Big Data & Analytics Summit Canada | Day 1:
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 Loblaw Enterprise AI Journey
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.
10:15 am
INDUSTRY EXPERT: AGENTIC AI
The Context Gap: Providing AI Agents with Trusted, AI-Ready Data
As AI agents take on more responsibility across the enterprise, organisations are confronting a hard truth: AI is only as reliable as the data behind it. Distributed systems, inconsistent governance, and poorly contextualised data are making it difficult for agents to interpret information accurately, act with confidence, and deliver trusted outcomes. Without access to governed, context-rich, and AI-ready data, agentic AI initiatives risk stalling before they can create measurable business value. Build a practical data foundation that gives AI agents the trusted context they need to make reliable decisions, automate complex workflows, and scale safely across the enterprise. Walk away with a framework to:
- Connect distributed enterprise data into a trusted, AI-ready foundation for agentic systems
- Strengthen governance, lineage, and access controls so agents can operate with confidence
- Add business context and semantic meaning to enterprise data to improve agent reasoning and decision-making
- Reduce risk, improve reliability, and accelerate the path from AI experimentation to production
Driving trusted automation, faster decision-making, and scalable AI value through context-rich enterprise data.
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
The Agentic Paradox: Why More Autonomy Demands More Control
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.
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
Invisible Risk, Real Impact: Confronting Data Debt Before AI and Analytics Fail
Modern AI and analytics platforms now underpin mission-critical business decisions and customer experiences at scale. Yet many organizations face a growing, largely invisible threat: data debt. Unlike outages or broken pipelines, data debt accumulates quietly—dashboards still refresh, models still run, but data quality steadily erodes. The result is declining model performance, missed KPIs, reduced customer trust, and AI investments that fail to deliver expected value. Draw on the experience building and operating large-scale data and machine learning platforms in high-growth, data-intensive environments to:
- Identify early warning signals of data debt across data pipelines, analytics, and machine learning systems before business outcomes degrade.
- Redesign data quality, observability, and ownership models to prevent silent failures at scale.
- Align technical teams and business leaders around shared accountability for data reliability, trust, and long-term AI performance.
Build resilient, trustworthy data ecosystems that protect customer outcomes, accelerate innovation, and ensure AI and analytics investments scale with confidence—not risk.
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
INDUSTRY EXPERT
Engineering AI Governance at Scale: Building a Logical Data Layer for Secure, Compliant, and High-Velocity GenAI
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.
3:00 pm
INDUSTRY EXPERT
Building Trusted Analytics and a Unified Metrics Layer for AI-Driven Decision Making
Organizations are under pressure to deliver faster insights and AI-powered decision support — yet many still struggle with inconsistent KPIs, fragmented reporting, dashboard sprawl, and limited trust in data. As AI-assisted analytics and decision intelligence expand, establishing a unified and governed metrics foundation has become essential to ensure accuracy, consistency, and business confidence. Design trusted analytics environments by:
- Creating a unified metrics and semantic layer that provides a single source of truth across dashboards, reports, and AI-driven analytics.
- Enabling AI agents and augmented analytics tools to generate reliable predictive and prescriptive insights from trusted data.
- Implementing governance, version control, and testing practices to ensure metric consistency and long-term trust.
- Deliver self-service analytics capabilities while maintaining data quality, standardization, and business alignment.
Build a trusted analytics foundation that transforms fragmented reporting into consistent, scalable, and AI-enabled decision intelligence.
3:00 pm
WORKSHOP
Operationalizing Trusted Data: Real-Time Quality, Governance, and Policy Enforcement in Modern Data Platforms
As organizations scale AI, analytics, and data sharing initiatives, ensuring data quality, trust, and regulatory compliance has become a critical business priority. Yet many enterprises struggle with fragmented data ownership, inconsistent quality controls, evolving regulations, and limited visibility across increasingly complex data ecosystems. Operationalize data governance by:
- Implementing real-time data quality monitoring, validation, and anomaly detection across streaming and batch data pipelines.
- Establishing automated data lineage, cataloging, and observability to improve transparency, auditability, and trust.
- Apply policy-as-code and governance-by-design frameworks to enforce regulatory requirements such as GDPR, CPPA, AIDA, and PIPEDA at scale.
