7:45 am
NETWORKING BREAKFAST: BUILD COMMUNITY CONTACTS
- 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.
8:45 am
OPENING COMMENTS FROM YOUR HOST & ICE BREAKER
Gain insight into today’s sessions so you can get the most out of your conference experience.
9:00 am
C-SUITE PANEL
Beyond the Hype: Building Data and AI Strategies That Deliver Value Today While Positioning for Tomorrow
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.
9:30 am
SPEED NETWORKING: MAKE MEANINGFUL CONNECTIONS
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.
9:45 am
KEYNOTE
The Canadian Tire Enterprise AI Journey: Where the Rubber Hits the Road
Canadian Tire 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 Canadian Tire, 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.
10:15 am
INDUSTRY EXPERT
Building Trustworthy Agentic AI Through Unified Data and Context
Agentic AI can only deliver real business value when it operates on a unified data and governance foundation. Without it, AI agents risk providing inconsistent answers, exposing sensitive data, and driving poor decisions. Organizations relying on fragmented silos or ad hoc integrations inevitably face compliance risks and a loss of trust. Develop a blueprint to:
- Ensure every AI-driven decision aligns with security, privacy, and regulatory mandates.
- Eliminate data silos and deliver consistent, explainable outcomes across business functions.
- Scale agentic AI initiatives from pilots to enterprise-grade deployments that accelerate automation and decision-making.
Improve your ability to operationalize AI in ways that responsibly drive measurable outcomes.
10:45 am
EXHIBITOR LOUNGE: VISIT BOOTHS & SOURCE EXPERTISE
- 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 private meetings for personalized advice.
11:15 am
KEYNOTE
Modernizing the TD Data Foundation
TD’s data teams have spent the past several years navigating the reality that advanced AI use cases can’t scale on top of decades-old data pipelines, inconsistent metadata, and fragmented operational stores. This modernization journey replaced brittle legacy pipelines with governed, scalable data products, unlocking the bank’s next wave of AI capabilities without compromising security, regulatory compliance, or operational resilience. Master the success factors to:
- Drive event-driven ingestion, migrate legacy workloads into cloud-native Lakehouse architecture, and roll out a unified enterprise semantic layer that standardizes definitions across credit risk, fraud, and customer analytics domains.
- Automate data lineage with OpenLineage-compatible tooling and operationalize data contracts to reduce upstream breakages.
- Execute AI-powered transaction categorization, near real-time AML anomaly detection, and large-scale NLP models that process advisor notes and call centre transcripts.
Impact how you modernize your data foundation to unlock faster, smarter AI-driven decisions.
11:45 am
INDUSTRY EXPERT
Empowering Innovation: Democratizing AI and Analytics with Low-Code/No-Code Platforms
Low-code and no-code platforms are transforming data and AI capabilities. They enable employees across various roles to build, deploy, and manage analytics models without extensive technical expertise, which is key to competitive advantage. Drive democratization, accelerate innovation, and enable a broader range of employees to leverage data-driven insights. Adopt best practices to:
- Identify use cases where low-code/no-code solutions can maximize team productivity and speed up deployment.
- Implement workflows that simplify data integration, model building, and visualization for non-technical users.
- Foster a culture of data-driven decision-making by empowering employees across all departments with accessible AI tools.
Achieve innovation at scale to drive accessible AI adoption across your organization and accelerate business growth.
12:15 pm
PANEL
From Pilot to Production: Operationalizing MLOps and Scalable Data Pipelines
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.
12:45 pm
NETWORKING LUNCH:DELVE INTO INDUSTRY CONVERSATIONS
- Meet interesting speakers and pick their brains on the latest industry issues.
- Expand your network and make connections that last beyond the conference.
- Enjoy great food and service while engaging with your big data and analytics colleagues.
1:45 pm
EXHIBITOR LOUNGE: VISIT BOOTHS & WIN PRIZES
- Browse through different sponsor booths and test drive innovative technology.
- Enter your name for a chance to win exciting prizes.
- Take advantage of event-specific offers and exclusive content.
2:00 pm
CASE STUDY
From Legacy to Next-Gen:
Migrating to Hybrid Architectures While Maintaining Reliability
Energy organizations today face pressure to modernize their IT and data infrastructure without disrupting critical operations. Many legacy systems were not designed for cloud-scale, hybrid integration, or AI-driven workloads, creating risks around reliability, performance, and maintainability. Create a roadmap to:
- Design and implement hybrid cloud architectures that balance on-premises systems with cloud scalability and flexibility.
