Enterprises want analytics and AI that are accurate, governed, and auditable—yet the underlying data is often fragmented, poorly described, and inconsistently secured. The result: conflicting dashboards, “which number is right?” debates, hallucinations, low trust, and stalled AI adoption.

In this breakout, we’ll walk through how Atlassian and Secoda create an “AI‑ and analytics‑ready” data foundation for Analytics, Rovo, and day-to-day workflows. We’ll show how a unified metadata layer, cross-system lineage, data quality and usage signals, and policy-driven access controls create the semantic context your organization needs to make high‑stakes decisions with confidence.

You’ll see how to:

• Map critical business concepts (like ARR, churn, SLA breach) into a shared semantic layer and business glossary that keeps metrics consistent across dashboards and AI experiences
• Use lineage, ownership, and query performance insights to understand where data comes from, how it’s used, and who’s accountable—before it reaches an executive dashboard or an AI agent
• Apply governance (RBAC, approvals, monitoring, PII tagging) so analytics and AI usage is safe, compliant, and explainable
• Ensure this governed data foundation is connected to Analytics, Data Lake, Guard, and Rovo, so product and IT teams can ship trustworthy dashboards and AI features faster—without sacrificing control

Attendees will leave with concrete ideas on how to move from data chaos and dashboard sprawl to a durable, governed foundation that powers trusted analytics and AI‑assisted decision‑making across Atlassian Cloud.