Big Data & Analytics Summit Canada | Day 2:

• Start your day off right and connect with big data and analytics leaders.
• Get to know your data peers and colleagues over a delicious breakfast.
• Gather practical insights, share best practices, and set a clear direction for the day ahead.

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

Canadian organizations are accelerating AI adoption, yet most still struggle with foundational issues—fragmented data estates, unclear ownership models, talent shortages, rising regulatory pressure, and widening gaps between ambition and operational reality. Many have successful pilots but lack the governance, infrastructure, and cross-functional alignment needed to scale AI safely and economically. Align your organizational strategy with national realities to:

• Diagnose the top maturity gaps in Canadian organizations across data quality, lineage, governance, talent, and model lifecycle management.
• Prepare for the next five years of change—including emerging regulatory expectations, new assurance models, and the shift toward real-time, production-grade AI.
• Walk away with a clear understanding of Canada’s current AI readiness—and a roadmap for closing the most urgent gaps before they become competitive liabilities.

 Drive strategy effectiveness through a clear mapping of the talent, regulatory and competitive landscape.

AI initiatives are stalling not because of model capability, but due to fragmented data architectures, inconsistent governance enforcement, and limited access to trusted, real-time data. Engineering teams are under pressure to operationalise governance in a way that enables—not restricts—AI delivery. Architectural approaches for embedding governance into data delivery layers, enabling real-time, policy-controlled access to distributed data while supporting scalable GenAI workloads. Develop a blueprint to: 

• Design a logical data layer that enforces governance, security, and compliance across hybrid and multi-cloud environments 

• Align data engineering and data science workflows through shared semantic models and governed data products 

• Enable real-time, AI-ready data access without replication, reducing latency while maintaining control 

• Measure and demonstrate the impact of governance on AI delivery speed, reliability, and business value 

Accelerate AI delivery with secure, scalable architecture—balancing innovation, compliance, and performance.

Competitive advantage now hinges on what happens the moment a customer clicks, calls, buys, switches channels, or signals intent. To deliver truly responsive and personalized experiences, organizations must move beyond batch reporting and static segmentation toward real-time, AI-ready decisioning. Integrate real-time analytics, unified customer data, and emerging AI capabilities to elevate every touchpoint — while maintaining trust and privacy. Take away specific solutions to:

• Build a real-time data backbone using streaming pipelines, event-driven architectures, and low-latency feature stores.
• Create holistic, consent-aware customer profiles that align with CPPA, CASL, and sector-specific privacy mandates.
• Apply early AI-driven personalization techniques like contextual recommendations, intent prediction, and behavioural scoring.
• Prepare for agentic personalization, where AI systems autonomously tailor interactions across digital and operational channels.

 Increase revenue channels with transparency, fairness, and explainability to protect customer trust.

Enterprises continue to pour billions into AI, data platforms, and transformation programs,  yet most struggle to translate that investment into consistent, scalable business outcomes. The issue is not lack of capability, but structural dysfunction. In many organizations, data is shaped by incentives, governance slows decisions without improving them, and execution layers override strategy in real time. The result: AI becomes performative,  visible in dashboards, invisible in outcomes.Challenge the dominant narrative that better tools, more data, or stricter governance will solve the problem.Move beyond the illusion of progress by: 

• Confronting how financial pressure, compliance structures, and operational targets distort data and decisions 

• Exposing why “data-driven organizations” still rely on negotiated realities rather than objective signals 

• Understanding how HR and organizational design reinforce control instead of enabling execution 

• Recognizing why AI initiatives fail at the last mile,  where incentives override intelligence 

• Reframing AI as an execution system that requires a different leadership model, not just better technology 

Stop optimizing fragments and start fixing the system AI is expected to operate within. 

As organizations accelerate investment in AI and machine learning, many assume that intellectual property protection is limited because algorithms and models themselves can be difficult to patent. Yet some of the most valuable competitive advantages in AI are created not by the models alone, but through the unique processes, architectures, datasets, workflows, and innovations that surround them. Understanding what can be protected—and when to start thinking about protection—is becoming increasingly important for technical leaders, product teams, researchers, and innovators seeking to create lasting business value from AI investments. Drawing on the real-world experience of developing a multi-patent-pending machine learning solution, this session explores the journey from technical innovation to intellectual property strategy and commercialization. Develop a framework to: 

• Identify potentially patentable innovations within AI, machine learning, data science, and software development initiatives.  

• Understand the practical limitations and opportunities of patents in AI-driven products and services.  

• Evaluate alternative forms of intellectual property protection, including trade secrets, proprietary datasets, process innovation, and evaluation frameworks.  

• Integrate intellectual property considerations earlier into product development, engineering, and innovation lifecycles.  

• Build a defensible competitive advantage by aligning technical innovation, governance practices, and commercialization strategies.  

• Leverage Canadian and provincial programs, funding opportunities, and support resources to protect and scale innovation.  

Transform technical innovation into sustainable business value through a strategic approach to intellectual property in the age of AI. A

Most organizations invest heavily in data platforms, dashboards, and now AI, yet still struggle to generate significant business value. The problem is rarely technology. It is human. Becoming truly data driven begins with mindset by helping people believe that data and AI are there to empower them, not judge or replace them. From there organizations can invest in people, design the right structures, and ultimately unlock meaningful results. Drawing on the experience of scaling a global analytics organization from five people to more than 170, this session shares a practical blueprint for turning data and AI from reporting tools into a true business advantage.Key Takeaways 

• Why mindset is often the missing ingredient in successful data and AI transformations
• How developing confident data and AI users accelerates adoption and impact
• How the right operating model converts analytics and AI into measurable business value 

Review the key solutions and takeaways from the conference. Source a summary of action points to implement in your work.