Big Data & Analytics Summit Canada | Day 2:
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 data peers and colleagues over a delicious breakfast.
- Gather practical insights, share best practices, and set a clear direction for the day ahead.
8:45 am
OPENING COMMENTS FROM YOUR HOST
Gain insight into today’s sessions so you can get the most out of your conference experience.
9:00 am
OPENING KEYNOTE
The State of Data and AI in Canada: Current Constraints, Maturity Gaps, and the Road Ahead
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.
9:30 am
INDUSTRY EXPERT
Bridging the Knowledge Gap: Enhancing AI Understanding Across Your Organization
There remains a significant gap in understanding AI’s impact on business processes among general employees, posing a barrier to the successful implementation of AI projects. Foster a culture of AI literacy within your organization to empower employees and strengthen the effectiveness of AI initiatives. Source practical tips to:
- Assess current employee understanding of AI and identify training needs.
- Develop effective training programs to enhance AI literacy across all levels of the organization.
- Promote a collaborative environment where employees feel comfortable engaging with AI technologies and contributing to project success.
Improve AI literacy to unlock its full potential within your organization.
10:00 am
ROUNDTABLES: DISCOVER THOUGHT-PROVOKING IDEAS
Take a deep dive down the innovation rabbit hole in one of our roundtable discussions. Share common challenges and best practices with your big data and analytics peers on a topic of your choosing:
- Prototypes to Production Pipelines
- Managing Data Quality at Scale in an AI-Driven World
- Balancing Privacy, Consent, and Innovation (AIDA, PIPEDA, Provincial Laws)
- Cross-Functional Data Product Teams: What’s Working and What’s Not
- Building a Culture That Supports Both Rigour and Experimentation
- Selecting and Rationalizing Tools in a Multi-Vendor, AI-Evolving Ecosystem
10:50 am
EXHIBITOR LOUNGE: VISIT BOOTHS & SOURCE EXPERTISE
- Explore the latest big data and 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.
11:30 am
PANEL
From Pilots to Production: How Canada’s Tier 1 Banks Are Building AI-Ready Data Foundations for Enterprise-Scale Decisioning
AI has moved beyond experimentation, but scaling it across a complex banking environment remains a fundamentally unsolved challenge. As expectations shift from insights to real-time, AI-driven decisioning, banks are being forced to rethink the role of data as a foundational layer for execution, not just analysis. Navigate the transition from isolated pilots to production-grade AI systems that can be trusted to inform and automate critical decisions. Walk away with a blueprint to :
- Align data platforms, governance frameworks, and operating models to enable AI systems to move from experimentation into enterprise-wide deployment
- Operationalize data as a real-time, context-rich layer that supports AI-driven decisioning rather than retrospective reporting
- Overcome fragmentation across legacy systems, business units, and data domains to create a unified foundation for AI at scale
- Embed trust, observability, and control into AI systems without slowing down innovation or time-to-value
Prioritize investments across data infrastructure, quality, and integration to unlock measurable business impact from AI initiatives
11:30 am
PANEL
Interoperability, Transparency, and Trust: Overcoming Data Fragmentation in Public Service Delivery
Public-sector and mission-driven organizations face a unique challenge: delivering seamless, equitable, and trustworthy services across systems that are deeply fragmented, historically siloed, and governed by strict privacy mandates. As expectations for transparency and real-time responsiveness rise, agencies must find ways to share data responsibly, coordinate missions, and build public confidence — without compromising security or citizen rights. Develop a blueprint to:
- Drive Interoperability in complex ecosystems: designing data-sharing frameworks across agencies, jurisdictions, and legacy systems without violating mandates, such as CPPA, PHIPA/FOIPPA, or sector-specific statutes.
- Foster transparent, accountable data use: embedding auditability, lineage, consent, and explainability to demonstrate responsible handling of citizen and community data.
- Mitigate fragmentation through modern architecture: leveraging APIs, data fabric layers, canonical data models, and real-time event-driven integration to enable coordinated service delivery.
