RevOps Co-Lab: The New Revenue Data Architecture — How to Rebuild Trust in Your Data Layer

For years, RevOps teams have been quietly compensating for broken operational assumptions.

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Conflicting lifecycle definitions. Duplicate accounts. Unclear ownership. Systems that technically “integrate” but operationally disagree with each other.

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Now AI is surfacing those problems faster than teams can hide them. 🤔

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Not because AI created bad processes, but because it forces organizations to confront a deeper question:

What operational decisions does your business actually trust enough to automate?

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In this RevOps Co-Lab, operations leaders will unpack how they’re rebuilding their revenue data architecture for the AI era. Not through theoretical frameworks or future-state slides, but through the real governance models, workflows, and operational systems they’re actively using today.

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This session is designed for operators responsible for creating trust across the revenue stack:

• defining ownership
• standardizing lifecycle logic
• resolving conflicting systems of record
• and determining where AI can safely operate inside GTM workflows

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Together, we’ll explore what happens when RevOps shifts from reactive data cleanup to proactive operational architecture.

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Because the goal is no longer just cleaner data.

It’s building a revenue system the business can actually trust.

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🔑 Key Takeaways

• Source of truth: Which system wins for which object, and how do you resolve conflicts when Salesforce, Marketo, and product data disagree?
• Data quality: Pre-flight checks, deduplication, normalization, and solving the “is this account even real?” problem
• Governance: Field ownership, change management, lifecycle design, and understanding who can break what
• AI readiness: What data and workflows teams actually trust AI to operate against, and what still requires human oversight

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📣 Speakers

Speaker lineup coming soon

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