Data Summit 2026 delivers a comprehensive and refined learning experience for data and AI professionals at every level of the data and AI organization. Our program features four deep technical tracks—Modern Data Architectures, Data Engineering, Analytics Delivery & Semantic Layers, and GenAI and Applied ML— providing attendees with hands-on insights and implementation strategies from industry experts.
New for 2026—the Data & AI Leadership Forum is an exclusive space for business and technical leaders to explore strategy, governance, responsible AI, and value realization—through interactive sessions, executive roundtables, and peer advisory discussions.
Whether you’re working on the technical side, developing new business strategies, or leading data initiatives, you’ll find the knowledge and connections to help you succeed at Data Summit 2026 this May 6 – 7 in Boston.
At the Data + AI Leadership Forum, visionary leaders share their strategic insights on building data-driven organizations and implementing AI at scale. The focus is on strategic outcomes rather than technical implementation details. From a C-suite perspective, the Forum looks at digital transformation, ROI frameworks, data governance, vendor ecosystem management, data monetization, and data-literate culture creation. Attend the Forum to learn about the intersection of business strategy and data architecture, AI ethics and risk management, and practical approaches to measuring data initiatives' business impact Access to the Data + AI Leadership Forum is included when you register for an All-Access Pass or may be registered for separately. Dive into the future of enterprise data platforms and modern data architectures that involve implementing data fabric architectures for unified data access and governance, migrations from legacy architectures, multi-cloud and hybrid strategies, data mesh topologies, lakehouse patterns, building composable data architectures that accelerate value delivery, and real-time streaming infrastructures. Learn about DataOps, platform team structures, and balancing centralized versus federated approaches. Attend this track to learn about architectural decisions, trade-offs, lessons learned from production deployments at scale, and the developments in modern data architectures. Target Attendees: Data Architect, Enterprise Data Architect, Principal/Staff Data Engineer, Cloud Data Architect, Solutions Architect, Platform Engineer, Data Platform Manager, Technical Lead, Director of Data Engineering, Data Infrastructure Engineer, Big Data Architect, Integration Architect Explore battle-tested approaches to modern data infrastructure. This track covers DataOps implementations, orchestration strategies, data observability, and data quality engineering across vendor-neutral solutions. Learn modern ETL/ELT patterns, change data capture, data contracts, and testing strategies for production pipelines. Sessions address data engineering for AI/ML workflows, feature engineering, and unstructured data at scale. Discover how teams improve performance, reduce costs, and build reliability through CI/CD for data pipelines, infrastructure-as-code, and optimized development workflows. Attend this track to gain insights from real-world implementations and the principles that transcend specific data engineering tools. Target Attendees: Data Engineer, Senior Data Engineer, ETL Developer, DataOps Engineer, Data Pipeline Engineer, Integration Developer, Analytics Engineer, Data Quality Engineer, Stream Processing Engineer, MLOps Engineer, Data Infrastructure Developer, Backend Data Developer Discover how semantic layers, metrics stores, knowledge graphs, and modern BI platforms deliver trusted analytics to business users and reduce analytics chaos. Learn about building business-friendly data models, implementing governance-enabled self-service analytics, and creating reusable metrics that ensure organizational alignment, ensure consistence, and accelerate time-to-insight. Hear presentations on analytics catalogs, business glossaries, metric certification processes, and change management for analytics adoption. Attend this track to learn vendor-neutral patterns for making analytics accessible to non-technical users while maintaining trust and governance. Target Attendees: Analytics Engineer, BI Developer, Business Intelligence Architect, Analytics Manager, Data Product Manager, Business Analyst, BI Team Lead, Data Analyst, Analytics Platform Owner, Self-Service Analytics Lead, Reporting Manager, Analytics Solutions Architect, Metrics & Insights Manager Expand your knowledge about building and deploying production ML and AI systems at scale. Look at GenAI and ML applications from prototypes to production, computer vision systems, recommendation engines, and predictive analytics implementations. Also covered are MLOps pipelines, LLM applications, RAG architectures, traditional ML, and agentic systems—from model development through monitoring, evaluation, and maintenance. Learn real-world patterns for GenAI and ML applications, vector databases, prompt engineering, fine-tuning, and cost optimization. Discover how teams measure impact, handle failure cases, and balance performance with ROI. Attend this track for real-world revelations about AI and ML. Target Attendees: Machine Learning Engineer, AI Engineer, MLOps Engineer, Data Scientist, Applied Scientist, AI/ML Architect, Deep Learning Engineer, NLP Engineer, Computer Vision Engineer, ML Platform Engineer, Principal Data Scientist, AI Solutions Engineer, Research Engineer
Data + AI Leadership Forum
Modern Data Architectures
Data Engineering
Analytics & Semantic Layers
GenAI & Agentic AI
