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DataOps Suite
The complete platform
Data Quality Monitor
Continuous quality monitoring
ETL Validator
ETL, ELT and migration testing
BI Validator
Dashboard and report testing
Test Data Manager
Compliant synthetic test data
Platform Overview
See how it works

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

Agentic AI-powered test and rule creation
Agents generate data quality rules, validation tests and SQL from prompts, with your approval.
Data quality score per dataset
Shows the current score, what changed since the last run and the failing records behind it.
Validation and observability inside the pipeline
Every load is compared with its source, transformations are tested against business logic, and freshness, volume and schema changes are monitored.
Reports that match the source
Dashboard figures are compared with the source data behind them before users see them.
Test data without real records
Synthetic data that mirrors production replaces sensitive records in test environments.
Quality gates for releases
Pipeline and report changes are tested before they go to production.
Explore DataOps Suite 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

AI-recommended DQ rules and scoring
Agents suggest completeness, validity and uniqueness rules, with scores down to the column.
Data profiling
Analyzes distributions, patterns and frequencies to surface outliers.
AI-based observability
Agents monitor pipelines end to end, detect anomalies and suggest fixes.
Data catalog
Organizes models and tables in one catalog, with AI-drafted descriptions.
Data lineage
Traces data from origin to destination through every transformation.
Data quality issue tracking
Assigns every failing record to an owner and tracks it to resolution.
Explore Data Quality Monitor

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

AI-generated ETL tests
Turns mapping docs into checks for joins, transforms and edge cases.
Source-to-target validation
Matches loaded data to the source, down to record counts and required fields.
Transformation validation
Verifies business logic with SQL, Python or Scala.
Migration validation
Checks schema, row counts and integrity for cloud migrations.
Metadata change audit
Catches schema and metadata discrepancies before they break pipelines.
Root cause and adaptive tests
Pinpoints where data broke, and agents update tests when mappings change.
Explore ETL Validator

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

AI-created BI tests
Describe a test scenario in plain English, and agents build it.
Visual-to-source comparison
Compares the data behind every visual to the source using auto-generated SQL.
Regression and upgrade checks
Pixel-level comparisons across environments, with AI summaries of what changed.
Measure logic checks
Converts BI measures and calculations to SQL to confirm KPI logic.
Performance and load testing
Simulates concurrent users and measures load and refresh times.
BI governance
Finds unused measures, duplicate reports and orphaned tables.
Explore BI Validator

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

AI synthetic data generation
Learns patterns from a small sample to create production-like data.
PII discovery and masking
Detects PII and PHI and replaces it with realistic fake values.
Relationship-aware data
Keeps linked tables connected with referential integrity.
Millions of records in minutes
Scales for load, stress and performance testing.
Multi-format output
CSV, Parquet or JSON, or loaded straight into test databases.
Compliant by design
Cloud or on-prem, supporting GDPR and HIPAA requirements.
Explore Test Data Manager
Data Quality Use Case
The problem to solve
Cloud platforms
Warehouses, lakes and CRM
BI and analytics
Dashboards and reports
ROI calculator
Estimate the time and cost youโ€™ll save.

Data Quality Use Case

Start from the outcome your team needs, with Agentic AI doing the groundwork.

Data Migration Testing
Proves data moved intact from legacy and on-prem systems to new platforms through cutover.
Data Reconciliation
Finds missing, mismatched, orphan and duplicate rows between source and target.
Data Observability
Tracks freshness, volume and schema changes across pipelines, with AI agents tracing root causes.
Data Remediation
Routes failing records to owners, suggests fixes and tracks each one to resolution.
Data Compliance
Audit-ready evidence for SOX, HIPAA, GDPR, CCPA, FERPA, NAIC and MAR.
AI Ready Data
Validates the data feeding ML models and catches drift before retraining.

Cloud Platforms Use Case

Validate and monitor data and its quality on the platforms you already run, backed by Agentic AI.

Snowflake
Validates and scores data inside Snowflake with SQL pushdown, catching schema drift and migration gaps.
Databricks
Validates Delta Lake tables from Bronze to Gold inside notebooks, with results in Unity Catalog.
Microsoft Fabric
Validates Fabric data from OneLake ingestion through Lakehouse and Warehouse to Power BI semantic models.
Azure Synapse
Validates Synapse migrations and Medallion-layer transformations, with tests running in Azure DevOps.
Salesforce
Checks Salesforce objects, uploads and updates for completeness and quality on every migration and ingest.

BI and Analytics Use Case

Keep every report accurate across your BI tools, as Agentic AI builds the tests for you.

