Big Data West Summit | Day 1:

Gain insight into today’s sessions so you can get the most out of your conference experience.

Organizations have invested heavily in cloud, analytics, automation, and modernization initiatives, yet still struggle to align architecture, governance, talent, and business priorities. As AI capabilities evolve, your competitive advantage needs to be fine-tuned. Adopt best practices to: 

• Align data, analytics, and AI investments directly to enterprise growth and operational priorities. 

• Perfect and design operating models that integrate engineering, governance, analytics, and AI. 

• Adopt modernization initiatives that deliver measurable business value while remaining adaptable to future AI. 

• Master organizational structures that reduce fragmentation and accelerate enterprise-wide adoption. 

Transform your disconnected data initiatives into scalable enterprise execution. 

As organizations move toward AI-driven decision-making and autonomous operations, static reporting environments are no longer sufficient. Build architectures capable of delivering trusted intelligence continuously and in real time. Walk away with a framework to: 

• Achieve real-time data pipelines across cloud, edge, and enterprise systems. 

• Improve trust through observability, lineage, monitoring, and automated quality controls. 

• Reduce operational bottlenecks that slow production analytics and AI deployment. 

• Excel event-driven architectures that support low-latency enterprise intelligence at scale. 

Turn your real-time intelligence into a measurable operational advantage. 

Traditional scaling approaches are becoming increasingly unsustainable as technical debt, operational complexity, and talent shortages continue to grow. Transform how your teams operate through automation, AI copilots, workflow optimization, and platform engineering strategies. Dramatically improve delivery velocity and operational efficiency. Create a roadmap to: 

• Reduce repetitive engineering and analytics work suitable for automation and AI augmentation. 

• Achieve AI-assisted workflows that accelerate development, testing, and operational delivery. 

• Increase output while protecting team wellbeing and reducing organizational friction. 

• Optimize operating models to maximize efficiency across engineering, analytics, and governance functions. 

Scale your enterprise delivery without scaling operational complexity. 

Fragmented metadata, inconsistent business definitions, weak lineage, and unclear ownership are becoming critical blockers to operational AI adoption. Establish a shared context layer which supports explainable, governed, and reliable AI-driven actions. Source practical tips to: 

• Master semantic context and shared business meaning across enterprise data ecosystems. 

• Adapt enterprise data foundations for agentic workflows and AI-powered automation. 

• Improve governance, explainability, and trust in autonomous decision-making systems. 

• Align metadata, lineage, and ownership models to support scalable AI execution. 

Enable intelligent systems to operate within your trusted enterprise context. 

While data mesh has become a widely discussed architectural concept, enterprises are still struggling to move from theory to practical implementation. Transform how your business operates across regulated environments where compliance, lineage, security, and trust remain critical. Develop a blueprint to: 

• Achieve decentralized operating models without losing governance, quality, or accountability. 

• Advance domain ownership across engineering, analytics, and business teams. 

• Improve and scale delivery capabilities while reducing operational fragmentation and governance risk. 

• Enrich platform standards, security controls, and shared services across distributed teams. 

Create scalable autonomy without sacrificing your enterprise trust. 

Organizations attempting to modernize are discovering that migration alone is not enough. Competitive advantage now depends on your building architecture, which is designed for adaptability, operational speed, governance, and continuous intelligence. Develop a blueprint to: 

• Heighten modernization priorities across fragmented enterprise data ecosystems. 

• Perfect and migrate legacy environments without disrupting operational continuity. 

• Optimize and design future-ready architectures that support AI, automation, and real-time analytics. 

• Reduce technical debt while improving agility, scalability, and governance. 

Replace your technical debt with competitive advantage. 

Data engineering teams are being asked to deliver more pipelines, higher-quality data, faster analytics, and scalable AI support while operating under growing capacity constraints. Manual maintenance, fragmented tooling, and operational firefighting slow your delivery and consume valuable technical resources. Source practical solutions to: 

• Advance manual engineering workflows suitable for automation and orchestration. 

• Improve reliability through alerting, monitoring, logging, and automated quality controls. 

• Reduce operational overhead while increasing delivery speed and consistency. 

• Bolster technical teams to focus on architecture, innovation, and business value creation. 

Transform your data engineering into a scalable automation engine. 

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!