The measurement environment grew with the business. The architecture governing it didn't.

Technology partners, resellers, and SIs source or influence a significant percentage of enterprise pipeline. The CRM tracks partner-associated deals through a separate field or record type that marketing attribution systems don't read. Partner-influenced pipeline doesn't appear in marketing's attribution model. The decision about how much to invest in the partner program is made without the revenue contribution it actually produces.

A second product line launched. It shares dashboards with the core product. Executive reporting shows aggregate metrics that look healthy while the new product's actual traction is masked by the legacy product's scale. Cannibalization between product lines is invisible because the signal layer was never designed to separate them cleanly. Investment decisions about the new product are made on blended data.

Revenue accrues from usage metrics in the product. The billing system calculates charges from those metrics. The CRM holds contract terms and committed ARR. All three systems are correct in isolation. The reconciliation between them — the actual realized revenue against contracted terms against usage-derived billing — requires a signal architecture that connects all three. Without it, revenue forecasting is built on the CRM alone and regularly diverges from the billing system's actuals.

A large enterprise prospect evaluates the product across three subsidiaries — each with different email domains, different CRM records, and different interaction histories. The marketing attribution system sees three separate prospects. The sales team knows they're one account. Account-based marketing investments target the subsidiaries individually rather than the parent account relationship. When the deal closes, attribution credits the wrong touchpoints because the account identity was never resolved across subsidiary boundaries.

The enterprise sales cycle is 6–12 months. Marketing attribution models retain source attribution for 90 days — then default to direct or organic. By the time the deal closes, the first-touch and multi-touch attribution that would credit the right channels has expired. Pipeline reports show 50–70% of enterprise deals attributed to direct or untracked sources. The marketing investment that opened those accounts has no attribution credit, which causes the team to underinvest in the channels that actually generate enterprise pipeline.

Predictive lead scoring, churn risk models, and expansion opportunity identification were deployed as the next phase of the RevOps maturity roadmap. The training data came from product analytics and CRM data that were never reconciled at the account level. The models are technically sophisticated. The signals feeding them reflect years of identity fragmentation, inconsistent event naming across product versions, and partner-sourced pipeline that never made it into the training dataset. Model performance is below expectations and the reason isn't model quality.