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 number your Teams reports from core-system migration to the board dashboard
with evidence ready before an examiner asks for it
The Challenges
What weak data validation actually costs — in enforcement, capital, and trust
KYC verification, AML watchlist screening, and CIP onboarding checks are only as reliable as the data feeding them. When identity data is incomplete or inconsistent, suspicious transactions go undetected — and enforcement consequences are measured in billions.
$3.09B — TD Bank’s combined AML penalties (2024), after failing to detect
$670M+ in suspicious transactions
Risk models and early-warning systems depend on continuously validated data — not data checked once at go-live and assumed correct. When drift goes undetected, risk scores degrade silently until an examiner or an event exposes the gap.
Every credit approval and underwriting judgment is downstream of the data that feeds it. When applicant data or risk scores carry undetected errors, the result is mispriced risk — bad credit calls in banking, adverse selection in insurance.
CCAR capital plans in banking. NAIC MAR filings in insurance. SOX close across public companies. Each demands that every submitted number traces back to its source with auditable evidence. Without automated reconciliation, evidence assembly becomes a multi-week fire drill before every review.
SR 26-2 now expects full lifecycle lineage from development through monitoring — AI models included. But 86% of financial services leaders aren't confident their data supports decision-making. Without an evidence trail from training data to the dashboard an examiner reviews, attestation becomes the bottleneck.
California's Department of Insurance reviewed 220 wildfire claims from one insurer in 2026 and found 398 violations in 114 of them — delayed investigations, underpayment, and inconsistent claims data across adjusters. Every finding traces back to the same root cause: claims, underwriting, and actuarial data that isn't governed, reconciled, or audit-ready before it reaches a regulator, an auditor, or an IFRS 17/LDTI reporting cycle.
398 — violations found across just 220 reviewed claims — up to ~$4M in penalties, the largest sought this century following a wildfire disaster.
The Platform
Each product solves a critical BFSI data problem — deployed independently or as a unified suite.
100% row-level source-to-target reconciliation via Spark-powered parallel execution. Validates billions of records at Snowflake, Databricks, and Azure Synapse native speed.
Visual and Cell level regression across Power BI, Tableau, Looker and Oracle Analytics. Trace any dashboard number back to its source record, with AI-generated summaries of exactly what changed between refreshes.
Continuous six-dimension quality monitoring satisfying BCBS 239 risk-data aggregation in banking, NAIC MAR financial controls in insurance, and SR 26-2 model lifecycle monitoring for AI/ML. Drift detection keeps monitoring rules current as data sources and risk typologies evolve.
Deterministic and reversible masking aligned to PCI-DSS. Eliminates the common and risky practice of copying production financial data into test and development environments.
AI proposes reconciliation rules and break-detection logic from mapping docs and SQL your team approves before anything runs. Self-healing tests survive schema changes. One lineage graph with RBAC and column-level audit trails covers BCBS 239, SOX, NAIC MAR, and GDPR from a single source of evidence
Deploy any product standalone or connect all five with shared lineage, unified audit trails, and a single compliance dashboard.
Proven Outcomes
Up to 60% reduction in migration testing time, validated across enterprise cloud migrations
A Midwest insurer cut 3+ weeks from period close and reduced premium variance tracing from days to hours
Continuous, exportable lineage from ingestion to reporting, satisfying BCBS 239, SOX, NAIC MAR, and SR 26-2 evidence requirements
GDPR and BCBS 239 fines avoided through validated, documented data movement with end-to-end evidence.
Use Cases
Source-to-submission reconciliation for FR Y-14A/Q schedules and PPNR inputs. Timestamped evidence trail across every data hop — from loan-tape and GL sources through to the submitted capital plan.
Guaranteed delivery controls, system balancing, and financial balancing across policy, claims, and premium systems. Proven at a U.S. property insurer across 600–800 data interfaces.
Independent cutover assurance for Temenos and Thought Machine in banking, Guidewire and Duck Creek in insurance — without competing with the platform vendor.
Pixel-level visual regression across FR Y-14 dashboards, DFAST visuals, and board capital adequacy decks — confirming the number on the dashboard matches the number in the warehouse.
