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Home Data Quality Remediation

Monitoring finds bad data. Datagaps gets it fixed, and proves it.

Trace every data quality failure to its source, route the fix to an owner for approval, and re-test 100% of your records before anything is released.

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14-day free trial · No credit card required · Runs inside your own environment

Diagnose to the source · Fix with sign-off · Prove every fix

customer_email: null rate 12%

Sample scenario · customer table

Illustrative

  • Detected by AI-generated rule

    Anomaly flagged as the data arrives

    Live

  • Traced to source, 3 reports affected

    Lineage and impact analysis

    Live

  • Fix previewed and approved by owner

    Dry run on the affected rows

    Rolling out

  • Re-tested source to target

    100% of rows, dashboards unchanged

    Live
Verified. Released with audit trail.

The Resolution Gap

Finding Bad Data Matters. Fixing It Is What Restores Trust.

Monitoring tells you something broke. Trust returns only when someone owns the issue, the root cause is found, the fix is applied safely, and the result is proven.

63%

Lack AI-ready data practices

Of organizations lack, or are unsure they have, the right data management practices for AI. Source: Gartner survey of 1,203 data management leaders, July 2024.

60%

AI projects at risk

Of AI projects without AI-ready data will be abandoned through 2026. Source: Gartner prediction, February 2025.

Why Detection Alone Falls Short

Six Gaps Between an Alert and a Fix You Can Trust

Each one slows resolution or lets bad data back in. Datagaps closes all six.

dq remediation alerts no owner

Alerts with no owner

The same alert fires every week. Nobody is assigned, nothing is prioritized.

dq remediation root cause

Root cause takes days

Analysts trace failures through pipelines by hand and map the damage in spreadsheets.

dq remediation fixes in scripts

Fixes live in scripts

One-off SQL patches in production. No review, no rollback, no record.

dq remediation fix breaks

A fix can break something else

Correcting one table quietly shifts a dashboard or model that relied on the old values.

dq remediation no audit proof

No proof for the auditor

Who changed what, who approved it, and whether it held sits in tickets and chats.

dq remediation defects return

The same defects return

Fixes never become rules, so the fault re-enters on the next load.

The Closed Loop

From Alert to Verified Fix: Six Stages, One Audit Trail

Most tools stop at the alert. Datagaps carries each issue through to proof.

LIVE Available today ROLLING OUT On the roadmap

1. DETECT — Find it early

LIVE AI-generated quality rules
LIVE Anomaly and drift detection
LIVE Multi-level quality scores

Data Quality Monitor

2. DIAGNOSE — Know why

LIVE Lineage with quality scores
LIVE Downstream impact analysis
ROLLING OUT AI root-cause suggestions

DQ Monitor + DataOps Suite

3. DECIDE — Put an owner on it

LIVE Issue tracking with history
ROLLING OUT Severity routing, SLAs, escalation

DataOps Suite

4. FIX — Correct with control

ROLLING OUT Cleanse, standardize, merge
ROLLING OUT Dry run, approve, roll back
LIVE Rehearse on masked data

DQ Monitor + Test Data Manager

5. VERIFY — Prove it's fixed

LIVE Source-to-target reconciliation
LIVE Dashboard regression testing
LIVE 100% of rows, no sampling

ETL Validator + BI Validator · proven today

6. PREVENT — Make it stay fixed

LIVE Versioned data contracts
LIVE CI/CD quality gates
ROLLING OUT One-click fix to rule

DataOps Suite

Every resolved issue becomes a permanent rule, so it cannot recur.

AUDIT TRAILIssue, owner, approval, fix and re-test result, time-stamped and exportable for SOX, HIPAA and APCD evidence.

Incoming dataPipelines, files, APIs
Quality checkAI rules on every loadLIVE
Passes
Trusted dataFlows on to reports, dashboards and AI
Fails
  1. Held backROLLING OUT
  2. Traced to the sourceLIVE
  3. Fix approved by its ownerROLLING OUT
  4. Re-tested on 100% of rowsLIVE
  5. Released with audit trailLIVE
Every verified fix becomes a new rule, so the same failure is caught next time.

