Phase 1: Audit (Manual Assessment)
The first phase sounds tedious because it is tedious. But you cannot fix what you haven't measured.
We exported core CRM data into spreadsheets and conducted manual reviews across five critical dimensions:
- Completeness: What percentage of records had all required fields populated? We defined "required" as any field that triggered automation, influenced routing, or appeared in executive reporting. At CT3, we discovered that 42% of lead records were missing at least one field that blocked automation execution.
- Accuracy: Were the values in populated fields correct? This required sampling—calling customers to verify phone numbers, checking company websites to validate job titles, cross-referencing LinkedIn to confirm employment status. Time-consuming, but revealing. We found a 19% error rate in fields we had assumed were accurate.
- Consistency: Did naming conventions match across records? Company names are notorious for inconsistency—"International Business Machines," "IBM," "IBM Corporation," and "I.B.M." all refer to the same company but create separate account records. We identified 347 duplicate accounts that had fragmented opportunity history and confused territory assignment.
- Timeliness: How old was the data? A job title from 2021 isn't reliable in 2024. Contact information degrades at roughly 30% annually in B2B databases. We flagged any record without activity in the past 18 months for verification or archiving.
- Relationships: Were parent-child account hierarchies correct? Enterprise sales depend on understanding corporate structure, but most CRMs struggle with complex organizational relationships. We found numerous subsidiary accounts incorrectly listed as independent companies, which broke territory rules and split opportunity tracking across multiple records.
The audit phase at CT3 took six weeks with two people working part-time. The output was a prioritized list of data quality issues ranked by business impact.
Phase 2: Prioritize (Focus on Automation Blockers)
With a comprehensive list of data problems, we resisted the urge to fix everything at once.
Instead, we applied a filtering methodology that focused resources on high-impact issues:
Impact Scoring: Each data quality problem received a score based on three factors:
- Revenue exposure (What's the pipeline value of affected opportunities?)
- Frequency (How many records have this problem?)
- Cascading effects (Does this issue cause other downstream problems?)
For example, missing territory data had high revenue exposure (affected $2.3M in pipeline), high frequency (31% of accounts), and cascading effects (broke routing AND reporting). It scored 27 out of 30 points.
Meanwhile, incomplete social media profile links had low revenue exposure (no direct pipeline impact), low frequency (only 8% of records), and no cascading effects. It scored 4 out of 30.
Automation Dependency Mapping: We identified every marketing automation workflow and sales process that depended on specific data fields. Then we cross-referenced our data quality findings against those dependencies.
This revealed that three fields—territory assignment, industry classification, and employee count—were blocking 18 different automation workflows. Fixing those three fields would unlock significantly more value than fixing dozens of lower-impact fields.
Effort Estimation: Some data problems are easy to fix programmatically. Others require manual research and validation. We categorized each issue as:
- Quick wins (Can be fixed with bulk updates or simple enrichment)
- Medium effort (Requires some manual review or external data sources)
- High effort (Needs extensive research or complex data transformation)
The prioritization matrix that emerged looked like this:
- High Impact + Quick Win =
Do immediately
High Impact + Medium Effort =
Schedule next
High Impact + High Effort =
Break into phases
Everything else =
Backlog
This framework prevented the common mistake of starting with the easiest problems rather than the most important ones.
Phase 3: Automate (Validation Rules & Enrichment)
With priorities established, we moved to prevention and correction.
Validation Rules at Point of Entry: We implemented CRM validation rules that prevented incomplete records from being created in the first place. Before any form submission could create a new lead record, it had to include:
- Valid email format (not just presence of @ symbol, but domain validation)
- Complete company name (minimum 3 characters, no test entries)
- Industry selection (from predefined list, not free text)
- Country code (for proper territory routing)
These rules felt restrictive to the marketing team initially, but they prevented thousands of incomplete records from entering the system. Better to capture 85% of leads with complete data than 100% of leads with 40% missing critical fields.
Progressive Enrichment Workflows: Rather than trying to enrich all historical data at once, we created automated workflows that filled gaps progressively:
- When a lead engaged with content, trigger enrichment lookup
- When an opportunity reached a specific stage, validate and complete account data
- When a contact changed companies (detected via email bounce), update employment information
This approach spread the enrichment cost over time and focused resources on active records rather than dormant ones.
Duplicate Prevention: We configured matching rules that checked for existing records before creating new ones. The matching logic considered:
- Email domain similarity (to catch spelling variations)
- Company name fuzzy matching (to catch IBM vs. I.B.M.)
- Phone number normalization (to standardize formatting)
When a potential duplicate was detected, the system either merged automatically (for high-confidence matches) or flagged for manual review (for ambiguous cases).
The automation phase at CT3 reduced new data quality issues by 67% within the first month of implementation.
Phase 4: Maintain (Governance & Training)
The final phase is what separates temporary fixes from lasting improvements.
Regular Audit Cadence: We established quarterly data quality audits using the same methodology from Phase 1. This created a baseline for measuring improvement and catching new issues before they became systemic problems.
Each quarterly audit included:
- Random sampling of 200 records across all sources
- Automated data quality reports comparing current vs. previous quarter
- Review of validation rule effectiveness (were they preventing bad data or just frustrating users?)
User Training: The sales and marketing teams received training on why data quality mattered and how their daily actions affected it. We avoided abstract concepts and focused on specific examples:
- "When you abbreviate company names inconsistently, leads get routed to the wrong rep"
- "When you skip industry classification, this prospect won't receive relevant nurture content"
- "When you create a new account instead of finding the existing one, pipeline reporting becomes inaccurate"
Training wasn't a one-time event. We incorporated data quality reminders into onboarding for new hires and created quick reference guides accessible from within the CRM.
Accountability Metrics: We added data quality metrics to team dashboards:
- Marketing: Percentage of leads with complete scoring data
- Sales: Percentage of opportunities with accurate close dates and next steps
- Operations: System-wide completeness score and duplicate account rate
These metrics weren't punitive—they were diagnostic. When scores dropped, it signaled either a process breakdown or a need for additional training.
Tool Optimization: Every six months, we reviewed our data enrichment tools and validation rules. Were they still catching the right issues? Had new data quality problems emerged that required new rules? Did we need different enrichment sources?
This continuous improvement mindset prevented the framework from becoming stale or irrelevant as business needs evolved.
