Automated Data Quality and Hygiene

The Data Quality Crisis

Poor data quality costs sales teams:

• Wasted time - searching for right records
• Duplicate outreach - multiple people contact same person
• Incomplete context - missing key information
• Inaccurate reporting - garbage in, garbage out
• Poor customer experience - wrong information used

Yet cleaning data is tedious and time-consuming.

How Shabe Automates Data Quality

Duplicate Detection:

"Find duplicate contacts"
→ List of potential duplicates with suggestions

"Merge all duplicates for john@acme.com"
→ Smart merging with primary record selection

"Show me duplicate companies"
→ Company-level deduplication

Missing Data Identification:

"Show contacts with no email address"
→ Incomplete records needing attention

"Find deals without close dates"
→ Forecast-impacting gaps

"List companies missing industry field"
→ Segmentation blockers

Stale Record Detection:

"Show contacts with no activity in 2 years"
→ Candidates for archiving

"Find deals stuck in same stage for 90+ days"
→ Stalled opportunities needing review

"List contacts with old job titles"
→ Enrichment opportunities

Data Quality Workflows

Weekly Data Audit:

"Find data quality issues"
→ Shabe scans for common problems

"How many duplicates do we have?"
→ Quantify the problem

"Show records missing phone numbers"
→ Prioritize enrichment efforts

Duplicate Management:

"Find duplicates created this month"
→ Catch issues early

"Merge contacts with same email address"
→ Automatic deduplication

"Show me potential duplicate companies"
→ Account-level cleanup

Enrichment Prioritization:

"Which high-value contacts are missing data?"
→ Focus on what matters

"Show incomplete records for enterprise accounts"
→ VIP data completion

"Find contacts needing phone numbers for calling"
→ Campaign readiness

Bulk Cleanup Operations

Mass Updates:

"Update all contacts from old-company.com to new-company.com"
→ Handle acquisitions/rebrands

"Set all bounced emails to invalid status"
→ Email list hygiene

"Tag all incomplete records for cleanup"
→ Systematic data improvement

Preventive Measures:

"Alert me when duplicates are created"
→ Proactive monitoring

"Check for missing required fields on new contacts"
→ Enforce data standards

"Flag contacts without company association"
→ Relationship mapping

Data Quality Metrics

Monitor Health:

"What's our data completeness rate?"
→ Overall database health

"How many records were updated this week?"
→ Activity tracking

"Show data quality trends over time"
→ Improvement measurement

Measured Results

Teams using Shabe for data quality see:

• 90% reduction in duplicate records
• 5x faster data cleanup
• 85%+ completeness for key fields
• Better reporting accuracy
• Improved user adoption - clean data is usable data

Get Started

Automate data quality management. Connect HubSpot to Shabe and ask "Find data quality issues" to start cleaning up.