Your data

  • Source systems
  • Warehouses and lakehouses
  • BI and reporting tools

Datagaps DataOps Suite

Runs in your cloud, VPC or data center

DetectDiagnoseFixVerifyPrevent

Embedded AI · 200+ connectors · Audit trail

Your workflow

  • CI/CD pipelines
  • Git
  • Data catalog
Inside your environment

Deploys beside your stack. No rip and replace.

No data to outside AI

The embedded LLM runs within the platform.

Enterprise-ready security

SOC 2 Type II and ISO 27001.

Works with Snowflake, Databricks, Microsoft Fabric, Power BI, Tableau, Collibra, Unity Catalog, Azure DevOps, GitHub Actions, GitLab and Jenkins.

The Platform

One Platform Behind Every Stage

Each product maps to a stage of the remediation loop, deployed independently or as a unified DataOps Suite.

Data Quality Monitor for Industry Icon

Data Quality Monitor

Find and diagnose bad data

Finds and diagnoses bad data with AI rules, anomaly detection and lineage.

Covers Stages 1, 2, 4

ETL Validator for Industry icon

ETL Validator

Prove the fix reconciled

Proves a fix reconciled source to target on every record.

Covers Stage 5

BI Validator for Industry Icon

BI Validator

Protect the dashboards

Regression-tests the dashboards a fix could quietly move.

Covers Stage 5

Test Data Manager for Industry icon

Test Data Manager

Rehearse a fix safely

Masked and synthetic copies to rehearse a fix safely.

Covers Stage 4

ETL Validator for Industry icon

DataOps Suite

Govern the whole loop

Issues, contracts, CI/CD gates and the audit trail in one place.

Covers Stages 3, 6

Full Platform DataOps Suite for Industry Call to Action Icon

See the Full Platform

Deploy any product standalone or connect all five with shared lineage, unified audit trails and a single quality dashboard.

Explore DataOps Suite

Proven Outcomes

What Teams Get When They Test, Resolve and Verify

50-70%

Faster Issue Resolution

Ad optimization and marketing analytics company

100%

Records Reconciled

Every record checked, with no sampling. ETL Validator

500+

AI-Generated Quality Rules

Created in under two hours with Data Quality Monitor

5-10 min

Reconciliation per Segment

Down from 90-120 minutes. National P&C insurer, 15 lines of business

Results are from Datagaps customer engagements and will vary by environment.

Use Cases

Where Remediation Pays Back First

AI and analytics readiness

Before data feeds a model, a Copilot or a board dashboard, find what is wrong, fix it with sign-off and certify the result. Every dataset carries a verified quality score and an audit trail.

Products: Data Quality Monitor, ETL Validator
Industries: Cross-industry

Migration cutover

Defects found during reconciliation are fixed and re-tested before go-live. A leading CPG company moved from Oracle to Snowflake with zero post-go-live defects and 100% record coverage.

Products: ETL Validator, Test Data Manager
Industries: Retail, Manufacturing, BFSI

Regulated reporting

When a regulatory submission fails a check, trace it, fix it with an approver on record and export the evidence for SOX, HIPAA or APCD review.

Products: DataOps Suite, BI Validator
Industries: BFSI, Healthcare

Shared reference data

Customer, supplier, product and provider records arrive in different formats. Standardize and de-duplicate them with a reviewed rule, then verify downstream reports. Not a replacement for a master data platform.

Products: Data Quality Monitor, ETL Validator
Industries: Retail, Manufacturing, Healthcare

Contracts and pipeline gates

Turn every resolved incident into a versioned rule or contract and enforce it in CI/CD, so the same fault is stopped before production.

Products: DataOps Suite
Industries: Cross-industry

Proof Points

The Foundation Teams Already Trust

National P&C insurer

Reconciliation from 90 minutes to 5 per segment

Automated financial balancing across 15 lines of business, with audit-ready evidence for every run.

5-10 min
Ad optimization and marketing analytics

Data quality at 50 TB a day

Automated validation across 10+ sources. The data assurance team shrank by 75%.

99.9%
Leading research university

A quality score governance can stand behind

Collibra-integrated validation of student information system data.

