DataOps Suite
Data Quality and Agentic AI platform that validates, reconciles, scores, monitors and remediates data from ingestion to BI and AI.
Why one platform
Data Quality Monitor
Continuously profiles, validates and scores enterprise data, with Agentic AI that recommends rules and flags anomalies, so analytics and AI run on trusted data.
Capabilities
ETL Validator
Automates ETL/ELT testing, with Agentic AI that builds tests from mapping docs, confirming every record arrives complete, accurate and correctly transformed.
Capabilities
BI Validator
Automates testing and governance for Power BI, Tableau and Oracle Analytics, with Agentic AI that builds tests from plain English and keeps reports accurate and reliable.
Capabilities
Test Data Manager
Uses Agentic AI to detect and mask sensitive data and generate synthetic data that mirrors production, so teams can test without exposing real records.
Capabilities
Data Quality Use Case
Start from the outcome your team needs, with Agentic AI doing the groundwork.
Cloud Platforms Use Case
Validate and monitor data and its quality on the platforms you already run, backed by Agentic AI.
BI and Analytics Use Case
Keep every report accurate across your BI tools, as Agentic AI builds the tests for you.
Banking, Financial Services, and Insurance (BFSI)
Stop reporting errors before examiners find them, with every number traced from core system to board dashboard.
How Datagaps helps
Healthcare
Keep claims and clinical data accurate through every migration, without exposing patient records in testing.
How Datagaps helps
Life Sciences
Catch issues in clinical and real-world data before submission, and keep trial subject data out of test environments.
How Datagaps helps
Manufacturing
Avoid broken ERP cutovers and supplier overpayments, and catch sensor drift before it hits production.
How Datagaps helps
Retail and Utilities
Catch settlement, inventory and pricing errors before your customers or regulators do.
How Datagaps helps
Higher Education & Research
Get enrollment, financial-aid and transcript data right before SIS cutover, before students feel it.
How Datagaps helps
Trusted Data Summit
A practitioner summit focused on the validation, observability, governance, and reconciliation practices that determine whether enterprise data is ready for AI.
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Customers and tools
Support
Automating Financial and Production Data Reconciliation for a U.S. Property Insurer
A major U.S. property insurer managing 600 to 800 data interfaces and pipelines across Oracle, Oracle PeopleSoft, Guidewire, and AWS
Datagaps
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Why teams trust Datagaps
100+ Enterprise Customers
Datagaps partners with Vega IT
Bringing trusted data foundations to digital and AI transformation.
Technology Partners
Datagaps works natively with the cloud, data and BI platforms your teams already run.
Our technology partners
SI Partners
Consulting and integration firms that deliver Datagaps validation for enterprise clients worldwide.
Our SI partners
Platform
Solutions
Use cases
Cloud platforms
BI and analytics
Industries
Trusted Data Summit
A practitioner summit focused on the validation, observability, governance, and reconciliation practices that determine whether enterprise data is ready for AI.
Resources
Learn
Customers and tools
Support
Company
Datagaps
Get in touch
One platform validates every EHR and policy-system migration, monitors claims and clinical data continuously,
and generates HIPAA compliant synthetic data — built for regulated, high-stakes environments.
The Challenges
What poor data validation costs healthcare in patient safety, compliance, and revenue.
EHR migrations Cerner to Epic, legacy to cloud and policy admin replatforming on Guidewire or Duck Creek are the highest-stakes technology programs in healthcare. Every unvalidated record migrated is a potential patient-safety incident, and vendor-provided testing rarely covers the full source-to-target evidence trail.
of migration projects across industries overrun budget and timeline. Compliance in healthcare increases cost of failure
Every state brings its own rules, thresholds, and exceptions: 150+ validations per file. Audits arrive unannounced. And data quality failures quietly compound across every line of business.
in annual claims leakage attributable to data quality failures
Dashboards that show what happened but never why are data theatre — lots of charts, little impact. Validation eats 30–60 minutes per report, errors reach decision-makers unchecked, and the last-mile problem isn't the dashboard. It's that no one trusts the numbers enough to act.
of executives don’t trust their data
Production PHI and PII routinely end up in development, test, and AI/ML training environments. Every unmasked copy is a HIPAA violation waiting to be found — and in healthcare, breaches carry both regulatory penalties and irreversible damage to patient trust.
of healthcare organizations have PHI in at least one non-production environment
CMS provider-directory mandates carry real penalties for inaccurate listings. Adjudication errors that reach a paid claim, instead of being caught pre-payment compound revenue loss with compliance risk
in CMS fines issued for provider directory non-compliance since 2022
The Platform
Each product maps directly to a healthcare data problem deployed independently or as a unified suite.
