Data Quality Issues
Duplicate records, incomplete information, inconsistent formatting, and outdated data undermine decision-making and erode trust in your CRM system.
Root Causes
Data quality issues don't appear overnight. They result from systemic problems in data governance, migration processes, and user behavior.
Poor Data Migration Strategy
Rushing data migration without proper cleansing, deduplication, and transformation rules. Legacy data is often transferred "as-is" with all its historical problems.
Real example: A wealth management firm migrated 15 years of data from Junxure without deduplication, creating 8,400+ duplicate household records.
Lack of Data Governance
No clear ownership, standards, or processes for data entry, validation, and maintenance. Different teams use different conventions for the same information.
Real example: One bank had 47 different formats for phone numbers across 12,000 customer records, making automated dialing impossible.
Insufficient Validation Rules
Missing or weak validation rules allow users to enter incomplete or incorrectly formatted data. Required fields aren't enforced, picklist values are inconsistent.
Real example: An RIA discovered 3,200 client records without email addresses because the field wasn't marked as required during migration.
Integration Data Conflicts
Multiple systems updating the same records without clear master data management create conflicts, overwrites, and synchronization issues.
Real example: CRM and portfolio management system both updating client addresses caused weekly sync conflicts affecting 600+ households.
User Workarounds and Bad Habits
When the system is difficult to use or doesn't support workflows, users create workarounds—free-text fields for structured data, notes instead of proper fields, shortcuts that compromise quality.
Real example: Advisors storing account numbers in notes fields because lookup was "too slow," making reporting impossible.
No Ongoing Data Stewardship
Data quality requires continuous monitoring and maintenance. Without dedicated data stewards and regular audits, quality degrades over time.
Real example: A credit union had 94% data quality at launch, which dropped to 67% within 18 months without ongoing governance.
Warning Signs
Recognize these red flags before they escalate into major business problems:
đź”´ Critical: Duplicate Records Proliferating
Multiple records for the same client, household, or account. Search returns several "John Smith" entries with no clear way to identify the correct one.
đź”´ Critical: Missing Required Information
Email addresses, phone numbers, or other critical fields are blank for significant percentages of records. Reports show "N/A" or "Unknown" everywhere.
⚠️ Warning: Inconsistent Formatting
Phone numbers stored as (555) 123-4567, 555-123-4567, 5551234567, and +1.555.123.4567. Makes automation and reporting impossible.
⚠️ Warning: Users Don't Trust the Data
Team members verify information by calling clients or checking other systems. "I don't trust what's in Salesforce" becomes a common refrain.
⚠️ Warning: Reports Don't Make Sense
Dashboards show impossible numbers. Totals don't add up. Management questions every metric because data integrity is suspect.
⚠️ Warning: Integration Sync Errors
Daily error logs show hundreds of failed syncs. Data doesn't match between CRM and integrated systems like portfolio management or document management.
ℹ️ Notice: Abandoned Records
Many records haven't been updated in months or years. "Last Modified" dates show accounts that haven't been touched since migration.
ℹ️ Notice: Heavy Use of Notes Fields
Important structured data is buried in unstructured notes and comments because proper fields don't exist or aren't being used correctly.
Proven Solutions
Implementing these strategies will significantly improve your data quality and prevent future degradation:
1. Implement Comprehensive Data Governance
Establish clear ownership, standards, and processes for data management:
- Appoint Data Stewards: Designate specific individuals responsible for data quality in each business unit
- Create Data Standards: Document exact formats for all data types—addresses, phone numbers, names, account numbers
- Define Data Entry Procedures: Step-by-step guides for common data entry scenarios with screenshots
- Establish Review Cycles: Monthly data quality audits with specific metrics and improvement targets
2. Deploy Automated Data Quality Tools
Use technology to prevent, detect, and fix data quality issues:
- Duplicate Detection: Real-time duplicate prevention and batch deduplication tools (Salesforce Duplicate Rules, Cloudingo, DemandTools)
- Data Validation: Comprehensive validation rules for format, completeness, and logical consistency
- Address Standardization: Automated address validation and correction (SmartyStreets, Melissa Data)
- Data Quality Dashboards: Real-time visibility into data quality metrics with trend analysis
3. Execute Professional Data Cleansing
Systematic approach to fixing existing data quality problems:
- Data Profiling: Comprehensive analysis to identify all quality issues across all objects and fields
- Deduplication Strategy: Intelligent merge rules that preserve the best data from each duplicate
- Data Enrichment: Fill missing information using third-party data sources or manual research
- Format Standardization: Bulk updates to enforce consistent formatting across all records
4. Implement Master Data Management (MDM)
When integrating multiple systems, establish clear master data rules:
- Define System of Record: Clearly designate which system owns each data element
- Conflict Resolution Rules: Automated rules for handling conflicting updates from different systems
- Bidirectional Sync Strategy: Clear understanding of what syncs when and in which direction
- Sync Error Management: Automated alerts and resolution workflows for integration failures
5. User Training on Data Quality
Make data quality everyone's responsibility:
- Role-Specific Training: Targeted training on data standards relevant to each user's job
- Why It Matters: Help users understand how poor data quality impacts their work and client service
- Best Practice Guides: Quick reference cards with data entry standards and common scenarios
- Ongoing Reinforcement: Regular tips, reminders, and recognition for good data stewardship
Vantage Point's Approach
Our data quality methodology is battle-tested across 400+ implementations. We don't just fix data—we build systems that prevent quality degradation.
Pre-Migration Data Audit
Comprehensive profiling of legacy data before migration. We identify issues, quantify quality metrics, and create a remediation plan that gets executed before data moves.
Intelligent Deduplication
AI-powered duplicate detection with custom merge rules for financial services. We preserve the best data from each duplicate and maintain full audit trails.
Automated Quality Gates
Custom validation rules, process builder flows, and real-time duplicate prevention. Bad data can't get in. RecordIQ provides ongoing quality monitoring.
Data Stewardship Training
We train your team on data governance best practices, establish steward roles, and provide ongoing support to maintain quality standards.
Real Results
Related Resources
Data Quality Scorecard
Assess your current CRM data quality across 5 key dimensions and get immediate recommendations.
Data Migration Services
Learn about our proven 4-phase methodology for zero-data-loss migrations from legacy CRMs.