Data Quality Sense
Know what's wrong with your data
Bad data costs more than you think—in failed automations, confused sales reps, and AI that hallucinates. Before you feed your Salesforce data to Agentforce, you need to know what's in it. DQS scans your data across five quality dimensions plus AI readiness checks, and tells you exactly which records need fixing.
Is your data ready for AI?
Before Agentforce touches your data, know what's in it. The AI Readiness module detects problems that break machine learning—and flags them at the record level.
PII Detection
Find personal data hiding in free text fields. Emails in comments, phone numbers in descriptions, SSNs where they shouldn't be. DQS flags records with PII exposure before AI amplifies the problem.
Noise Patterns
Detect placeholder values that add nothing: N/A, TBD, 'see attachment', '?', and dozens of patterns that make AI training less effective. Measure signal-to-noise ratio across your text fields.
Boilerplate Detection
Find templated content that reduces training diversity. Email signatures, standard disclaimers, copy-pasted phrases. When 40% of your Case comments are identical, AI has less to learn from.
Token Density
Measure how much useful text exists for AI training. Sparse records (under 20 words) don't give AI enough context. DQS identifies fields and objects with insufficient content density.
Complete data quality coverage
The fundamentals, measured at the record level. Not just scores—DQS tells you which specific records need attention.
Completeness
Is your data actually filled in?
Find empty fields, placeholder values like 'N/A' or 'TBD', and patterns of missing information. Set thresholds per field—some fields matter more than others.
Validity
Is it in the right format?
Catch malformed emails, invalid phone numbers, and data that doesn't match expected patterns. Auto-detects format for common field types, or configure custom regex.
Uniqueness
Do you have duplicates?
Detect repeated values, duplicate patterns, and data concentration that indicates entry problems or merge failures. Measures entropy and distribution across your dataset.
Timeliness
Is your data current?
Flag stale records that haven't been touched in months. Configure freshness thresholds per object—Accounts might need 90-day recency, Tasks might need 7-day.
Consistency
Is it standardized?
Find 'Active' vs 'active' vs 'ACTIVE' variants, spelling differences, and values that should be identical but aren't. Surface the variants so you can standardize.
How it works
Data Quality Sense — Development Journey
Tucario Team
Architecture
System Design
Q4 2025
Core Build
Active Development
Q4 2025 - Q1 2026
Testing & Polish
Quality Assurance
Current
Security Review Prep
Submission Ready
Pending
In Development
We're wrapping up development and preparing for Salesforce security review submission. Join the waitlist to be notified when DQS is available on AppExchange.
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Get NotifiedNeed custom quality dimensions?
Every organization has unique data quality requirements. If the standard dimensions don't cover your use case, we can extend DQS with custom checks tailored to your business rules.
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