How Do I Ensure Data Quality Across Revenue Systems?
Use a RevOps framework: set a data contract, standardize identity rules, validate at capture, monitor continuously, and govern changes—then scale with confidence.
Key Controls for Reliable GTM Data
| Control | Definition | Why it matters |
|---|---|---|
| Data contract | Field dictionary, picklists, owners, TTL | Prevents drift; speeds analysis |
| Identity resolution | Rules to dedupe & link people/accounts | Accurate routing & attribution |
| Validation at capture | Format/logic checks; enrichment | Stops bad data at the door |
| Consent & privacy | Region-aware consent + purpose flags | Enables compliant activation |
| Monitoring & alerts | Quality score, thresholds, notifications | Catches breaks fast |
| Change control | Sandbox tests, release notes, rollback | Protects pipelines & reports |
Data Quality Metrics & Threshold Cues
| Metric | How to measure | Threshold cue | Scope | Notes |
|---|---|---|---|---|
| Completeness | % required fields populated | < 95% on routing fields | Lead/Contact/Account | Segment- and region-based |
| Validity | % values pass format/business rules | Rising invalid rate week-over-week | All core objects | Use regex & lookup tables |
| Uniqueness | Dupes per 1k records by key | > target for email/domain rules | People/Accounts | Track fuzzy & exact dupes |
| Consistency | % cross-system field agreement | < 98% for IDs/segments | CRM ↔ MAP ↔ CS | Use field-level audits |
| Timeliness | Lag from event → system of record | > SLO by source/region | All integrations | Alerts for queue backlogs |
Identity Rules & Golden Record (Minimal Set)
| Entity | Primary keys | Secondary/fuzzy keys | Golden record policy |
|---|---|---|---|
| Person | Email (normalized) | Name + company + phone hash | Prefer CRM contact over MAP lead when both exist |
| Account | Domain (rooted) | Name similarity + billing country | One parent; controlled child hierarchy |
| Opportunity | CRM opp ID | Account + stage + amount range | CRM is system of record; MAP mirrors |
90-Day Data Quality Playbook
| Step | What to do | Output | Owner | Timeframe |
|---|---|---|---|---|
| 1 — Baseline | Audit fields, picklists, identity rules, consent | Data contract v1 + gaps | RevOps/Data | Weeks 1–2 |
| 2 — Guardrails | Enable validators, required fields, route fixes | Capture quality ↑; breakage ↓ | Platform Admin | Weeks 3–4 |
| 3 — Identity | Implement dedupe & merge with audit logs | Golden record policy live | RevOps + IT | Weeks 5–6 |
| 4 — Monitoring | Publish DQ dashboard + alerts | Weekly quality scorecard | Analytics | Weeks 7–8 |
| 5 — Remediate | Run cleanse sprint; fix top fields by ROI | Lift vs baseline + SOPs | RevOps/Enablement | Weeks 9–13 |
Frequently Asked Questions
CRM for accounts, contacts, and opportunities; MAP for engagement events; CS/Finance for renewal and billing. Document this in the data contract.
Keep 5–7 routing/forecast-critical fields required. Everything else should be optional or backfilled via enrichment and QA.
Use purpose-based consent flags with region overlays and TTLs. Build suppression and SAR workflows into MAP/CRM.
Publish a weekly DQ scorecard tracking completeness, validity, uniqueness, consistency, and timeliness by segment/region. Tie fixes to funnel and forecast KPIs.
RevOps owns the contract and monitoring; Systems and Data teams implement changes; channel owners remediate at the source.