01 Replication: All GTM stack data lands in the warehouse
The first Foundat10n requirement: every GTM system replicates to the warehouse, because data stranded in a vendor's screens can't be joined, analyzed or handed to an agent.
- Requirement: every GTM system (CRM, marketing automation, conversation intelligence, dialer, ERP, product usage) replicates to the warehouse.
- Scores 1: every system that holds GTM data lands in the warehouse on a schedule actively monitored for success and failure.
- Scores 0: some GTM data lives only in a vendor’s own screens, or loads by hand.
- Protects from: questions that can only be answered inside a vendor’s UI, which means they can’t be joined, analyzed or handed to an agent.
A tell-tale sign of issues with this tenet is an answers like:
"Oh, we can only get X from Y so we join it manually afterwards."
We’ve all seen it, and I think most people have their own usual boogeymen. Mine is forecasting tools. I regularly see RevOps teams rely on the vendor interface for evaluating pipeline conversion, measuring forecasting accuracy, or manually exporting Forecast submissions then blending it with other data. It’s such rich source of data to leave stranded in a vendor’s environment.
I haven’t done an exhaustive review of all the terms and conditions of every GTM tech stack vendor, but I will go to the barricades to tell RevOps teams to get this data into your warehouse. It’s your stuff! Your leader’s forecast judgment, your deals, your customer journeys, your product usage, and your relationships. You should always be able to analyze your stuff against all of your other stuff from any tool or point of entry that you want.
This seems like it would be the least controversial of the 10 items in the audit, but you would be surprised. Data engineering teams might ask “do you actually need this”? AI architects occasionally argue “who needs it in a database when you’ve got MCP”? And unfortunately, modern vendors have taken to charging you a fee for this, ~$20K - 30K per year is not abnormal these days.
If budgets are a concern, adopt it as a philosophy, choose an extensible approach so you don’t re-invent the wheel for every piece of software, and only start replicating datasets with open requirements downstream.