Interested in joining us as a speaker at the next Enterprise Data Summit?

Enterprise data teams are moving from building dashboards to powering AI agents, real time decisions, and mission critical products. At large scale, that work is shaped by platform choices, governance, cost pressure, and the realities of operating complex data systems inside enterprises.

Enterprise Data Summit is a focused day for leaders who are modernizing AI ready data platforms inside large organizations. It is where enterprise data engineers, platform and analytics leaders, AI and ML teams, and partners in finance, risk, and security compare notes on how to ship reliable data and AI systems at scale.

We are looking for concrete, in the trenches perspectives from:

• Data and analytics engineering leaders

• Platform and architecture teams

• AI, ML, and LLM platform owners

• FinOps and cloud cost leaders

• Governance, security, and risk or compliance owners

Talks should focus on what is working, what is hard, and what you would do differently next time, not product pitches.

Examples of areas that are a strong fit for submissions:

• Designing AI and agent ready data platforms, including lakehouse, streaming, and vector or retrieval layers

• Using generative AI to accelerate data and analytics engineering while keeping quality, lineage, and controls in place

• Data governance and AI governance as the control plane for trust, access, and regulatory compliance

• FinOps for data and AI, including unit economics, chargeback models, and practical cost controls at petabyte scale

• Operating models for enterprise data teams: platform teams, data products, contracts, and self service at scale

• Data and AI reliability and observability in production, including real incidents and what changed after them

• Hybrid and multi cloud data strategies, data sovereignty, and regionalization in heavily regulated environments

• Moving from AI pilots to an enterprise AI platform backed by durable data foundations

If you are leading or building enterprise scale data and AI systems and can share specific architectures, decision points, tradeoffs, and outcomes, we would like to hear from you.