AI-generated diff summaries flag what changed between refreshes across BCBS 239 risk-aggregation reports, NAIC MAR filings, and SOX close packages — before auditors or examiners see them.
Six-dimension rules plus anomaly and drift detection deliver the continuous monitoring SR 26-2 and BCBS 239 require — keeping risk models and KYC/AML rules current.
Automated DQ rules that block period-close and regulatory submissions until source data meets defined thresholds — replacing manual sign-off checklists for SOX close, NAIC MAR filings, and CCAR data packages.
Deterministic/reversible masking aligned to PCI-DSS and GLBA. Eliminates copying production financial data, policyholder PII, or applicant records into development and test environments.
Generate realistic synthetic transaction, applicant, claims, and underwriting datasets for credit model development, CCAR stress testing, actuarial model testing, and AI/ML challenger model validation — without privacy risk.
Single audit trail covering BCBS 239, SOX, NAIC MAR, SR 26-2, and GDPR. One lineage graph from source to submission — the evidence engine beneath your model risk and regulatory reporting stack."
Auto-generate validation rules from mapping documents, FR Y-14 and IFRS 17/LDTI specs, and SQL logic. Self-healing tests survive schema changes across CCAR and actuarial reporting cycles without manual updates.
Proof Points
Record-level validation across a Fortune 100 Financial Services company's large-scale mainframe-to-cloud migration. Zero post-go-live defects
Automated validation of AI/ML training data and model outputs before they reach financial decisions. End-to-end data quality checks across third-party sources, replacing manual correction with rule-based validation at scale.
Embedded reconciliation logic directly into data pipelines for NAIC MAR compliance. Premium variance trace reduced from multiple days to hours, with audit-ready reconciliation across all data layers.
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
Most tools validate a sample or bolt reconciliation onto a broader governance suite. Datagaps validates 100% of records at the source-to-target level with Agentic AI generating test cases directly from your mapping documents, cutting validation effort without cutting coverage. A Fortune 100 financial services firm ran ETL Validator continuously through a mainframe-to-Snowflake migration and achieved 100% validation coverage without growing headcount — see the case study.
Yes. DataOps Suite deploys entirely within your own VPC or on-prem environment — no data ever leaves your perimeter — with audit-ready evidence trails built for BCBS 239, SOX, and NAIC MAR. See the full control set on our Compliance Solutions page, or how it applies to banking and insurance specifically on our Financial Services Data Testing page.
ETL Validator runs source-to-target reconciliation in parallel with your migration instead of after it, so validation never sits on your critical path. A Fortune 100 financial services company validated its entire mainframe-to-Snowflake migration this way — read the case study — and our Snowflake Testing Automation page covers the same approach for schema conversion and Medallion-layer testing.
BI Validator traces every number in a regulatory filing back to its source record, so IFRS 17, LDTI, and ORSA reports carry an audit trail an examiner can follow. Pair it with Compliance Solutions for the underlying controls and evidence documentation these frameworks require.
Yes — Data Reconciliation automates GL-to-subledger and Nostro/Vostro matching at the record and amount level, flagging breaks instead of leaving them for manual review. A Midwest insurer used this approach to automate its entire quarterly NAIC MAR reconciliation cycle — see how.
These are the three levels of proof examiners look for in a reconciliation control: Guaranteed Delivery confirms every record that left the source arrived at the target, System Balancing confirms source and target totals match at the control-file level, and Financial Balancing confirms the dollar amounts tie out. ETL Validator runs all three automatically on every reconciliation job, backed by Data Reconciliation for the underlying matching logic.
BI Validator re-validates every dashboard against its SQL source on every refresh catching drift in Power BI, Tableau, or Oracle Analytics before an examiner does. See the approach on our Power BI Testing Automation page.
See how enterprises are solving complex data challenges and achieving measurable ROI
Datagaps and Vega IT Partner to Bring Trusted Data Foundations to Digital and AI Transformation
Accelerating Databricks Lakehouse: Automated Migration Validation and Trusted Analytics
Learn how leading enterprises build AI-ready data with continuous validation, automated testing and proactive quality monitoring.