VP of Revenue Operations
Data quality and CRM governance
What You Do Today
Enforce CRM data quality standards — required fields, stage definitions, activity logging. Fight the eternal battle of getting reps to update their deals accurately. Poor data quality is the #1 RevOps blocker.
AI That Applies
AI auto-enriches records from email, calendar, and engagement platforms — filling in contacts, logging activities, and updating deal stages without requiring manual rep data entry.
Technologies
How It Works
For data quality and crm governance, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
CRM hygiene shifts from nagging reps to automated data capture. AI fills the fields so reps don't have to.
What Stays
Defining what "good data" means, designing processes that produce clean data naturally, and managing the cultural change needed to make CRM the system of record.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for data quality and crm governance, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long data quality and crm governance takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your board chair or lead independent director
“What data do we already have that could improve how we handle data quality and crm governance?”
They shape expectations for how AI appears in governance
your CTO or CIO
“Who on our team has the deepest experience with data quality and crm governance, and what tools are they already using?”
They own the technology infrastructure that enables AI adoption
a peer executive at a company further along on AI adoption
“If we brought in AI tools for data quality and crm governance, what would we measure before and after to know it actually helped?”
Their lessons learned are worth more than any consultant's framework
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.