95%

These customers use Datagaps for detection, resolution and verification today. Remediation features are rolling out to design partners.

Why Datagaps

Detection Tools Find It. Big Suites Grade Their Own Fix. Datagaps Proves It.

Capability

Datagaps

Detection-only observability

All-in-one DQ and MDM suites

AI rules, anomalies, lineage

Yes LIVE

Yes

Yes

Cleanse, standardize, merge

Rolling out ROLLING OUT

No

Yes, deepest in matching and MDM

Approval before a fix is applied

Rolling out ROLLING OUT

Not applicable

Varies

Independent re-test on 100% of records

Yes LIVE

No

Usually the same tool grades itself

Dashboard regression after the fix

Yes LIVE

No

No

Rehearse on masked data

Yes LIVE

No

Varies

Best for

Teams that need proof, audit evidence and CI/CD fit

Teams that only need alerts

Teams that need master data at scale

Approve before apply

No fix runs without a named owner's sign-off and a row preview.

ROLLING OUT

Rehearse, then release

Trial the fix on masked data. Release only after verification passes.

LIVE

Roll back

Every applied fix keeps a path to the previous state.

ROLLING OUT

Stays in your environment

Your cloud, VPC or on-premises. No data sent to external AI.

LIVE

Get Started Today

See the Full Loop on Your Own Data

Bring one failing dataset. We will show you how Datagaps finds it, traces it and verifies the fix.

Book a 30 - min Demo
Start Free 14-Day Trial

SOC 2 Type II certified | ISO 27001 certified | No credit card required for trial | Your data never leaves your environment

FAQs: Data Quality Remediation

 Common questions from Enterprise Buyers

What is data quality remediation?

Data quality remediation is the process of diagnosing, correcting and verifying data that fails quality checks. It goes beyond monitoring: find the issue, trace it to its source, assign an owner, apply an approved fix, prove it worked, and prevent it from recurring.

How is it different from data observability?

Observability tells you something is wrong. Remediation resolves it. Datagaps detects with AI rules and anomaly detection, then carries each issue through diagnosis, approval, fix and verification. See the complete guide.

Does Datagaps fix data automatically?

No. It proposes fixes and your team approves them, with a row preview and a path to roll back. Fix-stage features are rolling out now to design partners. Detection, lineage, issue tracking and verification are available today.

How do you verify a fix did not break anything downstream?

ETL Validator re-runs source-to-target reconciliation on 100% of records, and BI Validator regression-tests dependent dashboards. Both results are saved as time-stamped evidence.

How does it fit our stack, and does data leave our environment?

DataOps Suite connects to 200+ sources including Snowflake, Databricks, Microsoft Fabric and Azure Synapse, works with Collibra and Unity Catalog, and triggers from Azure DevOps, GitHub Actions, GitLab and Jenkins. It deploys in your own cloud, VPC or on-premises. The embedded LLM sends no data to external AI. SOC 2 Type II and ISO 27001 certified.

Does it replace a master data management platform?

No. Datagaps detects, fixes and proves corrections to data in your pipelines and reports. It can standardize and de-duplicate records, but many teams use it alongside an MDM platform to verify its output.

What does it cost, and how do we start?

Pricing scales by user, not by rule count or data volume; see pricing. Start with the free 14-day trial, book a demo, or estimate savings with the ROI calculator.

Learn More About Datagaps Solutions

See how enterprises are solving complex data challenges and achieving measurable ROI

Datagaps Partnership with Vega IT to Help Organisations Build Trusted Data Foundations for Digital and AI Transformation

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Datagaps Partners with Vega IT

Datagaps and Vega IT Partner to Bring Trusted Data Foundations to Digital and AI Transformation

Accelerating Databricks Lakehouse: Automated Migration Validation and Trusted Analytics

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Accelerating Databricks
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Accelerating Databricks Lakehouse: Automated Migration Validation and Trusted Analytics

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Learn how leading enterprises build AI-ready data with continuous validation, automated testing and proactive quality monitoring.
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Data Quality Monitor

Continuously assess, score, and improve your enterprise data quality using rule-based and AI-powered validation
Automated Data Quality Checks at Scale

Validate uniqueness, completeness, domain accuracy, and detect orphan records.

