Big Data West Summit | Day 2:

Gain insight into today’s sessions and the afternoon track structure, so you get the most out of your conference experience. 

Public sector organizations hold enormous volumes of valuable information locked inside documents, forms and legacy records. Extracting this information manually is time-consuming, while conventional automation often struggles to interpret complex content consistently. Drawing on a digitization engine prototyped by Aman Sidhu at the City of Vancouver, explore how ontologies, automated extraction and human review can work together to transform unstructured documents into reliable, machine-readable JSON. Walk away with a roadmap to:

• Use ontologies to define the relationships, entities and business rules needed to extract consistent, structured information from diverse document types.
• Combine automated document processing with human-in-the-loop validation to resolve ambiguity, improve accuracy and maintain confidence in the resulting data.
• Transform extracted information into standardized JSON that can be integrated into operational systems, analytics platforms and emerging AI applications.

Reduce manual processing costs, improve data accessibility and accelerate public sector innovation by transforming previously inaccessible documents into trusted, reusable data assets.

Enterprises increasingly need event-driven architectures that connect operational systems directly to business decisions as conditions change. Real-time analytics is becoming a foundational capability for your organization’s continuous intelligence across operations, customer experience, and automation. Leave with a framework to: 

• Master event-driven analytics pipelines that connect operational, transactional, customer, and machine data streams in real time. 

• Adapt operational systems to decision workflows so insight can trigger action. 

• Achieve low-latency architectures that support streaming analytics, real-time feature engineering, and AI-driven decisioning. 

• Improve reliability through observability, lineage, and automated monitoring. 

Shape your outcomes through continuous intelligence and real-time enterprise execution.

Take a deep dive into your strategy, share common challenges, and exchange best practices with peers working through similar data, analytics, and AI challenges. 

• Synthetic Reality: Building safer AI with synthetic data and privacy engineering. 

• Enterprise Memory: Knowledge graphs and ontologies for trusted AI. 

• Generative AI Beyond Chatbots: Delivering enterprise ROI in production. 

• Self-Service Analytics That Actually Gets Used: Turn data curiosity into business impact. 

• Third-Party AI Risk and Foundation Model Governance: Establish clear oversight of foundation models and third-party AI providers. 

Data initiatives rarely struggle because organizations lack technology, data or technical expertise. More often, value is lost when data teams and the business gradually fall out of alignment — solving the wrong problem, producing outputs that aren’t adopted, or struggling to translate technical capability into measurable outcomes. Drawing on practical experience building data teams and communities, discover how to make continuous alignment the foundation for delivering value. Walk away with a roadmap to: 

• Align people, processes, data and tools around clearly understood business priorities and measurable outcomes. 
• Establish an operating rhythm that keeps data teams connected to stakeholders as requirements, priorities and business conditions evolve. 
• Build a stronger data-driven culture by shifting the relationship between data teams and the business from service provider to strategic partner. 

Move beyond simply delivering data products to build an aligned data organization that consistently turns business problems into measurable value. 

What happens when data professionals apply their expertise to some of society’s most pressing challenges? In this fast-paced Pecha Kucha-style showcase, Data for Good Vancouver shares eight projects demonstrating how practical data, analytics and AI can help non-profits improve services, influence public policy and strengthen communities.

• Family Services of Greater Vancouver: Turning a 49-tab year-end reporting spreadsheet into a more manageable reporting process, then mentoring internal IT staff to simplify and rationalize data flows.
• First United: Using geospatial data to track renovictions, producing evidence that helped influence changes to British Columbia’s renters’ rights legislation.
• Overdose Prevention Society: Transforming daily supervised consumption records into visual evidence of zero recorded deaths in the analyzed logs, alongside surveys that helped identify and understand key stakeholders.
• BC-AI Health Sciences Subgroup: Connecting AI expertise with frontline organizations in Vancouver’s Downtown Eastside to explore how advanced technology can address urgent community needs rather than simply showcase new models.
• Special Olympics BC: Redesigning athlete and volunteer surveys to improve participation, increase response rates and generate more useful feedback.
• Rural Division of Family Practice: Developing a low-cost approach to identifying and tracking shortages of rural doctors, nurses and clinical support staff.
• Awl Together Leather: Measuring how a community of cobblers reduced leather waste and diverted reusable material from landfill.
• earth: Using semantic and ontological modelling to map food distribution across the Fraser Lowlands, identify supply-chain vulnerabilities and reveal potential single points of failure.

Building sophisticated analytics capabilities has traditionally required specialist data scientists, engineers and significant technology investment. But AI-assisted development is beginning to change that equation. Discover how one analyst combined domain expertise, existing real estate data and AI to build a continuously refreshed analytics platform that now informs U.S. market selection and investment decisions — demonstrating how smaller teams can create capabilities that previously required far greater resources. Walk away with a roadmap to:

• Multiply the capabilities of individual analysts by using AI across coding, model testing, debugging and documentation while retaining human oversight and domain expertise.
• Build sophisticated analytics capabilities faster and with fewer resources by extracting greater value from data and technology the organization already owns.
• Translate analytics into investment decisions by designing around specific business questions, identifying market shifts and relative opportunities rather than simply producing more data and dashboards.

Discover how AI is changing the economics of analytics — shifting the constraint from the size of your data team to the quality of the questions your people can ask.

The next wave of winners will not be defined by who bought AI first, but by who built the strongest data, governance, operational, and execution foundations. Decide which capabilities, investments, and operating models will determine long-term competitiveness. Walk away with a strategy to: 

• Identify the capabilities that will define AI maturity over the next three years. 
• Prioritize investment across talent, architecture, governance, automation, and real-time intelligence. 
• Adapt faster than technology changes while maintaining trust, accountability, and execution discipline. 
• Amplify AI strategy to regional strengths across technology, resources, public sector, healthcare, transportation, and industrial innovation. 

Turn your current infrastructure decisions into tomorrow’s competitive advantage for Western Canada’s next generation of AI leaders. 

• Review the key solutions and takeaways from the conference. 
• Source a summary of action points to implement in your work. 
• Close the event with a clear view of the foundational data and AI priorities that will shape the year ahead.