Big Data & Analytics Summit Canada | Day 1:

• Start your day off right and connect with big data and analytics leaders. 
• Get to know your industry peers and colleagues over a delicious breakfast.
• Source practical tips, discuss best practices, and prepare for the day ahead.

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

Organizations everywhere are racing to build AI capabilities. The real differentiator is the ability to deliver outcomes now while laying a foundation that can evolve with shifting technology, regulations, and business priorities. Many teams struggle with fragmented architecture, unclear business ownership, and innovation efforts that never reach production. Take away specific solutions to:

• Create a durable AI foundation that balances short-term wins with long-term scalability.
• Structure data platforms, governance models, and integration patterns so that future AI capabilities — agentic systems, multimodal models, real-time intelligence — can be adopted without major rework.
• Embed business accountability in data and AI programs, creating a culture where value delivery, not experimentation, becomes the core operating principle.

Optimize your strategy to withstand hype cycles, economic pressure, and evolving AI technologies.

Loblaw is transforming into a fully data-powered enterprise. The path to 2030 demands a fundamentally different approach to how data, intelligence, and innovation are delivered. The rapid rise of agentic AI, real-time personalization, and cross-channel automation is driving real transformation. This shift is prompting long-established organizations to rethink how their data is structured, governed, accessed, and operationalized at scale. Source practical tips to: 

• Rearchitect the data foundation for real-time intelligence, enabling the business to shift from retrospective reporting to operational decision-making powered by streaming, event-driven data. 

• Build a unified governance and access layer that applies consistent security, lineage, and quality controls across thousands of products, services, and channels — this is critical as agentic AI systems inherit permissions and take autonomous actions. 

• Operationalize AI as a core enterprise capability, not a series of disconnected projects. For Loblaw, this includes restructuring product teams, redefining platform ownership, and embedding cross-functional delivery models that shorten deployment cycles. 

Advance your ability to modernize the right layers first to accelerate innovation across the enterprise. 

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.

Many organizations struggle to move AI initiatives beyond proofs of concept, leaving high-potential models stuck in the pilot stage. Challenges such as fragmented data infrastructure, lack of reproducible pipelines, insufficient model monitoring, and unclear governance often prevent teams from scaling AI effectively. Take back to your office strategies to:

• Implement MLOps workflows that enable reliable model deployment, versioning, and continuous improvement.
• Design scalable, robust data pipelines that ensure high-quality, reproducible inputs for AI models.
• Establish model monitoring and performance metrics to detect drift, bias, and operational risks.
• Integrate data governance and compliance practices into AI lifecycle management without slowing innovation.

Heighten your business impact today to build adaptability and governance for tomorrow.

While AI is crucial to the success of organizations around the globe, without trusted data, your strategy may fall flat. A new approach is needed to deliver coordinated oversight across both data and AI, enabling your organization to move from ambition to results at scale. In this session, you’ll learn practical steps for improving AI model effectiveness and reducing data risk. Find out how you can establish a foundation for success with a unified platform that ensures your data and AI are governed, collaborative, and aligned with business goals. Join us to gain valuable insights on how to:
 

• Catalog and manage AI use cases 

• Capture business context and value 

• Ensure legal/compliance details are documented 

• Gain full transparency into data driving AI models 

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.  

• Building scalable data foundations that support longitudinal patient visibility, advanced analytics, and future innovation in healthcare delivery.  

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

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!