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. 

AI has never been easier to build, yet many organizations still struggle to move promising initiatives into production. The bottleneck is increasingly not the model itself, but the fragmented data, duplicated logic and repeated governance work sitting behind it. Every new use case that rebuilds these foundations increases cost and complexity. Reusable enterprise capabilities can turn one-off AI projects into an architecture designed for scale. Walk away with a roadmap to:

• Build reusable data, governance and business-logic capabilities that can support multiple AI applications rather than rebuilding the foundation for every new use case.
• Standardize how trusted data is discovered, accessed and validated so teams can move from experimentation to production faster while maintaining appropriate controls.
• Design an enterprise architecture that reduces duplication and complexity while allowing new models, agents and AI capabilities to be introduced as technology evolves

Accelerate AI deployment, reduce development costs and increase returns on AI investment by building trusted foundations once and reusing them across the enterprise.

In demanding leadership roles, success is often measured by the challenges we overcome, the innovations we deliver and the people who depend on us. But what happens when the relentless pressure to get things done comes at the expense of our own health and well-being? Drawing on his deeply personal experience of a life-threatening cardiac event while navigating extraordinary professional responsibilities, Gerry shares a powerful and confronting perspective on leadership, resilience and the true cost of an always-on work culture. Walk away with a roadmap to:

• Recognize how the pressure to deliver, manage risk and meet competing expectations can gradually normalize unsustainable workloads and put personal well-being at risk.
• Challenge the leadership mindset that equates commitment with personal sacrifice, exploring how to establish healthier boundaries without compromising accountability or organizational performance.
• Rethink what resilience and success really mean by building teams and leadership practices that protect people, distribute responsibility and sustain performance through periods of extraordinary pressure.

Build healthier leadership cultures, strengthen organizational resilience and sustain long-term performance by recognizing that no professional achievement should come at the cost of your life.

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!