Advance citizen trust by making it a core operating principle to design AI-assisted decisions that are explainable, auditable, and responsible.
11:30 am
PANEL
Moments That Matter: Using Real-Time Intelligence to Personalize Every Customer Interaction
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.
11:30 am
PANEL
Operationalizing IoT and OT Data at Scale: Solving Integrity, Latency, and Integration Challenges Across Industrial Environments
As manufacturers race to modernize their plants, the surge of IoT sensors, industrial controls, and OT systems has created massive data volumes. Most of it is noisy, siloed, or too latency-sensitive for traditional data pipelines. Build reliable, real-time data foundations. From edge-to-cloud architecture decisions and protocol translation (OPC-UA/MQTT) to digital twin enablement and AI-driven predictive maintenance, operationalize OT and IoT data without disrupting production. Master the success factors to:
- Ensure data integrity at the edge: filtering noise, handling sensor drift, and validating time-series accuracy.
- Optimize architectures for low-latency industrial analytics: determining when to compute at the edge, cloud, or on-premises.
- Converge IT and OT data safely: overcoming incompatible protocols, historian lock-in, and network segmentation.
- Enable digital twins and predictive maintenance with cleaner, more consistent data.
Optimize industrial operations by integrating IoT and OT data to enhance decision-making and reduce downtime.
12:00 pm
CASE STUDY
Breaking the Silos: How One Regulated Enterprise Unified Sensitive Data for Real-Time AI Without Violating Compliance
Many regulated organizations struggle to operationalize AI because their most valuable data— claims, patient records, transactions, investigations, KYC files, and risk assessments —remains locked in siloed, high-security systems with strict access controls and residency constraints. Deliver real-time AI and analytics by building a secure, compliant controlled-access data layer — without moving or exposing sensitive data. Achieve a step-by-step action plan to:
• Implement a zero-copy, privacy-preserving architecture using technologies like secure data virtualization, in-place compute, and tokenized joins.
• Enable real-time model inference on protected datasets through enclave-based processing, private endpoints, and fully auditable access paths.
• Reconcile conflicting regulatory requirements across CPPA, AIDA, HIPAA/PHIPA, EMR constraints, OSFI B-13, and internal security policies.
• Automate policy enforcement and dynamic data masking to ensure analysts, data scientists, and AI systems only see what they are allowed to access.
• Reduce data delivery times from months to minutes while maintaining full lineage, risk classification, residency guarantees, and auditability.
Transform the patient experience with scalable AI to deliver insights faster while maintaining compliance and security.
12:00 pm
CASE STUDY
One Citizen View: How a Public Agency Unified Identity, Consent, and Case Data to Transform Service Delivery
Public sector organizations are under growing pressure to deliver seamless, personalized, and equitable services — yet citizen data is often scattered across disconnected case systems, departmental silos, legacy registries, and incompatible standards. Without a unified view of individuals and households, agencies struggle with inefficiency, duplication, inconsistent decisions, and declining public trust. Build a privacy-preserving, cross-agency citizen data layer — enabling better decisions, faster service delivery, and dramatically improved transparency. Adopt best practices to:
• Create a unified identity and case-data graph, resolving discrepancies across decades-old systems while respecting PHIPA/FOIPPA, CPPA, and sector-specific statutes.
• Implement consent-aware data sharing, where citizen permissions dynamically shaped what information can be accessed, by whom, and for which purpose.
• Establish a secure, real-time interoperability layer using APIs, event streams, and standardized schemas to connect health, social, housing, justice, and benefits systems.
• Use explainable analytics to support consistent, auditable decisions in eligibility, triage, and benefits processing.
Enrich public service delivery by integrating identity, consent, and case data to enhance responsiveness and fairness.
12:00 pm
CASE STUDY
From Batch to Instant: How One Enterprise Built a Real-Time Customer Intelligence Layer That Transformed CX and Reduced Churn
Many customer-centric organizations still rely on batch data, overnight refreshes, and siloed systems — making it impossible to respond in the moments customers actually need help, show intent, or signal frustration. This case study highlights how one large Telco made the leap to real-time customer intelligence, driving measurable gains in churn reduction, NPS, and revenue. Create a roadmap to:
• Build a single, low-latency customer state store, integrating streaming events, CRM data, behavioural signals, and operational data with sub-second freshness.
