Stop Measuring Data Quality. Start Engineering It.

★★★★★

5.0

Capterra

★★★★★

4.7/5

Your Data Quality Score Isn't the Problem. It's the Symptom.

Organizations spend millions on data quality tools and still face failures. DQ tools measure defects after the damage is done. iceDQ engineers reliability into every pipeline stage: pre-production testing, source-to-target validation, and production monitoring. One rule set from Dev to Production. No rebuilding. No blind spots. No bad data reaching the business.

Trusted by Fortune 500 companies

Why Choose iceDQ?

Data Reliability Engineering designed to replace reactive data quality scoring with proactive validation, reconciliation, and monitoring.

Pre-Production Data Validation

Test every data pipeline in Dev, QA, and UAT before go-live. iceDQ catches transformation errors, schema mismatches, and business rule violations before bad data reaches production - not after.

Full Source-to-Target Reconciliation

iceDQ validates 100% of records across every pipeline hop - from raw source data through every transformation layer to the final target. Row counts, attribute values, business rules, and referential integrity - all reconciled, not sampled.

Deploy Testing Rules into Production Monitoring

The validation rules you build in pre-production do not get left behind. Deploy them directly into production as continuous monitoring jobs - so the same logic that certified your pipeline becomes your production safety net.

CI/CD and DataOps Integration

Embed data reliability engineering into your CI/CD pipeline using API-first design. Automated validation runs on every deployment - catching regressions before they reach production, with results pushed to JIRA, Azure Test Plans, and ServiceNow.

Auto-Rule Generation Across Every Layer

iceDQ's AI-driven auto-rule generation scans source and target schemas and generates validation and reconciliation rules across thousands of tables in hours - covering completeness, data types, transformation logic, duplicates, and business rules.

One Platform. Testing, Monitoring, and Observability.

Replace multiple siloed tools - your DQ scorer, your pipeline tester, your monitoring tool - with one unified platform. iceDQ covers the full data reliability lifecycle: Build and Test, Run and Monitor, Produce and Observe.

Data Quality vs. Data Reliability

Your data quality tool measures outcomes. iceDQ engineers them - before, during, and after production.

Aspect	Data Quality Tool Approach	iceDQ - Data Reliability Approach
When it runs	After data reaches production	Before, during, and after - from Dev to Production
What it checks	Scores DQ dimensions at the consumption point - accuracy, completeness, timeliness	Validates 100% of records at every pipeline stage - source data, transformations, targets
How it works	Reactive - detects defects after the damage is done	Proactive - prevents defects at the source before they propagate
Data coverage	Samples 5-10% of records - the other 90% is untested	Validates billions of records with no sampling - every record, every time
Validation and reconciliation	Limited - flags individual record issues but cannot reconcile source to target	Full source-to-target reconciliation at every hop - row counts, attributes, transformations, and business rules
Pre-production testing	Not designed for it - built to observe final data, not test pipelines	Built for it - automates data pipeline testing in Dev, QA, and UAT before go-live
Production monitoring	Post-production scoring only - no embedded pipeline checks or real-time controls	Deploy the same validation rules built in testing directly into production as continuous monitoring jobs
Rule reuse	Rules live in the DQ tool only - rebuilt separately for each environment	One rule set reused across Dev, QA, UAT, and Production - no rebuilding required
Scope	Final production data only	Entire data ecosystem - source files, raw data, pipelines, transformations, and production
Approach	Reactive inspection - measuring defects that already exist	Proactive engineering - building quality in from the start

Out-of-Box Checks

Accelerate Data Reliability Engineering with Prebuilt Validation and Reconciliation Checks

Custom

Complex conditions using custom expressions

Completeness

Validates for NULLs, spaces, or empty values

Contains

Verifies attribute contains only specified values

Datatype

Checks if value can be cast to a specific type

Range

Ensures values fall within a specified range

Date

Validates strings against selected date formats

Pattern

Matches values against a regular expression

Duplicate

Detects duplicates across one or more attributes

Length

Checks the length of each attribute value

Reconciliation

Cross-system record matching and validation

Features

Easy, Low-Code/No-Code Testing

Automate data validation and reconciliation with minimal effort
Powerful scripting for complex validation scenarios, with rule-based validation and reconciliation

High-Performance, Scalable Testing

Achieve million-record-per-second validation speeds
Flexible deployment on-prem or in the cloud with parallel and cluster processing

Seamless Connectivity and Integration

Connect to over 150 databases, cloud systems, and file sources
Integrate seamlessly with test case management and ticketing systems

Accelerate DataOps with API-First Design

Fully compatible with CI/CD pipelines
Automate regression testing and enable end-to-end data reliability for DataOps

Benefits

See the transformation iceDQ delivers across real data reliability projects

Data Pipelines Validated

3,000

↓

5,000

67% more coverage

Data Validation Automation Level

10% - 20%

↓

95%

~5x improvement

Data Validation Coverage

Less than 80%

↓

100%

Full coverage achieved

️

Time to Data Reliability

24 Months

↓

5 Months

79% faster delivery

Testing Team Size

10 People

↓

5 People

50% team reduction

Production Monitoring Cycles

3 Months

↓

1 Month

3x faster cycles

Trusted by Industry Leaders

Every morning our DQ dashboard said we were fine. Turns out 30% of our ETL transformations were broken. iceDQ caught it on day one in pre-production. Day one.

