Big Data West Summit | Day 1:

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

Organizations have invested heavily in cloud, analytics, automation, and modernization initiatives, yet still struggle to align architecture, governance, talent, and business priorities. As AI capabilities evolve, your competitive advantage needs to be fine-tuned. Adopt best practices to: 

• Align data, analytics, and AI investments directly to enterprise growth and operational priorities. 
• Perfect and design operating models that integrate engineering, governance, analytics, and AI. 
• Adopt modernization initiatives that deliver measurable business value while remaining adaptable to future AI. 
• Master organizational structures that reduce fragmentation and accelerate enterprise-wide adoption. 

Transform your disconnected data initiatives into scalable enterprise execution. 

Agentic AI is rapidly changing how work gets done. Systems that can reason, plan, and act autonomously are increasing individual productivity at unprecedented speed, yet many enterprises are struggling to translate that autonomy into scalable business value. As AI agents proliferate, organizations are running into a familiar tension: centralization provides trust, governance, and control but slows innovation, while decentralization unlocks speed and creativity but fragments accountability, data quality, and outcomes. Agentic AI exposes the limits of both approaches. Resolve the autonomy–control tension with practical approaches to: 

• Design a core operating model that centralizes trust, governance, and data foundations while enabling autonomy at the edge where agents operate. 
• Establish clear control mechanisms for agentic systems, including context inheritance, permissions, and accountability, without constraining innovation. 
• Align data, platforms, and operating models so agentic AI can scale consistently across teams, use cases, and markets. 

Enable agentic AI to deliver enterprise-scale impact by pairing autonomy with strong foundations that provide trust, context, and control where it matters most. 

Healthcare organizations continue to struggle with fragmented patient records, disconnected clinical systems, and incomplete visibility across care journeys. While cancer registries have historically served as important repositories for surveillance and reporting, many remain limited by inconsistent data capture, siloed information sources, and static historical records that restrict their operational value. As healthcare systems pursue more connected, patient-centred models of care, organizations must strengthen foundational data quality while building integrated ecosystems capable of supporting clinical insight, operational planning, and improved patient outcomes. Develop a roadmap for: 

• Strengthening cancer registry data quality through AI-enabled approaches that improve data capture, structuring, validation, and operational efficiency.  
• Integrating clinical and operational data across previously disconnected systems to create a more complete understanding of patient pathways and outcomes.  
• Transforming static registries into dynamic, connected data assets that support real-time insight, planning, and coordinated care delivery.  
• Embedding AI within broader data modernization and workflow transformation initiatives rather than treating it as a standalone technology solution.  

Improve patient-centred care, strengthen clinical decision-making, and create connected healthcare data ecosystems that enable more coordinated and insight-driven outcomes. 

As generative AI moves from experimentation into enterprise deployment, organizations are discovering that AI consumption can scale far faster than the value it creates. Tokens, model choices, inference volumes and increasingly agentic workflows introduce a new layer of technology economics that leaders need to understand. The challenge is no longer simply proving that AI works, but designing and operating AI solutions where the cost of intelligence remains proportionate to the business value it generates. Walk away with a roadmap to: 

• Measure the true cost of AI use across models, tokens, infrastructure and workflows, connecting consumption to individual use cases and business outcomes. 
• Optimize AI economics by selecting the right models, architectures and levels of intelligence for different tasks rather than automatically deploying the most powerful—and expensive—option. 
• Establish governance and accountability for AI consumption so teams can experiment and scale while continuously measuring whether increased spend translates into measurable value. 

Improve AI ROI, control rapidly growing technology costs and scale innovation sustainably by treating AI consumption as an investment that must continuously earn its place. 

For years, some of BCLC’s most important business logic was effectively hidden inside its BI environment. Moving from on-premise infrastructure to the cloud exposed a bigger challenge: modernization required not simply migrating technology, but making critical data logic visible, governed and reusable. Discover how BCLC rebuilt this foundation to support a broader transition toward trusted self-service analytics and AI readiness. Walk away with a roadmap to: 

• Extract and rebuild business logic trapped within legacy BI environments so critical knowledge becomes visible, understandable and reusable across the organization. 
• Establish governance, metadata and ownership around modernized data assets so employees can confidently discover, understand and use trusted information without depending on specialist teams. 
• Use cloud modernization as an opportunity to create a governed self-service data foundation capable of supporting analytics today and increasingly sophisticated AI use cases tomorrow.

Increase analytics productivity, accelerate AI innovation and reduce the cost and risk of legacy complexity by turning hidden business logic into governed, reusable enterprise data. 

Within four months, Gerry Akkerman experienced two crises that fundamentally reshaped his approach to leadership: a severe cardiac event and one of British Columbia’s most significant ransomware incidents. Drawing on his experience leading TransLink’s seven-month organizational response, Gerry will explore what happens when leadership is tested simultaneously by operational crisis and human limits — and why organizational resilience ultimately depends on the resilience of the people responsible for delivering it. Walk away with a roadmap to: 

• Lead decisively in the immediate aftermath of a major cyberattack by establishing clear incident command, communication and decision-making structures when normal operating models no longer work. 
• Sustain teams through a prolonged recovery by managing executive expectations, maintaining organizational focus and recognizing the human impact of operating continuously in crisis mode. 
• Challenge leadership cultures built around overwork and individual heroics, creating healthier and more resilient teams capable of performing when the organization faces its most difficult moments. 

Strengthen organizational resilience, accelerate crisis recovery and protect your most critical people by building leadership practices capable of sustaining both the organization and the humans responsible for it. 

Review the key solutions and takeaways from today’s sessions. Source a summary of action points to implement in your work. Discuss tomorrow’s highlights!