• Replace batch decisioning with a real-time next-best-action engine, enabling proactive interventions during key high-friction moments (billing, outages, cart abandonment, and call-centre intent detection).
• Implement consent-aware identity resolution, ensuring personalized actions remain compliant with CPPA, CASL, and industry data-handling rules.
• Leverage real-time feature engineering to power AI models for churn prediction, dynamic offers, anomaly detection, and personalized service recovery.
Heighten business outcomes by moving from batch to real-time decisioning to increase revenue and strengthen trust.
12:00 pm
CASE STUDY
Architecting Voice AI Agents for Connected Fleets with MCP & Skills
Organisations are rapidly advancing AI agents from experimental use cases into mission-critical operational environments. Legacy architectures, rigid workflows, and limited scalability are constraining the performance and adaptability of voice-driven systems in real-world settings.
Re-architects voice AI agents using modular, skill-based frameworks and MCP to enable scalable, real-time interactions across connected fleet environments. Demonstrates how shifting from monolithic designs to composable architectures improves flexibility, performance, and ongoing evolution of agent capabilities. Develop a blueprint to:
- Design modular, skill-based architectures for scalable voice AI agents
- Transition from legacy agent designs to flexible, composable frameworks
- Integrate voice agents with connected fleet systems and real-time data streams
- Improve performance, adaptability, and lifecycle management of AI agents in production
Driving operational efficiency, real-time decision-making, and scalable automation in connected environments.
12:30 pm
AI You Can Trust: Operationalizing Observability, Risk, and Compliance in High-Stakes AI Environments
Organizations in highly regulated and data-sensitive sectors are rapidly deploying AI across critical decisions, yet many lack the visibility and controls needed to ensure models remain accurate, fair, and compliant over time. As AI systems evolve in production, risks such as model drift, bias, performance degradation, and regulatory exposure can emerge silently—undermining trust and creating significant operational and legal consequences. Scaling AI in these environments requires continuous monitoring, auditability, and governance frameworks that provide transparency without slowing innovation. Strengthen AI reliability and compliance with practical approaches to:
- Monitor model performance, drift, and bias continuously to detect risks early and maintain consistent, reliable outcomes in production environments.
- Establish auditability and traceability across data, models, and decisions to meet regulatory requirements and strengthen governance controls.
- Embed AI observability into operational workflows to ensure models remain compliant, explainable, and aligned with evolving business and policy standards.
Deploy AI with confidence by building transparent, accountable systems that reduce risk, strengthen compliance, and sustain trust at scale.
12:30 pm
From Maps to Mandates: How Geospatial Data is Powering Smarter Urban Planning Across Ontario
Urban planning decisions increasingly depend on the ability to understand how people, infrastructure, and environments interact across space and time. Across Ontario, public sector organizations are using geospatial data to guide critical decisions on housing, infrastructure investment, climate resilience, and population growth. Yet turning spatial data into operational insight requires overcoming significant challenges, including fragmented data across jurisdictions, inconsistent standards, privacy considerations, and the technical complexity of scaling spatial analytics. Strengthen planning and policy outcomes with practical approaches to:
- Integrate geospatial data across municipal and provincial systems to create a unified view of infrastructure, population dynamics, and service needs.
- Apply advanced spatial analytics to model urban expansion, optimize resource allocation, and support sustainable and equitable community development.
- Operationalize geospatial intelligence by addressing data quality, governance, and technical infrastructure requirements for real-time decision-making.
Transform geospatial insight into actionable policy by enabling data-driven urban planning that improves resilience, efficiency, and long-term public value.
