Data Quality — Validation, Dedupe & Quality Scoring
Records you can trust. Automated rules check for missing fields, bad formats and likely duplicates as records come in, and track quality over time.
Overview
Bad data quietly breaks everything downstream, from misfired campaigns to wrong reports and failed automations. Data Quality applies automated checks that keep records trustworthy: required-field rules, format validation and duplicate detection, each tied to a specific object and field with a severity and an owner.
Quality is measured, not assumed. Rules track how often they fail over time, so you can see which objects and fields drift and act before the mess spreads. Combined with the governance of import and export jobs, where every bulk movement carries row counts, errors and an approver, Data Quality keeps the single source of truth clean at the point data enters and as it lives in the platform.
Explore Data & Privileges →- ✓ Required-field, format and duplicate checks per field
- ✓ Severity levels so critical failures stand out
- ✓ Failure counts tracked over rolling windows
- ✓ Owners accountable for each quality rule
- ✓ Governed import and export jobs with error visibility
- ✓ Cleaner data feeding reports, automation and the AI agent
Everything Data Quality gives you
Validation Rules
Enforce required fields and correct formats so records are complete and consistent on entry.
Duplicate Detection
Catch duplicate records automatically to keep one clean version of the truth.
Failure Tracking
See each rule's failure count over a rolling window to spot where data is drifting.
Severity Levels
Rank rules by severity so the most damaging quality issues are addressed first.
Governed Data Jobs
Every bulk import or export records row counts, errors and who authorised it.
Rule Ownership
Each quality rule has an owner accountable for keeping that data clean.
How the agent works with Data Quality
The Flow-Cognition Agent runs a real loop on your Data Quality data — grounded in your records, governed and logged.
Sense
Watches new and imported records against your rules.
Decide
Spots likely duplicates and malformed values.
Act
Suggests merges and fixes for an owner to approve, and logs each change.
Five-tier model routing · field-level permissions · full audit trail. See the AI layer →
Live in four steps
Define Rules
Set required-field, format and duplicate checks on the objects and fields that matter.
Set Severity
Assign a severity and owner so failures are prioritised and accountable.
Monitor Failures
Watch failure counts over time to spot drift and problem fields early.
Govern Imports
Route bulk data jobs through approval with full visibility of rows and errors.
Where teams put it to work
Clean lead capture
Validation rules reject leads with malformed phone numbers or missing owners, so sales works from complete, contactable records.
Deduped customer base
Duplicate detection flags repeat customer records so reporting and outreach are not skewed by the same person counted twice.
Safe bulk import
A large import is routed through an approved data job that surfaces row counts and error rates, catching problems before they hit production data.
The payoff
Trustworthy records
Records stay more complete, consistent and free of duplicates.
Reliable analytics
Clean inputs mean dashboards and reports reflect reality, not data-entry noise.
Problems caught early
Failure tracking and severity surface drift before bad data spreads downstream.
Controlled data movement
Governed import and export jobs make every bulk change visible and approved.
Questions, answered
Rules cover required fields, format validation and duplicate detection, each tied to a specific object and field with a severity level and an owner responsible for it.
Each rule tracks its failure count over a rolling window, so you can see whether a field is getting cleaner or drifting worse over time and act accordingly.
Rules identify records that appear to be duplicates so you can consolidate to one clean version of the truth, preventing the same customer or lead being counted and contacted twice.
Yes. Every data job records its direction, object, row count, errors, who requested it and who approved it, so bulk movements are authorised and their outcomes are fully visible.
The Flow-Cognition Agent acts on your records, so clean, validated, deduplicated data means its decisions and automations are based on an accurate source of truth rather than flawed inputs.
See Data Quality in your workflow
Start free, or get a guided walkthrough with our team — on the one platform that runs Data & Analytics and your whole business.