Forward Data Conference 2026

01 · Data Foundations for Humans & AI

Reliable, accessible data foundations, designed for both human and AI consumers.

AI-Ready Data: source-of-truth modeling, semantic layers, ontologies and knowledge graphs to prepare data for machine consumers

Data Quality & Trust in the Agentic Era: quality, data contracts and observability as the trust infrastructure of AI initiatives

Data Platform Evolution: open table formats by default and the consolidation of lakehouse architectures

Cost-Aware Data Engineering: cost becomes a design constraint for pipelines and warehouses

02 · Agentic Systems Engineering

All the engineering around models: context, memory, evaluation, orchestration, security and cost.

Evals & Observability: evaluation as the gateway to production for AI systems, to catch regressions before users do

Context & Memory Engineering: deciding what goes into the context window, what gets retrieved, and what gets remembered

Agentic Architectures & Orchestration: multi-agent architectures in production, orchestration patterns and failure modes

Security, Sovereignty & Cost Control: prompt injection, agent identity and self-hosting to keep control of your AI systems

03 · Engineering Shifts

How AI is transforming existing engineering practices, from code to analytics.

The Agentic SDLC: specs, reviews, tests and delivery when machines write the code

AI-Augmented Data Engineering: the Data Engineer role with agents that write pipelines, fix incidents and document

Agentic Analytics: a rethought self-service analytics, where business users converse with governed semantic layers

AI Product Engineering: shipping AI features that are genuinely adopted, despite non-determinism

04 · Leading the AI Shift

The organizational questions: enablement, accountability and operating models.

AI Enablement at Scale: going from a handful of enthusiasts to an organization-wide capability with champions, academies, rituals and measured ROI

Governance & Accountability: who is responsible when an agent acts, the AI Act and shadow AI

Data & AI Operating Models: centralized or decentralized after data mesh, where AI teams fit, and how the CDO and CAIO roles are evolving