Data Quality Sense

Live on AgentExchange · 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.

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AI Module

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. DQS points to the specific records that 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 runs in two phases: configure and analyze. In Builder Studio, you define what to check. Select an object, pick the fields that matter, and choose which dimensions to measure. Configure thresholds, maybe Email completeness needs to be 95%, but Website can be 80%. The system handles the scanning across your entire dataset. In Insights Studio, you see the results: record-level insights alongside the aggregate scores. Which specific Accounts have invalid emails? Which Contacts haven't been touched in 90 days? Which Cases contain PII that shouldn't be there? DQS runs entirely inside Salesforce: your data stays in your org, with nothing external to integrate. It respects your existing security model and scales to enterprise volumes.

Architect's Note

Need tailored quality dimensions?

Every organization has unique data quality requirements. If the standard dimensions don't cover your use case, we can extend DQS with tailored checks aligned to your business rules.

Talk about extending this app

Ready to know what's wrong with your data?

Install Data Quality Sense from AgentExchange and run your first scan on a live org the same day.

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