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. 

The strongest data leaders translate complexity into commercial language, connect technical work to enterprise priorities, and help business stakeholders understand risk, opportunity, and value. Your technically advanced teams may continue to struggle because their work is disconnected from their goals and outcomes. Walk away with strategies to: 

• Translate technical work into boardroom outcomes that link architecture, data quality, analytics, and AI investments. 

• Achieve influence across executive stakeholders by communicating risk, value, trade-offs, and investment needs. 

• Amplify data teams as strategic growth partners that solve operational problems. 

• Improve alignment between technical roadmaps, financial performance, customer outcomes, and enterprise transformation goals. 

Turn your technical credibility into organizational influence, executive trust, and enterprise-wide business impact. 

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. 

The next wave of analytics value will come from insight products designed around real workflows, operational accountability, and measurable behaviour change. Rethink analytics delivery through a product mindset focused on usability, workflow integration, and operational outcomes. Get a blueprint to: 

• Transform and design analytics products that drive adoption. 

• Optimize insights directly into frontline workflows, so recommendations, alerts, and performance signals reach teams at the point of action. 

• Increase business usage and accountability through various measures. 

• Reduce dashboard sprawl by prioritizing decision products. 

Turn your analytics into frontline execution, operational accountability, and measurable business performance. 

As organizations scale analytics, automation, and autonomous decision-making systems, data quality must evolve from reactive cleanup into proactive engineering. If your organization fails to prioritize trust foundations, you’ll see reduced adoption, increased operational risk, duplicated effort, and declining confidence. Adopt key practices to: 

• Adapt from reactive cleanup to proactive quality engineering embedded into pipelines, platforms, and data product lifecycles. 

• Master quality metrics tied directly to business KPIs. 

• Improve and scale governance and trust frameworks without slowing delivery. 

• Achieve accountability across producers, consumers, and platform teams. 

• Perfect AI readiness by strengthening the data quality, lineage, and validation practices. 

Create trust in every decision, model, and dashboard by treating your data quality as a core engineering discipline. 

Leading organizations are introducing engineering contracts that define ownership, quality standards, schema expectations, and change management processes. Data contracts are emerging as a foundational mechanism for improving your operation’s trust, scalability, and accountability. Leave with a framework to: 

• Achieve data contracts across engineering, analytics, platform, and business teams. 

• Reduce downstream failures caused by schema changes, unclear ownership, and undocumented dependencies. 

• Improve collaboration between producers and consumers by defining expectations before production. 

• Heighten quality standards and validation rules. 

Build accountability into every data product you ship and protect trust across the analytics and AI value chain. 

Knowledge graphs, semantic layers, and ontologies are emerging as the missing infrastructure required for AI systems to reason across complex enterprise environments. If your organization is seeking trusted AI outcomes, it must move beyond raw data pipelines and establish shared semantic models. Walk away with a plan to: 

• Master semantic models that unify fragmented enterprise knowledge across customers, products, assets, policies, processes, and decisions. 

• Improve explainability and trust in AI outputs by giving systems access to governed business context and relationships. 

• Excel AI agents to reason across business domains. 

• Advance knowledge graphs, ontologies, metadata, and data products. 

Give AI the business context required to make your trusted decisions and scale intelligent automation across the enterprise.

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.