- Build governance operating models that define ownership, stewardship, and accountability across the data lifecycle.
- Enable automated remediation and self-healing workflows to proactively manage data issues and reduce operational risk.
Strengthen enterprise data foundations by embedding quality, governance, and compliance into every stage of the data lifecycle — enabling trusted insights, responsible AI, and regulatory confidence.
3:00 pm
WORKSHOP
Operationalizing Enterprise AI: Monitoring, Governance, and Lifecycle Management for Reliable, Responsible AI
As organizations scale machine learning, generative AI, and autonomous decision systems, ensuring reliability, transparency, and governance across the AI lifecycle has become a critical business challenge. Many enterprises struggle to move beyond pilots, manage model risk, maintain performance over time, and ensure AI systems remain aligned with business goals and regulatory expectations. Operationalize AI in production by:
- Implementing continuous monitoring of AI models for drift, bias, hallucination risk, and performance degradation across production environments.
- Establish evaluation frameworks that benchmark models against evolving business outcomes and operational KPIs.
- Build automated retraining and lifecycle management workflows using feature stores, orchestration tools, and MLOps pipelines.
- Govern generative AI and AI agents through explainability, transparency, and human oversight mechanisms.
- Create enterprise operating models for managing AI risk, accountability, and ongoing value realization.
Transform experimental AI initiatives into reliable, scalable, and trusted enterprise capabilities that deliver measurable business impact.
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
ROUNDTABLES
- Killing Projects Without Killing Morale
As organizations rationalize AI and analytics portfolios, leaders must sunset underperforming initiatives without eroding trust or damaging innovation culture. Discuss practical approaches for stopping work strategically while maintaining team momentum and executive credibility. - Who Owns Enterprise AI Risk?
AI risk spans legal, compliance, security, engineering, and business teams. Explore where accountability should sit, how responsibilities should be distributed, and what governance structures actually work in complex enterprises. - Measuring AI ROI in Ambiguous Environments
AI value is often indirect, cross-functional, and long-term. Share frameworks for defining, measuring, and defending AI investments when traditional ROI models fall short. - Centralization vs Federation: What Actually Works?
Many enterprises are rethinking operating models as they scale AI. Compare real-world experiences with centralized platforms, federated models, and hybrid approaches, and examine where each succeeds or fails. - Defending Foundational Investment Under Economic Pressure
When budgets tighten, foundational data platforms are often scrutinized. Discuss how leaders justify long-term infrastructure investment while still delivering short-term business wins. - Redefining Data Leadership in the Durability Era
As AI moves from experimentation to enterprise capability, the role of the data leader is evolving. Explore how leadership expectations, stakeholder alignment, and executive influence are shifting in this new phase.
4:00 pm
ROUNDTABLE
From Investment to Impact: Unlocking ROI from Data and AI Through Mindset, People, and Structure
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
4:00 pm
WORKSHOP
Making AI Systems Practical: Semantic Caching and Relearning Loops for Modern Data Platforms
Organisations are under pressure to move AI from experimentation into reliable, production-grade systems that deliver consistent value. Latency, escalating compute costs, and inconsistent outputs continue to erode performance and limit scalability.
Applies semantic caching to minimise redundant model calls and accelerate response times. Embeds relearning loops that continuously improve outputs using feedback, usage patterns, and evolving data. Develop a blueprint to:
- Reduce latency and inference costs through intelligent semantic caching strategies
- Continuously improve model performance using feedback-driven relearning loops
- Integrate optimisation techniques into modern data platforms and MLOps pipelines
- Deliver consistent, high-quality outputs across production AI environments
Driving operational efficiency, scalable AI performance, and accelerated time-to-value.
4:00 pm
From Data to Decisions: Building Agents Without Code
Organisations are accelerating the shift from dashboards to decision automation, seeking faster ways to operationalise AI across business functions. Skills gaps, complex tooling, and long development cycles continue to slow the deployment of intelligent, decision-making systems.
Enables the creation of AI agents using no-code and low-code platforms to automate workflows and decision-making. Connects agents to enterprise data sources, APIs, and business logic to deliver real-time, context-aware actions. Develop a blueprint to:
- Build and deploy AI agents without deep technical expertise
- Connect agents to enterprise data, systems, and workflows
- Automate decision-making across high-impact business processes
- Scale agent-based solutions securely across the organisation
Driving agility, faster decision-making, and scalable automation across the enterprise.
