Data Summit 2027 delivers a comprehensive 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, Data Governance & Data Products, and Agentic AI & Context Engineering— providing attendees with hands-on insights and implementation strategies from industry experts.
Back by popular demand, the Data & AI Leadership Forum is a dedicated 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 2027 this May 12 – 13 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. Designed for Senior executives leading enterprise data and AI initiatives — including CIOs, CDOs, CDAOs, and other data-driven decision makers. The gap between what your architecture was designed for and what your business now demands keeps widening. Legacy platforms strain under real-time expectations; disconnected systems undercut governance; and every new use case seems to need infrastructure the last one didn't. This track explores the platforms and patterns built to close that gap: data fabric architectures for unified access and governance, data mesh topologies, lakehouse patterns, and composable architectures that accelerate value delivery. Learn how organizations are implementing multi-cloud and hybrid strategies, real-time streaming infrastructures, and modernization initiatives that reduce complexity without stalling the business. Discover how these shifts are reshaping architecture practices and platform team structures and how leading organizations are balancing centralized control with federated autonomy. Attend the Modern Data Architectures track to walk away with practical architectural guidance, key trade-offs, and lessons learned from production deployments at scale. Designed for the 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 Pipelines break in the gaps nobody's watching: a schema shift several systems upstream, a quality check that passes when it shouldn't, or an orchestration job that silently retries in an expensive loop. This track is for the engineers solving those challenges, with battle-tested approaches to orchestration, data observability, and data quality engineering across vendor-neutral solutions. Get hands-on with modern ETL/ELT patterns, change data capture, data contracts, and testing strategies for production pipelines. Learn how leading teams are improving performance, reducing costs, and increasing reliability through CI/CD, infrastructure-as-code, and modern data engineering practices. Explore data engineering for AI/ML workflows, feature engineering, and unstructured data at scale. Attend the Data Engineering track to walk away with real-world lessons and proven principles that outlast any single tool or platform. Designed for the 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 Most governance programs don't fail loudly. They fail quietly, buried in policy documents nobody opens twice. This track is about governance people actually use, built around data products with clear ownership, defined SLAs, and measurable business value. Learn how active metadata, automation, and policy-as-code are transforming governanc, and why data quality is evolving from a hygiene initiative into business-critical infrastructure. Explore master data management, metadata management, data catalogs and internal marketplaces, metric certification, and the operating models that make data product thinking successful, including the growing role of data product managers. As AI and autonomous systems become increasingly dependent on trusted data, examine the accountability frameworks, auditability, lineage, and access controls needed to maintain trust. Attend the Data Governance & Data Products track to walk away with practical strategies for turning governance from a compliance exercise into a strategic advantage. Designed for the Chief Data Officer, Data Governance Manager, Data Product Manager, Data Steward, Master Data Manager, Compliance Officer, Data Quality Manager, Enterprise Data Architect, Analytics Manager, Risk & Compliance Lead, Data Catalog Owner, Metadata Manager An AI agent making decisions on bad definitions is just automating your organization's confusion faster. This track sits at the intersection of the two things that make AI trustworthy at scale: the semantic foundation and the agentic systems built on top of it. Learn how semantic layers, metrics stores, knowledge graphs, and modern analytics platforms deliver the shared business context that agents need. Explore context engineering, semantic modeling, and retrieval optimization techniques that improve AI accuracy, consistency, and trust. Then dive into RAG architectures, memory frameworks, vector databases, agent orchestration, and the practical patterns for taking AI and agentic applications from prototype to production. Attend the Agentic AI & Context Engineering track to walk away with practical strategies for pairing trusted data foundations with agentic AI at scale. Designed for the Analytics Engineer, BI Developer, Business Intelligence Architect, Data Product Manager, Business Analyst, ML Engineer, AI Engineer, MLOps Engineer, Data Scientist, Applied Scientist, AI/ML Architect, NLP Engineer, ML Platform Engineer, AI Solutions Engineer, Research Engineer
Data + AI Leadership Forum
Modern Data Architectures Track
Data Engineering Track
Data Governance & Data Products Track
Agentic AI & Context Engineering Track