Power BI
Matches visuals to source data and tests DAX, slicers and row-level security from Dev to Prod.
Tableau
Matches dashboards to source tables, checks calculated fields and row-level security, and catches drift across versions.
Oracle Analytics
Matches analyses to SQL results, tests subject areas and permissions, and validates OBIEE to OAC migrations.
Financial services
SOX, BCBS 239 and NAIC MAR
Healthcare
HIPAA, EHR migration and APCD
Life sciences
GxP and 21 CFR Part 11
Manufacturing
ERP migration and supplier data
Retail and utilities
Settlement, pricing and NERC CIP
Higher education
FERPA, IPEDS and research data

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

Core platform migrations
Cut over banking and insurance platforms, including mainframe to cloud, without losing a record.
Regulatory reporting
Hold submissions until the data meets quality thresholds.
Financial reconciliation
Match GL to subledger automatically, with every break flagged.
Risk and credit data
Catch drift before risk, lending and pricing models degrade.
Dashboard attestation
Prove capital and board dashboards match the warehouse.
See How it Works

Healthcare

Keep claims and clinical data accurate through every migration, without exposing patient records in testing.

How Datagaps helps

EHR migrations
Confirm every record survives the move to Epic, Cerner or Oracle Health.
Claims data quality
Catch errors in claims and remittance feeds as they arrive.
APCD submissions
Check every file against state templates before you submit.
Clinical dashboards
Catch KPI and report changes after every refresh.
Provider directories
Keep provider listings accurate, current and audit-ready.
See How it Works

Life Sciences

Catch issues in clinical and real-world data before submission, and keep trial subject data out of test environments.

How Datagaps helps

Clinical data migrations
Move EDC, CTMS and LIMS data to the cloud without losses.
CRM migrations
Keep rebate calculations right through Veeva and Salesforce moves.
Pharmacovigilance data
Catch adverse event data issues before regulatory submission.
Commercial and clinical dashboards
Trace every KPI back to source clinical and lab records.
De-identified trial data
Keep subject data out of test, analytics and AI environments.
See How it Works

Manufacturing

Avoid broken ERP cutovers and supplier overpayments, and catch sensor drift before it hits production.

How Datagaps helps

ERP migrations
Move BOM, inventory and financial data across Oracle, SAP and cloud without breaks.
Supplier reconciliation
Catch overpayments and duplicate invoices with three-way PO, ASN and invoice matching.
Sensor data quality
Catch drift before it skews predictive maintenance and yield models.
Operations dashboards
Catch report breaks after every release or upgrade.
Safe ERP test data
Keep supplier pricing and production volumes out of lower environments.
See How it Works

Retail and Utilities

Catch settlement, inventory and pricing errors before your customers or regulators do.

How Datagaps helps

Settlement reconciliation
Match marketplace payout files to the general ledger, record by record.
Inventory accuracy
Keep POS, ERP and warehouse numbers in sync.
Pricing and promotions
Catch price and promotion mismatches across channels.
Reliability dashboards
Keep SAIDI and SAIFI reliability reporting accurate.
BI under load
Make sure reports hold up for thousands of concurrent users.
See How it Works

Higher Education & Research

Get enrollment, financial-aid and transcript data right before SIS cutover, before students feel it.

How Datagaps helps

SIS migrations
Validate moves from Banner or PeopleSoft to Workday Student or Ellucian.
Financial-aid reconciliation
Match aid and Bursar data to the general ledger.
Enrollment reporting
Keep census and admissions dashboards consistent from cycle to cycle.
Federal reporting
Monitor data quality ahead of IPEDS submissions.
Safe student data
Test and train AI without exposing real student records.
See How it Works

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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Solutions

Use cases

Data migration testing
Proves data moved intact from legacy and on-prem systems to new platforms through cutover.
Data reconciliation
Finds missing, mismatched, orphan and duplicate rows between source and target.
Data observability
Tracks freshness, volume and schema changes across pipelines, with AI agents tracing root causes.
Data remediation
Routes failing records to owners, suggests fixes and tracks each one to resolution.
Data Compliance
Audit-ready evidence for SOX, HIPAA, GDPR, CCPA, FERPA, NAIC and MAR.
AI Ready Data
Validates the data feeding ML models and catches drift before retraining.