4:30 pm
INDUSTRY EXPERT
From Data Strategy to AI Governance: Building Trusted, Risk-Ready, and Value-Driven Data Organizations
As organizations accelerate AI adoption and analytics transformation, many struggle to align data strategy, governance, and risk management with business value. Fragmented data initiatives, unclear ownership models, and emerging AI risks create barriers to scaling innovation while maintaining regulatory compliance and operational trust. To succeed, enterprises must establish integrated data strategies that balance transformation, governance, and responsible AI deployment.
Strengthen your enterprise data capability by learning how to:
- Define enterprise data strategies aligned to business outcomes that accelerate analytics and AI adoption while driving measurable value.
- Establish modern data governance operating models that clarify ownership, stewardship, and accountability across the organization.
- Implement AI risk management frameworks to address model risk, regulatory requirements, and responsible AI practices.
Build a future-ready data organization that balances innovation with control — enabling trusted AI, resilient governance, and sustainable analytics transformation at enterprise scale.
4:30 pm
INDUSTRY EXPERT
From Data Chaos to Trusted Foundations: Scaling Enterprise Data Quality, Observability, and Governance
Poor data quality remains one of the biggest barriers to reliable analytics, AI adoption, and enterprise decision-making. Inconsistent records, fragmented master data, and limited visibility into data health undermine operational performance, regulatory compliance, and business trust. As organizations scale data platforms and AI initiatives, data quality must evolve from reactive cleansing to proactive, governed, and observable enterprise capability. Strengthen your data foundation by:
- Implementing automated data profiling and cleansing to improve accuracy, consistency, and completeness across enterprise data pipelines.
- Establish enterprise matching and identity resolution to create trusted master data and eliminate duplication across systems.
- Embed data observability and monitoring frameworks to continuously track data health, detect anomalies, and prevent downstream failures.
- Integrate governance, stewardship, and quality controls into analytics and AI workflows to ensure compliance and trust.
Elevate data quality from a technical function to a strategic enterprise capability that enables trusted analytics, scalable AI adoption, and sustained business value.
4:30 pm
WORKSHOP
From Dashboards to Decisions: Using Ontology to Power Trusted AI Agents
As organisations race to operationalise AI, many are discovering that analytics alone is not enough. Business intelligence can surface valuable insights, but when enterprise data lacks shared meaning, AI agents struggle to interpret context, act consistently, or make decisions aligned with business rules. The result is fragmented intelligence, unreliable automation, and stalled AI adoption. Ontology provides the missing semantic layer—defining core business concepts, relationships, and rules in a way both humans and machines can understand. By creating a governed enterprise knowledge model that connects concepts like customer, product, and order directly to operational data, organisations can move from passive reporting to intelligent action. Walk away with a framework to:
- Define enterprise concepts, relationships, and business rules using ontology to create shared organisational context
- Connect ontologies to distributed data sources to enable consistent reasoning across business domains
- Equip AI agents with trusted business context to drive more accurate decisions and autonomous actions
- Bridge the gap between BI insights, operational workflows, and enterprise AI execution
Turn fragmented data into context-aware intelligence that powers trusted automation, faster decisions, and scalable AI execution.
4:30 pm
WORKSHOP
Data Mesh vs. Data Fabric: Architecting AI-Ready Organisations
Organisations are rethinking data architecture to support scalable, production-grade AI and faster decision-making. Centralised bottlenecks, inconsistent data quality, and limited ownership continue to slow the transition to AI-ready operating models.
Compares Data Mesh and Data Fabric approaches to enable scalable, governed access to high-quality data across the enterprise. Defines how each model supports AI use cases through data products, interoperability, and real-time access patterns. Leave with a blueprint to:
- Select the right architectural approach based on organisational structure and AI maturity
- Establish data ownership, governance, and accountability across domains
- Enable seamless data access and interoperability for AI and analytics use cases
- Accelerate the delivery of AI-ready data products at scale
Driving agility, data accessibility, and faster AI adoption across the enterprise.
5:00 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!
5:15 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