical workflow to turn prediction into action. Walk away with a roadmap to: 

• Predict emerging patient safety risks earlier by combining structured clinical information with unstructured physician and nursing documentation to continuously identify changes in patient risk. 

• Embed AI-generated insights directly into existing clinical workflows, reducing friction for clinicians and designing around how physicians and nurses actually deliver care rather than expecting them to adapt to another technology. 

• Operationalize predictive AI at scale by bringing together clinicians, data scientists, quality leaders, UX and infrastructure teams to deliver secure, near-real-time insights across complex hospital environments. 

Move beyond building accurate AI models to creating clinical AI that is trusted, adopted and acted upon — shifting patient safety from reactive identification towards proactive prevention. 

Review the key solutions and takeaways from today’s sessions. Optimize essential action points you can start using immediately. Discuss tomorrow’s highlights!