CDAO Canada Public Sector schedule

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know. Have fun! 

• The Data Hoarding Problem - Organizations collect massive amounts of data without purpose, creating costly silos and storage burdens while preventing meaningful insights from emerging across departments. 

• The Transformation Journey - Moving from data collection to data curation requires building organizational data literacy, establishing governance frameworks, and creating a culture that values hypothesis-driven thinking over gut instinct. 

• Evidence-Based Decision Making - The end goal is an organization where data drives strategic decisions through practical tools, processes, and leadership commitment to analytical thinking, supported by clear metrics and continuous improvement. 

In an era where algorithms increasingly drive public services and policy, ensuring that these systems operate fairly, transparently, and inclusively is critical. 

• What operational frameworks are proving effective for managing algorithmic fairness and privacy? 

• How are Canadian agencies applying Bill C-27 and GDPR-inspired practices in real-world contexts? 

• Which tools can proactively detect and mitigate algorithmic bias? 

• How can data teams work with legal, accessibility, and DEI units to operationalize trust? 

Marianela Pereira, Director, Analytic Services – Canada Post  

Jerry Maniate, Associate Professor - University of Ottawa 

Dr. Lyn Sonnenberg, Professor Emeritus - University of Alberta 

Agentic AI is emerging as a powerful tool for improving service delivery, automating workflows, and enhancing decision-making across government. But without strong foundations in data and governance, these initiatives can stall or introduce significant risks. 

This session will explore how government organizations can build the conditions for reliable and trustworthy Agentic AI, with a focus on: 

• Ensuring AI agents inherit appropriate access controls and operate within mission and regulatory frameworks. 

• Addressing the risks of fragmented legacy systems and siloed data. 

• Building a unified, governed data and business context layer to support explainable, consistent, and secure outcomes. 

• Lessons from forward-thinking agencies moving from pilots to scaled AI use cases that deliver measurable public value. 

Digital twin technology is revolutionizing how cities manage and optimize urban planning, infrastructure, and public services. By creating virtual replicas of physical environments, digital twins enable real-time monitoring and predictive analytics that can enhance everything from traffic management to disaster response. How can digital twins improve the efficiency and sustainability of urban infrastructure management? What challenges do cities face when implementing digital twin technology, especially in terms of funding and scalability? 

• How are departments building data capabilities into everyday tools and systems (e.g., HR platforms, budgeting software)? 

• What are examples of “invisible” analytics improving operational decisions — from staffing models to purchasing efficiency? 

• How do you balance automation with human oversight in internal data use? 

• What skillsets and internal partnerships are needed to scale operational analytics? 

• How are success and ROI measured for analytics that don’t have public-facing outcomes? 

• What role does change management play in helping staff adopt a “data-first” mindset? 

Moderator - Karem Elsayed, Senior Advisor Planning & Analysis – Government of Canada   

Xavier Girard, Director of Data – Public Services and Procurement Canada 

• What are the key features of a resilient public sector data infrastructure? 

• How can agencies ensure real-time data access during system disruptions or high-demand events? 

• What role does cloud, hybrid architecture, or decentralized storage play in data continuity? 

• How should governments approach scenario planning and testing for data infrastructure under stress? 

James Lindsay, Senior AI Architect and Engineer - Department of National Defence 

Ian Stewart, Director of Analytic Environment & Data – Bank of Canada   

What does it really take to lead data transformation from a vision to reality? This candid fireside chat features leaders who’ve moved beyond policy mandates to create lasting data-driven change.  

• How are data strategies being translated into measurable outcomes that matter to citizens? 

• What are the most unexpected challenges leaders have faced—and how did they overcome them? 

• How do they balance political expectations with operational realities and citizen demands? 

• What’s the next bold move in data-driven leadership? 

Moderator: Ben Dick, Senior Data Analytics Strategist - City of Ottawa 

Michael Lam, Chief Data Officer - Natural Sciences and Engineering Research Council of Canada (NSERC) 

David Collister, Manager, Data Governance - Fisheries and Oceans Canada 

Nish Anjaria, Vice President Technology Innovation, Strategy & Enterprise Architecture - Canada Post 

As data volumes and computing power grow, predictive analytics is emerging as a transformative force in designing policies that are not just reactive but forward-looking. From forecasting housing demand and social service needs to anticipating public health trends, predictive models are reshaping how institutions allocate resources and deliver value. 

• How can predictive modeling improve policy decisions in areas like social services, housing, and education? 

• What risks must be addressed to avoid overreliance on models in policy? 

• How can a government balance proactive services with reactive accountability? 

• How do we ensure transparency and explainability in predictive models used for public decision-making? 

• What cross-sector partnerships are proving most effective in advancing predictive analytics initiatives? 

Governments hold vast amounts of information that can drive better services, innovation, and collaboration when managed responsibly. This discussion will explore how the public sector can maximize the societal and operational value of data while upholding trust, transparency, and accountability. 

Key Discussion Points: 

• How can governments clearly demonstrate and communicate the public value created from data use? 

• What are the opportunities and challenges in forming responsible public–private data partnerships? 

• In what ways can anonymized or aggregated datasets support innovation and research, while safeguarding privacy? 

• How do data licensing, APIs, and open standards contribute to long-term accessibility, interoperability, and responsible use? 

Moderator: Andrew Churchill, Vice President of Public Sector - QLIK   

Ahmed Shalaby, Executive Director Enterprise Information Management - Service Canada   

Meagan Collins, (A)Chief, Enterprise Information Management, Corporate Services Sector Treasury Board of Canada Secretariat / Government of Canada 

• What challenges or opportunities led to the decision to build centralized service dashboards in your organization? 