12:30 pm
Voice to Value: Scaling Speech and Voice AI for Customer Experience in Regulated Industries
Retail, telecom, utilities, and media organizations manage millions of customer conversations daily, yet voice remains one of the most difficult data types to operationalize at scale. Delivering reliable speech and voice AI requires more than transcription—it demands real-time processing, high accuracy across accents and environments, conversational intelligence, and seamless integration with enterprise systems, all while meeting strict regulatory and privacy requirements. As organizations automate customer interactions and reduce call centre costs, the challenge is building voice platforms that are fast, contextual, secure, and production-ready. Unlock value from customer conversations with practical approaches to:
- Deploy real-time speech recognition and conversational AI that handle diverse accents, noisy environments, and low-latency processing while maintaining high accuracy and compliance.
- Extract actionable intelligence from voice interactions through transcription, sentiment analysis, and conversational analytics to improve service quality, agent performance, and customer outcomes.
- Integrate voice data into enterprise workflows and customer platforms to enable automation, personalization, and scalable CX operations across regulated environments.
Turn every customer conversation into measurable business value by improving service efficiency, reducing operational cost, and delivering more responsive customer experiences at scale.
12:30 pm
From Cloud to Plant Floor: Scaling Edge AI and Industrial Compute for Real-Time Operations
Industrial and operational environments are generating unprecedented volumes of data from sensors, machines, and production systems, yet much of this data remains underutilized due to latency constraints, connectivity limits, and the cost of centralized processing. As manufacturers and infrastructure operators seek real-time insight, predictive maintenance, and autonomous operations, the focus is shifting toward edge AI and specialized hardware deployed directly at plant and site level. Delivering reliable AI in these environments requires purpose-built compute, efficient model execution, and seamless integration with operational systems. Unlock real-time operational intelligence with practical approaches to:
- Deploy edge AI and specialized compute architectures that process sensor and machine data locally to enable low-latency decision-making and autonomous operations.
- Optimize AI workloads for industrial environments using purpose-built hardware that improves performance, energy efficiency, and scalability at plant and site level.
- Integrate edge intelligence with enterprise systems to support predictive maintenance, quality control, and operational optimization across distributed infrastructure.
Turn industrial data into immediate operational value by enabling faster decisions, greater efficiency, and more resilient production environments.
1:00 pm
NETWORKING LUNCH:DELVE INTO INDUSTRY CONVERSATIONS
- Meet interesting speakers and pick their brains on the latest data 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.
1:30 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.
1:45 pm
CASE STUDY
Building Credibility with Executive Teams Through Balancing Short- and Long-Term Data Value Creation
Establishing trust and credibility with executive leadership is crucial for data teams seeking to drive strategic initiatives. Data leaders must decide between delivering immediate, tangible value through iterative projects and investing in foundational transformations that yield long-term benefits. Find the right balance for your organization, educate executives, and manage expectations with the board. Source practical tips to:
• Develop a roadmap that prioritizes both short-term wins and long-term data foundation enhancements.
• Communicate the ROI of data projects effectively to executive teams, helping them appreciate the art and science of data.
• Foster a collaborative environment where executives feel empowered to both challenge and support data leaders.
Improve your culture of data-driven decision-making to align with, and be led by, executive leadership.
1:45 pm
CASE STUDY
Driving Innovation Through Strategic Vendor Partnerships in Data and Analytics
In today’s rapidly evolving data landscape, managing vendor relationships effectively is vital for fostering innovation and adopting cutting-edge technologies. Navigating an intricate eco-system of vendors that provide platforms and capabilities at scale while meeting unique organizational needs requires specialized skills in influence, co-design, and partnership. Achieve a step-by-step action plan to:
• Influence vendors to align their product development pathways with your organizational needs.
• Facilitate collaboration between vendors to drive interoperability and critical functionality.
• Harness external expertise to introduce innovative solutions that enhance data capabilities and analytics outcomes.
Transform your organization’s data and analytics innovation journey through strategic vendor partnerships to maximize impact and adoption.
