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DataOps Suite
The complete platform
Platform Overview
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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.
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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.
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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.
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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.
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Data Quality Use Case
The problem to solve
ROI calculator
Estimate the time and cost you’ll save.
Financial services
SOX, BCBS 239 and NAIC MAR

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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Nov 19th, 2026
Technology Partners
Cloud, data and BI platforms

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Technology Partners

Datagaps works natively with the cloud, data and BI platforms your teams already run.

Our technology partners

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SI Partners

Consulting and integration firms that deliver Datagaps validation for enterprise clients worldwide.

Our SI partners

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CitiusTech logo
Mindtree logo
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+6 more partners
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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 →

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 Back to Whitepapers

The ETL Testing Playbook: From Assessment to Action

ETL Testing Playbook

From Data Chaos to Trusted Decisions

Silent data errors can derail analytics and decisions. Your data deserves better than manual checks.
The ETL Testing Playbook is more than an eBook—it’s a transformation toolkit for modern data teams.

Inside, you’ll discover:

  • ETL Validator Readiness Checklist – A step-by-step guide to implement automated testing.
  • Measure automated data testing effectiveness using SLIs/SLOs.
  • 90-Day Action Plan – A practical roadmap to modernize your data testing fast.
  • Modern Data Testing Readiness Assessment – Benchmark your current maturity level.

Whether you’re migrating to Snowflake or Databricks, or building CI/CD pipelines this playbook gives you the frameworks and tools to ensure every decision is built on trusted data. Stop losing time, money, and trust to silent data errors. Automated testing isn’t a luxury—it’s a requirement.

Fill out the form to download the ebook for a detailed representation.

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