AI-Driven Anomaly Detection and Alerts

Identify data drift and outliers using ML-based statistical methods and IQR-based profiling.

Low-Code Rule Configuration with Data Rule Wizard

Create and deploy validation rules quickly without coding, even across large datasets.

Graphical Scoring and Monitoring Dashboard

Visualize data quality trends across models, tables, and records with actionable insights.

CI/CD and Cloud Integration Ready

Enable continuous validation across pipelines using integrated APIs and DevOps compatibility.

Test Data Manager

Generate high-quality synthetic test data securely while maintaining regulatory compliance with HIPAA, GDPR, and CCPA
AI-Powered Synthetic Test Data Generation

Automatically create realistic data based on patterns in production while masking PII/PHI.

Reduced Cost and Time for Test Data Preparation

Eliminate manual rule-writing and speed up test readiness for complex use cases.

Support for Diverse Data Formats and Models

Generate millions of records in JSON, XML, CSV, relational, or hierarchical formats.

Secure, Policy-Driven Data Masking

Ensure sensitive fields are protected using deterministic, reversible, or random masking.

Flexible Deployment Across Cloud or On-Prem

Deploy within your secure environment and integrate into automated pipelines seamlessly.

ETL Testing

Maximize the efficiency, quality, and reliability of your data pipelines through intelligent automation, validation, and scalability.
100% Data Validation Across Pipelines

Validate billions of records using Spark-powered parallel execution across on-prem and cloud sources.

Accelerated Migration and QA Cycles

Reduce migration testing time by up to 60% and QA costs by 30% with automated workflows.

Automated Metadata and Transformation Testing

Detect schema mismatches and ensure business rules are correctly applied via AI-assisted validation.

Seamless Collaboration and Governance

Enable role-based access, ALM integration, and shareable web reports to unify cross-team efforts.

Low-Code/No-Code Test Creation with AI

Empower both technical and business users to build, schedule, and execute validations using prompt-based automation.

BI Validator

Ensure accuracy, performance, and security of your Business Intelligence dashboards and reports across platforms like Tableau, Power BI, and Oracle Analytics
Automated Regression Testing Across BI Reports

Detect broken visuals or logic changes post-upgrade and data refreshes.

Cross-Platform Validation of Reports and Dashboards

Compare visuals and data across environments and BI tools with zero manual effort.

Performance and Load Testing for BI Assets

Simulate concurrent user access to measure response times and report load failures.

Access and Security Validation

Ensure only authorized groups have access to the correct records and reports.

Aesthetic and Metadata Change Detection

Identify formatting inconsistencies, filter changes, and layout drift with each release.

Products

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DataOps Suite

Intelligent Data Validation and Analytics Testing Platform with Agentic AI.

ETL Validator automated ETL testing tool

ETL Validator

Automated Data Validation and ETL Testing with Agentic AI.

BI Validator automated BI testing tool

BI Validator

Smarter BI Validation For Power BI, Tableau, Oracle Analytics – Accelerated by AI Agents.

Data Quality Monitor software

DQ Monitor

Proactive Data Quality with Agentic AI – Predict, Prevent, Govern.

Test Data Manager software

Test Data Manager

Generate compliant and realistic test data for all your testing needs, enabled by Agentic AI.

Solutions

Use Case

Data Migration Testing
Data Observability
Data Reconciliation
Compliance
APCD Solutions

Cloud

Snowflake
Databricks
Azure Synapse
Salesforce
Microsoft Fabric
APCD Solutions

Analytics

Power BI
Tableau
Oracle Analytics

Industry

Healthcare
BFSI
Life Sciences
Retail & Utilities
Manufacturing
Higher Education
Compliance Solutions
Datagaps DataOps Data Observability Tool

Data Observability

Data Reconciliation tool

Data Reconciliation

Resources

Data Testing Concepts

ETL Testing
BI Testing
Flat File Testing
Data Testing
Database Testing
Data Migration Testing
Data Warehouse Testing
DW Testing Checklist

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Blog
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Pricing

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DataOps Suite Pricing
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BI Validator Pricing
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