Head of Data Engineering,
Morgan Stanley

Three years with a DQ tool. Three years of failed audits. Then iceDQ tested every record, every transformation, source to target. We haven't had a single audit finding since.

Chief Data Officer,
Arch Insurance

I used to dread 2am calls about bad data. The day we deployed iceDQ's testing rules into production monitoring? Those calls stopped. Same rules, now our safety net.

VP of Data Platforms,
Pfizer

Our old tool said 94% complete. Great, right? Except that missing 6% was customer IDs across 18 million records. iceDQ's reconciliation caught it before it wrecked our CRM.

Director of Data Quality,
wiley

5,000 hours. Half a million dollars. That's what iceDQ saved us on one migration project. I didn't believe the numbers at first either, but they held up.

Head of Quality Assurance,
PepsiCo

100% test coverage used to be a slide in a presentation. After iceDQ, it's actually real. Our entire team still talks about how fast the implementation went.

Head of Business Analytics,
BMC Software

Built-In Functionalities

Parameterization

Rules Wizard

Data Reliability Engineering

Data Monitoring

Built-In Scheduler

️User-Defined Function

Flat File Testing

️SAP HANA Migration Testing

Reporting and Analytics

Security - LDAP and SSO

Query Designer

Regression Testing

Salesforce Migration Testing

Alerts and Notifications

️Integrated Key Vault

Ready to Move Beyond Data Quality Scores?

Try it for yourself today

Book a Demo

Frequently Asked Questions

What is the difference between data quality and data reliability?

Data quality is a snapshot - it measures whether data meets defined standards at a specific point in time, typically at the end of the pipeline. Data reliability is consistent data quality over time. It requires engineering quality into every stage of the data lifecycle - from pipeline development and testing through to production monitoring - so that good data is not an occasional outcome but a guaranteed one. iceDQ is built for data reliability, not just data quality scoring.

Why is checking data quality at the end of the pipeline not enough?

By the time a defect shows up in a dashboard or quality score, the damage has already propagated through every downstream system, report, and business decision that relied on that data. A 2% error in source data, combined with a transformation bug and an orchestration issue, does not produce 11% corrupted data - it compounds to 30% or more. Fixing defects at consumption costs 10-100x more than preventing them at the source. iceDQ shifts your quality controls left - catching issues in pre-production before they ever reach the business.

How does iceDQ validate data differently from a data quality tool?

Traditional DQ tools score data dimensions - accuracy, completeness, timeliness - at the consumption point. iceDQ validates 100% of records at every stage of the pipeline. It performs full source-to-target reconciliation, checking row counts, attribute values, transformation logic, business rules, and referential integrity across every hop - not just sampling 5-10% at the end. The result is not a quality score. It is a verified, tested, production-ready dataset.

Can iceDQ replace my existing data quality tool?

For most organizations, yes. iceDQ covers the full data reliability lifecycle - pre-production data testing, source-to-target validation and reconciliation, continuous production monitoring, and data observability - in a single platform. Teams typically consolidate 2-4 tools into iceDQ, eliminating the fragmentation between their testing tool, their DQ scorer, and their monitoring platform. Every rule built in testing is reusable in production - no rebuilding across tools or environments.

How does the pre-production testing and production monitoring workflow work?

In pre-production, iceDQ builds and runs validation rules against your pipelines in Dev, QA, and UAT - catching transformation errors, schema mismatches, business rule violations, and reconciliation failures before go-live. Once certified, those exact same rules are deployed directly into production as continuous monitoring jobs. Your production environment runs the same checks, on the same schedule, against live data - alerting your team in real time before bad data reaches downstream systems or business users.

How quickly can iceDQ auto-generate data validation and reconciliation rules?

iceDQ's AI-driven auto-rule generation scans source and target schemas and generates validation rules across thousands of tables and attributes in hours - work that would take a manual team weeks. Rules cover completeness, data types, referential integrity, transformation logic, duplicates, and reconciliation, and can be reviewed, refined, and deployed across all environments from a single rule repository.

How quickly can we deploy iceDQ for our data reliability program?

Most organizations complete a proof of concept within 2-4 weeks and full deployment within 30 days. Every iceDQ customer receives a dedicated Forward Deployed Engineer (FDE) for 3 months at no additional cost - who configures the platform to your specific data stack, builds initial validation and monitoring rules, and gets your team engineering data reliability from day one.