Cloud platforms

Snowflake
Validates and scores data inside Snowflake with SQL pushdown, catching schema drift and migration gaps.
Databricks
Validates Delta Lake tables from Bronze to Gold inside notebooks, with results in Unity Catalog.
Microsoft Fabric
Validates Fabric data from OneLake ingestion through Lakehouse and Warehouse to Power BI semantic models.
Azure Synapse
Validates Synapse migrations and Medallion-layer transformations, with tests running in Azure DevOps.
Salesforce
Checks Salesforce objects, uploads and updates for completeness and quality on every migration and ingest.

BI and analytics

Power BI
Matches visuals to source data and tests DAX, slicers and row-level security from Dev to Prod.
Tableau
Matches dashboards to source tables, checks calculated fields and row-level security, and catches drift across versions.
Oracle Analytics
Matches analyses to SQL results, tests subject areas and permissions, and validates OBIEE to OAC migrations.

ROI calculator โ†’

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Home Industries Banking, Financial Services and Insurance

Audit-Ready Data Assurance for Banking, Insurance,
and Regulatory Reporting

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

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The Challenges

Six Business Risks Every BFSI Team Faces

What weak data validation actually costs โ€” in enforcement, capital, and trust

Client Onboarding & KYC/AML Icon

Client Onboarding & KYC/AML

What's the cost of not thoroughly evaluating a customer at onboarding?

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 โ€” and enforcement consequences are measured in billions.

$3.09B โ€” TD Bankโ€™s combined AML penalties (2024), after failing to detect
$670M+ in suspicious transactions

Risk Management & Monitoring Icon

Risk Management & Monitoring

What's the cost of a monitoring gap nobody notices until a regulator does?

Risk models and early-warning systems depend on continuously validated data โ€” 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.

$2B โ€” Danske Bankโ€™s US/Danish penalties for allowing suspicious transactions to flow through one branch, undetected, for years
Credit, Lending & Underwriting Decisions Icon

Credit, Lending & Underwriting Decisions

What's the cost of a credit or underwriting decision built on bad data?

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 โ€” bad credit calls in banking, adverse selection in insurance.

5% โ€” of borrowers get a worse rate โ€” or a wrongful denial โ€” because of an error in the data feeding the decision.โ€  Source: FTC, National Study of Credit Report Accuracy (ftc.gov).
Finance & Regulatory Reporting Icon

Finance & Regulatory Reporting

What's the cost of a capital plan โ€” or a regulatory filing โ€” the examiner can't fully trust?

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.

$12.9M โ€” average annual cost of poor data quality per organization (Gartner). In lending and underwriting, that shows up as mispriced risk and regulatory exposure
AI Governance & Attestation Icon

AI Governance & Attestation

What's the cost of an AI model no one can fully explain to an examiner?

SR 26-2 now expects full lifecycle lineage from development through monitoring โ€” 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.

86% of financial services leaders arenโ€™t confident their data can be used for decision-making (InterSystems) โ€” and SR 26-2 now expects full model lineage

Claims, Underwriting & Actuarial Accuracy

What's the cost of a claims file nobody reconciles until the regulator does?

California's Department of Insurance reviewed 220 wildfire claims from one insurer in 2026 and found 398 violations in 114 of them โ€” 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 โ€” violations found across just 220 reviewed claims โ€” up to ~$4M in penalties, the largest sought this century following a wildfire disaster.

The Platform

How DataOps Suite Solves It

Each product solves a critical BFSI data problem โ€” deployed independently or as a unified suite.

ETL Validator for Industry icon

ETL Validator

Solves: Financial reconciliation & regulatory reporting controls

100% row-level source-to-target reconciliation via Spark-powered parallel execution. Validates billions of records at Snowflake, Databricks, and Azure Synapse native speed.

Migration Validation
Spark-scale
Source-to-Target Reconciliation
BI Validator for Industry Icon

BI Validator

Solves: Silent errors in regulatory dashboards & board reporting

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.

Dashboard Regression
BCBS 239 Reports
AI Diff Summaries
Data Quality Monitor for Industry Icon

Data Quality Monitor

Solves: Risk monitoring & KYC/AML compliance

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.

BCBS 239
Anomaly Detection
Continuous Monitoring
Test Data Manager for Industry icon

Test Data Manager

Solves: Sensitive data in credit, underwriting & AI model development

Deterministic and reversible masking aligned to PCI-DSS. Eliminates the common and risky practice of copying production financial data into test and development environments.

PCI-DSS
Data Masking
Synthetic Data
DataOps Suite for Industry Icon

DataOps Suite

Solves: Unified evidence trail for multi-regulator compliance

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

SOX
GDPR
Full Lineage
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 compliance dashboard.