- Transition monolithic applications and databases to modular, service-oriented designs while preserving operational continuity.
- Enhance observability, monitoring, and automated testing frameworks to ensure consistent performance and uptime.
Bolster your cloud agility and hybrid capabilities without compromising reliability, performance, or regulatory compliance.
2:00 pm
CASE STUDY
Transforming BI: Reducing Dashboard Debt, Standardizing Metrics, and Preparing for AI-Assisted Analytics
Many organizations struggle with sprawling BI environments: dozens or hundreds of dashboards, inconsistent metrics, and outdated reports that erode trust and slow decision-making. At the same time, the rise of AI-assisted analytics is changing expectations for insight generation and data democratization. Walk away with an action plan on:
- Eliminating dashboard debt, reducing redundancy, and improving clarity for decision-makers.
- Standardizing key metrics and KPIs across business units to ensure consistency and reliability.
- Enhancing data governance and cataloguing practices that support reproducible and auditable analytics.
- Preparing the BI environment for AI-assisted analytics, including augmented insights, natural language queries, and predictive dashboards.
Reduce complexity and inconsistency across your BI environment to unlock clearer, faster, AI-enhanced insights.
2:00 pm
CASE STUDY
Boosting Practical Governance: Building Trust and Compliance While Enabling AI Innovation
Organizations increasingly rely on AI models deployed across hybrid cloud environments, but scaling AI responsibly requires governance frameworks that maintain the tightrope. Mismanaged AI pipelines risk bias, non-compliance with AIDA, CPPA, GDPR, and PIPEDA, as well as operational inefficiencies. Implement practical, scalable governance while unlocking AI value. Take away specific solutions to:
- Define data stewardship and ownership across multi-cloud, data lake, and warehouse environments to enforce accountability.
- Apply policy-as-code and automated compliance checks to ensure adherence to AIDA, CPPA, and GDPR.
- Implement bias detection, model explainability, and ethical guardrails in AI workflows.
- Integrate MLOps pipelines with governance controls for continuous monitoring and auditing.
Adapt your governance and AI practices to balance compliance, trust, and innovation at scale.
2:00 pm
CASE STUDY
Enterprise AI Deployment at Bell:
What Actually Works and How to Scale Beyond Point Solutions
Many organizations pilot AI solutions successfully but scaling beyond isolated proofs of concept remains challenging. Fragmented pipelines, siloed data, governance gaps, and lack of operational rigor often prevent AI from delivering enterprise-wide impact. Update your AI strategy with lessons from Bell. Achieve a step-by-step action plan to:
- Move from point solutions to enterprise AI platforms with integrated MLOps and model orchestration.
- Leverage feature stores, model registries, and automated CI/CD pipelines for reproducible and scalable deployment.
- Embed regulatory compliance, bias mitigation, and explainability throughout the AI lifecycle.
- Align cross-functional teams across data engineering, AI, and business units for operational adoption.
Transform your isolated AI wins into enterprise-level impact while scaling responsibly.
2:30 pm
PANEL
The Architecture of the Next Five Years: Optimizing Cloud for Cost, Performance, and Real-Time Data
As enterprises scale their data and AI workloads, cloud spend and performance become critical challenges. Without deliberate architectural strategies, real-time data pipelines and event-driven systems can quickly lead to skyrocketing costs, inefficiencies, and performance bottlenecks. Create a roadmap to:
- Design cost-efficient, scalable cloud architectures using serverless, containerization, and auto-scaling strategies.
- Build real-time data pipelines and event-driven systems that maximize throughput while minimizing cloud resource consumption.
- Apply cloud monitoring, observability, and cost management frameworks to track spend and optimize resource allocation.
- Future-proof cloud workloads for evolving business demands and emerging technologies.
Amplify cloud performance and efficiency to deliver real-time data at scale without overspending.
2:30 pm
PANEL
Balancing Descriptive Reporting with Predictive and Agentic Insights
Traditional BI excels at descriptive reporting — showing what happened — but organizations increasingly need predictive and agentic insights that anticipate outcomes and recommend actions. Striking the right balance between historical analysis and forward-looking intelligence is critical to driving timely, data-driven decisions. Source your plan of action by:
- Evolving from descriptive dashboards to predictive models that identify trends and opportunities.
- Integrating real-time and historical data sources to enable seamless predictive and prescriptive insights.
- Managing governance, accountability, and data literacy challenges when scaling AI-driven insights across the organization.