Patient-safety-grade, 100% record-level validation across EHR migrations (Onprem to Cloud, Legacy system to Latest Tech). AI-generated test cases reduce manual mapping-document review time. Independent cutover assurance for EHR platform migrations(Epic, Cerner, Oracle Health) and core claims/enrollment system modernization.
Visual regression and pixel-to-pixel comparison across clinical KPI dashboards, pharma field/HQ reports, and provider-directory listings. AI-generated diff summaries flag what changed between refreshes closing the last mile between insight and action before stakeholders, CMS, or auditors see the numbers.
Continuous six-dimension quality monitoring on claims data feeds. Row-level reconciliation against payer/provider source systems for APCD submission accuracy across 7 named statewide programs including 837/ 835 EDI claims-and-remittance reconciliation, the specific mechanism state Medicaid modernization programs are re-validating today.
HIPAA-aligned deterministic, reversible, and random masking generates realistic synthetic patient, member, and claims data without exposing real PHI. Purpose-built for AI/ML training, model development, and test automation on clinical and claims datasets.
Deploy any product standalone or connect all five with shared lineage, unified audit trails, and a single compliance dashboard.
Proven Outcomes
the target of record-level validation on every EHR migration
row-level reconciliation before every state APCD filing deadline
covered by continuous data quality monitoring across the platform
HIPAA-aligned masking removes the need for production data in dev environments
Use Cases
100% source-to-target reconciliation for EHR migrations. AI-generated test cases reduce manual mapping review time. Independent from Epic and Cerner validates the data moving through them.
Independent cutover assurance for Guidewire and Duck Creek migrations. Source-to-target reconciliation across merged health system environments post-consolidation with full audit trail.
Pixel-to-pixel visual regression testing for clinical KPI dashboards and pharma field/HQ reports. Automated checks across 7–8 time-period slicers cut 30–60 minutes of manual validation per cycle.
Continuous monitoring keeps provider-directory listings accurate and flags adjudication and charge-capture anomalies before they reach a paid claim, CMS submission, or regulatory review.
Plug-and-play templates with state-specific rules automate 150+ validation checks per file. Covers 18 states with 35 healthcare data providers maintained as a stable pipeline for over 8 years.
Continuous six-dimension quality monitoring on claims data feeds. Row-level reconciliation against payer/provider source systems including 837/835 EDI claims-and-remittance reconciliation.
Deterministic, reversible masking generates realistic patient, member, and claims datasets for test automation and AI/ML model training without PHI exposure.
Privacy-safe synthetic datasets for clinical risk scoring, readmission prediction, and claims triage model development without copying production PHI into development environments.
Single audit trail covering HIPAA, APCD state mandates, and CMS directory requirements. One lineage graph from source system to regulatory submission.
Auto-generate validation rules from HL7/FHIR specs, claims mapping documents, and EDI file layouts. Self-healing tests survive schema changes across system upgrades.
Proof Points
Automated KPI card-to-trend-chart validation across 30+ production pipelines for multiple pharma clients. 70% time savings vs. manual validation, 80% reduction in turnaround time, with reusable template projects eliminating rebuild redundancy.
Automated state-specific validation across 150+ rules per file for 35 healthcare data providers. APCD submissions live in 18 states with 7 pending — maintained as a stable, production pipeline for over 8 years.
Automated regression, performance, and security testing across six Tableau production environments serving 11,000 users. 45-55% faster upgrade testing, 25% reduction in functional testing time, and 20% overall TCO reduction.
Our team will run a live demo against your specific use case your stack, your data problem, your pipeline.
SOC 2 Type II certified | ISO 27001 certified | No credit card required for trial | Your data never leaves your environment
Common questions from Enterprise Buyers
ETL Validator validates 100% of migrated records against source without PHI ever leaving your environment, and where test environments need realistic data, Test Data Manager generates HIPAA-safe synthetic records instead. Both run inside your VPC or on-prem, aligned to the controls covered on our Compliance Solutions page.
Yes — APCD Compliance Automation applies state-specific templates and 150+ validation rules to catch submission errors before a state agency does. A top 3 US health insurer used it to standardize multi-state APCD checks and remove manual per-cycle review — see the case study.
ETL Validator validates source-to-target completeness and transformation accuracy across EHR, claims, and enrollment migrations — see the full approach on our Data Migration Testing Automation page. Request a demo to walk through your specific migration scope.
BI Validator continuously checks provider-directory and claims dashboards against source data, catching stale or mismatched records before they reach a CMS-facing report. Data Quality Monitor adds ongoing profiling and anomaly detection to keep the underlying data current between refreshes.
Yes — from a top 3 US health insurer automating APCD submissions to a global pharmaceutical leader automating overnight Tableau regression testing across hundreds of dashboards. See these and more on our Case Studies page, or read how DataOps Suite drives ROI specifically in healthcare in this whitepaper.