• How can public sector teams foster cross-departmental collaboration when developing integrated dashboards? 

• In what ways can data partnerships be leveraged to enhance BI reporting across different service lines? 

• What are the most effective strategies for presenting complex data clearly and impactfully to senior leadership? 

• How do you establish governance structures and build trust in the accuracy and integrity of dashboard data? 

As AI continues to redefine the digital landscape, the path forward for public services requires more than adoption—it demands intentional design, ethical foresight, and strategic collaboration. This session explores how data and AI can be harnessed to build responsive, inclusive, and resilient systems that meet the evolving needs of society. 

💬 Format: Open-dialogue roundtable with moderator-led prompts and participant Q&A. 

• What does a future-ready public service look like in an AI-native world? 

• How can governments shape the ethical and technological standards that will guide society? 

• What role will cross-sector partnerships play in accelerating innovation while safeguarding equity? 

• How can governments future-proof data infrastructure for real-time responsiveness and adaptability? 

• What are the most promising areas for AI to solve systemic public challenges by 2030? 

James Lindsay, Senior AI Architect and Engineer - Department of National Defence 

Michael Mohammed, Manager, Policy & Legislation – Government of Canada 

Adapting fast. Deciding wisely. Collaborating better. 

In today’s unpredictable world, data leaders in the public sector must be ready to respond to complex, fast-moving crises—from natural disasters and public health emergencies to cyberattacks and humanitarian shocks. But how do you lead with data when timelines are short, the stakes are high, and the information is incomplete? 

This closing roundtable brings together cross-sector experts who’ve been at the data frontlines during critical moments. We’ll explore the human, technical, and leadership dimensions of crisis-driven analytics—sharing candid lessons, practical frameworks, and collaborative models for navigating what’s next. 

💬 Format: Open-dialogue roundtable with moderator-led prompts and participant Q&A. 

Key Discussion Prompts: 

• How can data leaders embed agility into their teams and tools before a crisis hits? 

• What real-world examples highlight how analytics shaped decision-making in emergencies? 

• How do we balance speed with governance, ethics, and data accuracy in high-pressure scenarios? 

• What role does cross-government and cross-sector data sharing play during crisis response? 

• How can leaders debrief and build institutional learning after a major disruption? 

• What’s one innovation or lesson from the last crisis that should be institutionalized for the next? 

Ian Stewart, Director of Analytic Environment & Data – Bank of Canada   

Scott McKenzie, Chief Technology Officer - Fort Vermilion School Division 

As public education embraces digital transformation, data systems are playing a critical role in improving student outcomes and operational decision-making. This session will explore the journey of building in-house data infrastructure to enable real-time insights and proactive intervention — while navigating the complex leadership challenges of talent retention in an increasingly AI-driven world. 
 
Key Discussion Points: 

• Building accessible, purpose-driven data systems to support evidence-based decision-making 

• Cultivating internal data leadership in the face of limited public sector resources 

• Addressing the talent gap: strategies to compete with the private sector for data and AI professionals 

• Balancing innovation with sustainability: how to scale data capabilities without over-reliance on external tools 

This session will explore how Global Affairs Canada is leveraging artificial intelligence to transform its Grants and Contributions processes and systems. Maher Mamhikoff will share how AI is streamlining workflows, reducing administrative burden, and supporting faster, data-informed decision-making. He will highlight lessons learned in automating key functions while ensuring transparency, fairness, and responsible AI use. The session will also touch on collaboration across departments and the long-term vision for scaling innovation in funding programs. 

The growing importance of data in government decision-making means that data literacy is a critical skill for civil servants. The goal is to build a data-driven culture that empowers public sector workers to make more informed, evidence-based decisions. We’ll explore models for effective training, the obstacles to embedding a data culture in legacy institutions, and how governments can evaluate the ROI of their data literacy programs to ensure they are meeting objectives and driving real impact. What are the most effective approaches for upskilling civil servants in data analytics and storytelling? What barriers exist when embedding a data-driven culture in legacy institutions, and how can they be overcome? How can public sector leaders measure the effectiveness of their data literacy programs? 

As public sector organizations face growing complexity in health, environmental, and regulatory challenges, data is becoming the backbone of smarter, faster, and more ethical decision-making. This panel brings together leaders from across government science, policy, and data functions to explore how advanced methodologies are reshaping the future of risk assessment. 

Speakers will discuss real-world applications, the infrastructure needed to support innovation, and how collaboration between scientists, data professionals, and policymakers is unlocking a new era of public sector impact. 

Key Discussion Topics: 

• Real-world use cases of data in regulatory science and risk assessment 

• Infrastructure and governance for cross-disciplinary collaboration 

• Lessons learned in integrating advanced modeling and analytics 

• The role of public sector leadership in adopting data-driven innovation 

Moderator:  Dr. Yad Bhuller, Executive Advisor – Health Canada 

Panelists: 

Dr. Marc Avey, Director of Standards - Canadian Council on Animal Care 

Dr. Charu Chandrasekera, Founder & Executive Director - Canadian Centre for Alternatives to Animal Methods 

David K. Lee, Chief Regulatory Officer - Health Products and Food Branch 

Dr. Valerie H. Taylor, Department Chair, Psychiatry - University of Calgary, Departmental Science Advisor, Health Canada 

Dr. Ranjana Sharma, Chief Scientist - Natural Resources Canada, Government of Canada 

Fabio Onesi, Director General of Talent and Workforce Culture Directorate – Corporate Services Branch