Perfect BI environments by integrating descriptive clarity with predictive foresight to enable faster, smarter, and more confident decision-making.
2:30 pm
PANEL
Governance for the Agentic Age: Controls, Privacy, Transparency, and Human Oversight
As agentic AI — autonomous systems capable of recommendation or action — enters production, traditional governance struggles to keep pace. Enterprises must combine technical controls, regulatory compliance, and human oversight to manage risk. Master the success factors to:
- Implement human-in-the-loop mechanisms for high-risk decisions while enabling autonomous workflows.
- Embed transparent logging, audit trails, and explainability layers to meet AIDA, CPPA, and sector-specific regulations.
- Apply differential privacy, synthetic data, and privacy-preserving ML to protect sensitive information.
- Design automated compliance monitoring pipelines across hybrid and multi-cloud AI deployments.
Amplify agentic AI deployment to drive efficiency and customer personalization while ensuring accountability, privacy, and regulatory compliance.
2:30 pm
PANEL
Agentic Workflows, RAG, and Multimodal AI: What’s Practical and What’s Coming Next
The AI landscape is evolving rapidly, with agentic workflows, retrieval-augmented generation (RAG), and multimodal AI emerging as practical tools for real-world applications. Organizations face decisions on what is feasible today amid tool and cost consolidation versus experimental approaches, and how to govern and operationalize these technologies. Source practical tips to:
- Design agentic AI workflows that balance autonomy with human oversight for operational tasks.
- Implement RAG pipelines for knowledge-intensive applications, combining LLMs with structured enterprise data.
- Explore multimodal AI models that integrate text, image, and structured data for richer insights.
Master your governance, auditability, and bias controls while experimenting with cutting-edge AI to safely scale innovation and drive actionable insights.
3:00 pm
WORKSHOP
Building Resilient, Observable, Automated Data Pipelines with Modern Orchestration Tools
Building pipelines that are reliable, transparent, and scalable is critical — but many teams struggle with operational complexity, failure recovery, and monitoring at scale.
Equip your engineers and data architects with practical techniques to design, implement, and maintain robust data pipelines using modern orchestration frameworks. Develop a blueprint to:
- Design resilient, fault-tolerant pipelines that handle failures gracefully and minimize downtime.
- Automate pipeline deployment, scheduling, and dependency management using modern orchestration tools, such as Airflow, Prefect, or Dagster.
- Optimize pipelines for performance, scalability, and maintainability, supporting real-time and batch workloads in hybrid or cloud environments.
Excel in data pipeline management to scale analytics and AI initiatives reliably and efficiently.
3:00 pm
WORKSHOP
Creating a Unified Metrics Layer That Enables AI Agents and Consistent Reporting
Organizations often struggle with inconsistent KPIs, duplicated metrics, and fragmented reporting across teams — challenges that are amplified when introducing AI-assisted analytics. A unified metrics layer provides a single source of truth, enabling reliable reporting and powering AI agents for predictive and prescriptive insights. Take back to your office strategies to:
- Build a centralized metrics layer that serves both BI dashboards and AI-driven analytics workflows.
- Enable AI agents and augmented analytics tools to leverage clean, trusted data for predictive and prescriptive insights.
- Implement version control, testing, and governance practices to maintain metric accuracy over time.
Optimize your data workflows to turn fragmented metrics into a trusted single source of truth for AI and BI.
3:00 pm
WORKSHOP
Implementing Real-Time Data Quality and Policy Enforcement in Modern Data Stacks
Modern enterprises operate streaming pipelines, event-driven architectures, and cloud-native data stacks, yet real-time data quality enforcement and regulatory compliance remain major challenges. Poor quality data can compromise AI, analytics, and regulatory adherence. Operationalize data quality and policy enforcement in real time. Adopt best practices to:
- Implement streaming validation, anomaly detection, and automated data lineage tracking in tools like Apache Kafka, Apache Flink, and Snowflake.
- Apply policy-as-code frameworks to enforce CPPA, AIDA, GDPR, and PIPEDA compliance automatically across pipelines.
- Build observability dashboards for data quality, completeness, and drift in real time.
- Integrate self-healing and remediation workflows to correct data anomalies automatically.
Enrich your data governance to convert fragmented metrics into reliable, enterprise-wide insights.