2:15 pm
CASE STUDY
Closing the Execution Gap: Leadership and Change Strategies to Operationalise AI
Organisations are investing heavily in AI but continue to struggle to translate capability into measurable business impact. Competing priorities, overloaded teams, and organisational friction are preventing tools from being adopted and used effectively in day-to-day workflows.
Reduces execution friction by embedding AI into existing workflows and aligning initiatives to real operational needs. Establishes leadership approaches and cultural frameworks that enable teams to adopt tools confidently, experiment safely, and prioritise high-impact use cases. Develop a blueprint to:
- Eliminate friction by integrating AI into existing workflows and daily routines
- Enable adoption across teams through clear guardrails and practical use cases
- Shift from theoretical insights to tools that deliver immediate time savings and impact
- Build a culture of experimentation that supports continuous improvement and learning
Driving productivity, workforce adoption, and measurable business impact from AI investments.
2:15 pm
WORKSHOP
Building Bridges Over Uncertain Waters: Preparing the Next Generation of Data Talent for an AI-Driven Workforce
Data leaders across industries are increasingly aligned on one concern: many data and analytics graduates arrive with strong theoretical foundations but lack the applied skills needed to deliver value in real organizations. At the same time, AI is rapidly absorbing entry-level analytical tasks—automating data cleaning, basic reporting, and even model development—reshaping what “job-ready” truly means. Confront the growing disconnect between education, employment, and technological change and gain practical approaches to:
- Redesign employer-led programs around real-world data problems, modern toolchains, and decision-making contexts rather than isolated technical exercises.
- Build stronger employer–university partnerships that embed internships, live datasets, and production environments into learning pathways.
- Equip students to work with AI from day one, using automation to accelerate impact while focusing human effort on judgment, context, and value creation.
Build durable bridges—ensuring graduates are employable, organizations are productive, and the next generation of data professionals is prepared to thrive in uncertain waters.
3:15 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.
3:30 pm
PANEL
Why Most AI Investments Don’t Deliver, and Why More Governance Won’t Fix It
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.
4:00 pm
FIRESIDE CHAT
Redesigning Leadership for the AI Era: From Fragmented Roles to a Unified Intelligence Mandate
Organizational structures are failing to keep pace with the speed of AI. Traditional models—where data, security, and technology leadership sit across separate mandates—are creating friction, slowing execution, and limiting the ability to scale innovation. Built for control rather than agility, these structures are increasingly misaligned with the demands of AI-driven organisations. A new leadership model is emerging as a natural evolution of the CIO/CDO/CSO structure—one that consolidates accountability across data, security, and innovation into a single mandate. Re-design leadership structures for the AI era
- Consolidate fragmented leadership roles into a unified mandate that accelerates decision-making and AI delivery
- Identify and break down the core organizational constraints across finance, compliance, HR, and operations that limit innovation at scale
- Evolve leadership structures to align with emerging mandates across risk, finance, and enterprise intelligence
Unlock speed, alignment, and enterprise-wide impact through next-generation leadership design.
4:30 pm
CLOSING KEYNOTE
Streaming Everything: How Real-Time Data, Event Meshes, and Autonomous Pipelines Will Power the Next Generation of AI
Real-time, event-driven architectures are transforming how organizations collect, process, and act on data. By unifying operational data, analytics, and AI workloads on a single real-time backbone, enterprises can move from batch decision-making to continuous intelligence, enabling faster insights, automated interventions, and predictive outcomes. Source your plan of action by:
- Building low-latency, event-driven pipelines that connect IoT, transactional, and business data streams.
- Implementing real-time feature stores and streaming ML workflows for predictive and autonomous decisioning.
- Designing self-healing data pipelines with observability, alerting, and automated remediation.
- Integrating streaming systems with existing data lakes, warehouses, and analytics platforms.
- Preparing for the next generation of AI-driven automation and agentic systems with streaming-first design principles.
Advance continuous intelligence across your organization to unlock real-time insight and AI-driven autonomy.
4:30 pm
CLOSING COMMENTS FROM YOUR HOST
Review the key solutions and takeaways from the conference. Source a summary of action points to implement in your work.
5:00 pm