Explore DataOps Suite

Proven Outcomes

Measurable Impact Across BFSI Teams

60%

Faster Migration Testing

Up to 60% reduction in migration testing time, validated across enterprise cloud migrations

67%

Faster Close & Reconciliation

A Midwest insurer cut 3+ weeks from period close and reduced premium variance tracing from days to hours

100%

Audit-Ready Compliance Coverage

Continuous, exportable lineage from ingestion to reporting, satisfying BCBS 239, SOX, NAIC MAR, and SR 26-2 evidence requirements

โ‚ฌ20M

Maximum Fine Exposure Addressed

GDPR and BCBS 239 fines avoided through validated, documented data movement with end-to-end evidence.

Use Cases

Banking Use Cases by Product

CCAR & Regulatory Submission Validation

Source-to-submission reconciliation for FR Y-14A/Q schedules and PPNR inputs. Timestamped evidence trail across every data hop โ€” from loan-tape and GL sources through to the submitted capital plan.

Financial Controls Reconciliation 

Guaranteed delivery controls, system balancing, and financial balancing across policy, claims, and premium systems. Proven at a U.S. property insurer across 600โ€“800 data interfaces.

Core Platform Replatforming 

Independent cutover assurance for Temenos and Thought Machine in banking, Guidewire and Duck Creek in insurance โ€” without competing with the platform vendor.

CCAR & Stress Test Dashboard Attestation

Pixel-level visual regression across FR Y-14 dashboards, DFAST visuals, and board capital adequacy decks โ€” confirming the number on the dashboard matches the number in the warehouse.

Regulatory Report Integrity

AI-generated diff summaries flag what changed between refreshes across BCBS 239 risk-aggregation reports, NAIC MAR filings, and SOX close packages โ€” before auditors or examiners see them.

SR 26-2 & BCBS 239 Continuous Monitoring

Six-dimension rules plus anomaly and drift detection deliver the continuous monitoring SR 26-2 and BCBS 239 require โ€” keeping risk models and KYC/AML rules current.

Pre-Close & NAIC MAR Quality Gates

Automated DQ rules that block period-close and regulatory submissions until source data meets defined thresholds โ€” replacing manual sign-off checklists for SOX close, NAIC MAR filings, and CCAR data packages.

PCI-DSS & GLBA Safe Test Environments

Deterministic/reversible masking aligned to PCI-DSS and GLBA. Eliminates copying production financial data, policyholder PII, or applicant records into development and test environments.

Synthetic Data for Credit & AI Models

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 โ€” without privacy risk.

Unified Evidence Trail for Multi-Regulator Compliance

Single audit trail covering BCBS 239, SOX, NAIC MAR, SR 26-2, and GDPR. One lineage graph from source to submission โ€” the evidence engine beneath your model risk and regulatory reporting stack."

AI-Generated Test Cases

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

Customer Evidence in Financial Services

Fortune 100 Financial Services

Mainframe-to-Snowflake Migration Validation

Record-level validation across a Fortune 100 Financial Services company's large-scale mainframe-to-cloud migration. Zero post-go-live defects

Read Case Study
Fintech Industry Leader

AI/ML Data Validation at Scale

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.

Read Case Study
Midwest Insurer

Financial Reconciliation Automation

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.

Read Case Study
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Our team will run a live demo against your specific use case your stack, your data problem, your pipeline.

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FAQs: Banking & Financial Services

 Common questions from Enterprise Buyers

How is Datagaps different from QuerySurge, iceDQ, Ataccama, Informatica, or DQLabs?

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 โ€” see the case study.

Can Datagaps run entirely on-prem or in our VPC cloud for BCBS 239 and other regulated data?

Yes. DataOps Suite deploys entirely within your own VPC or on-prem environment โ€” no data ever leaves your perimeter โ€” 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. 

How quickly can we validate a mainframe-to-Snowflake or core-banking migration?

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 โ€” read the case study โ€” and our Snowflake Testing Automation page covers the same approach for schema conversion and Medallion-layer testing.

How does Datagaps support IFRS 17, LDTI, and ORSA reporting for insurers?

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.

Does Datagaps handle GL-to-subledger or Nostro/Vostro reconciliation?

Yes โ€” 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 โ€” see how.

What do you mean by "Guaranteed Delivery," "System Balancing," and "Financial Balancing" controls?

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.

How does Datagaps keep risk and regulatory dashboards accurate through every refresh?

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.

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 โ€“ Accelerated by AI Agents.

Data Quality Monitor software

DQ Monitor

Proactive Data Quality with Agentic AI โ€“ 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

AI-Ready Data
Data Migration Testing
Data Observability
Data Reconciliation
Data Remediation
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

Academy

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