3:00 pm
WORKSHOP
How to Monitor, Evaluate, and Retrain Models for Reliability and Future Adaptability
Enterprise AI is only as effective as its ongoing monitoring, evaluation, and retraining practices. Without robust observability and feedback loops, models degrade over time, fail to adapt to new data, or breach compliance and ethical requirements. Gain best practices for operationalizing AI reliability and adaptability. Walk away with an action plan on:
- Implementing real-time model monitoring for drift, bias, and performance decay using tools like MLflow, Evidently, and Prometheus.
- Establish continuous evaluation pipelines to benchmark models against evolving business metrics.
- Apply automated retraining workflows with feature stores, orchestration tools, and CI/CD for AI models.
- Integrate explainability, fairness checks, and regulatory compliance audits into retraining processes to meet AIDA, CPPA, and GDPR requirements.
Increase your model transparency to convert complex AI models into explainable, auditable systems.
3:30 pm
EXHIBITOR LOUNGE:ATTEND VENDOR DEMOS & CONSULT INDUSTRY EXPERTS
- Enjoy exclusive sponsor demos and experience the next level of big data and analytics innovation firsthand.
- Meet one-on-one with leading solution providers to discuss organizational hurdles.
- Brainstorm solutions and gain new perspectives and ideas.
4:00 pm
CASE STUDY
Designing a Future-Ready Data Operating Model: Ownership, Funding, Talent, and AI-Enabled Workflows
Organizations are under pressure to extract maximum value from data and AI while navigating complex governance, talent shortages, and fragmented funding models. Without a future-ready operating model, even advanced technologies fail to deliver sustained business impact. Create a roadmap to:
- Define data ownership and stewardship structures that clarify accountability across business units, data domains, and AI initiatives.
- Align funding and investment strategies to support both foundational data capabilities and AI experimentation.
- Build cross-functional talent models that integrate data engineers, data scientists, analytics translators, and AI specialists effectively.
- Embed AI-enabled workflows into business processes, leveraging automation, decision support, and predictive insights.
Heighten resilience and agility to enable your organization to adapt quickly, respond to change, and maximize the value of data and AI.
4:00 pm
ROUNDTABLE
Closing the Strategy-Execution Loop: Driving Actionable Data and AI Outcomes
Many organizations struggle to translate long-term strategic vision into actionable initiatives, leaving data and AI projects fragmented, underfunded, or misaligned with business goals. Bridging the gap between strategy and execution is critical to delivering measurable value while preparing for future growth. Explore practical approaches for aligning current priorities with future ambitions. Source your plan of action by:
- Translating strategic data and AI goals into executable programs and measurable outcomes.
- Balancing short-term initiatives with long-term capability building, including technology, talent, and governance.
- Implementing decision-making frameworks and accountability models that connect day-to-day operations with enterprise strategy.
- Prioritizing investments in data platforms, AI workflows, and analytics capabilities to drive both immediate value and future readiness.
- Fostering a culture of adaptive planning and continuous feedback to ensure strategy evolves with emerging technologies, regulatory shifts, and market dynamics.
Heighten collaboration and accountability to turn insights from peers into actionable organizational improvements.
4:00 pm
CASE STUDY
Data Fabric, Data Mesh, and Beyond: The Next-Gen Data Architecture Playbook
Enterprises are evolving beyond traditional monolithic data warehouses, adopting data mesh, data fabric, and hybrid architectures to meet demands for agility, scalability, and real-time analytics. Yet implementing these next-gen approaches presents challenges around data ownership, governance, interoperability, and tooling. Achieve a step-by-step action plan to:
- Design data mesh frameworks that decentralize ownership while ensuring consistent governance and interoperability.
- Implement data fabric layers to unify distributed data sources, providing seamless access across cloud, on-premises, and hybrid environments.
- Apply modern orchestration, streaming, and metadata management tools to enable real-time, event-driven pipelines.
- Integrate governance, compliance, and observability into next-gen architectures to ensure trust, security, and regulatory alignment.
Excel in data integration and observability to turn hybrid and distributed architectures into future-ready capabilities.
4:00 pm
WORKSHOP
4:30 pm
CLOSING COMMENTS FROM YOUR HOST
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!
5:00 pm
EVENING RECEPTION:ENJOY GREAT CONVERSATION, MUSIC, & NETWORKING
- Relax and unwind with tasty cocktails after a long day of learning.
- Don’t miss your chance to win fun prizes at our Reception Gift Giveaway.
- Make dinner plans with your new connections and enjoy the best of what Toronto nightlife has to offer. Just be sure to set your alarm for Day 2!
6:00 pm