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.
Learn
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
Datagaps is recognized as a Representative Vendor in the Data Observability Market Guide by Gartner
Generate and recommend data quality rules from user inputs, metadata, or uploaded documents, reducing manual setup.
Empower business teams to define validation rules and enable continuous data quality monitoring across datasets and data pipelines.
Each rule produces a score that rolls up across tables and systems, giving you clear visibility into data health through scorecards.
Monitor patterns, ranges, and trends with ML‑based anomaly detection to identify deviations early and enhance overall data quality monitoring.
Automatically identify schema changes and data shifts that can impact data quality and downstream systems.
Set acceptable quality thresholds for key data elements and continuously monitor when data quality falls outside those predefined limits.
Validate that key business metrics mean the same thing across systems and reports, catching semantic mismatches that lead to conflicting numbers.
Automatically capture end‑to‑end data lineage to understand origins, transformations and downstream dependencies. View data quality scores within lineage flows to assess impact when issues arise.
Enforce consistent schema, types, and rules with versioned data contracts that validate every dataset against the latest approved version.
Integrate with governance and data catalog platforms like Unity Catalog and Collibra to share lineage, metadata, and data quality signals, enabling consistent visibility and stronger cross‑system alignment.
Reuse and version data quality assets through imports, exports, and Git integration to support consistent deployment across pipelines and environments.
Automate data quality monitoring with scheduled or pipeline‑triggered executions, ensuring continuous validation across ingestion, staging, and warehouse layers.
Continuously track data quality issues as they emerge and evolve, with visibility into issue history, resolution timelines, and outstanding problems.
Uses historical profiling to anticipate anomalies and data drift early.
Uses AI to auto‑generate clear, human‑readable dataset descriptions for better business understanding.
Our clients receive great value from our data validation solutions
— Kasturi Sen
— Ekatmika Khatua
Reduce your data testing costs dramatically with Data Quality Testing –
Get your 14 days free trial now.
Try Data Quality Monitor free for 14 days or contact us for a demo.
Agentic AI generates context-aware validation rules from simple prompts, reduces manual setup, and accelerates the creation of complex business rules without scripting.
It uses rule‑based validation, data profiling, ML‑based anomaly detection, and source‑to‑target reconciliation to ensure accurate, complete, and consistent data throughout the pipeline.
Yes. Data Quality Monitor integrates with data governance platforms to share lineage, metadata, and data quality insights for stronger governance and unified visibility.
Data Quality Monitor offers a more complete approach to data quality than traditional tools by combining automated rule‑based validation, ML‑powered anomaly detection, deep data profiling, and end‑to‑end reconciliation in one unified platform. It supports the full data pipeline from ingestion to BI, provides observability signals to detect issues early, and integrates with governance systems to enhance visibility and control. With dashboards, scorecards, low‑code automation, and Agentic AI for rule creation, Data Quality Monitor helps teams deliver accurate, trusted data at scale while reducing the operational effort typically required to maintain high data quality.
DataOps Suite – Data Quality Monitor helps define data rules using an easy-to-use web interface and share the results with your business users.
Validate uniqueness, completeness, domain accuracy, and detect orphan records.
Identify data drift and outliers using ML-based statistical methods and IQR-based profiling.
Create and deploy validation rules quickly without coding, even across large datasets.
Visualize data quality trends across models, tables, and records with actionable insights.
Enable continuous validation across pipelines using integrated APIs and DevOps compatibility.
Automatically create realistic data based on patterns in production while masking PII/PHI.
Eliminate manual rule-writing and speed up test readiness for complex use cases.
Generate millions of records in JSON, XML, CSV, relational, or hierarchical formats.
Ensure sensitive fields are protected using deterministic, reversible, or random masking.
Deploy within your secure environment and integrate into automated pipelines seamlessly.
Validate billions of records using Spark-powered parallel execution across on-prem and cloud sources.
Reduce migration testing time by up to 60% and QA costs by 30% with automated workflows.
Detect schema mismatches and ensure business rules are correctly applied via AI-assisted validation.
Enable role-based access, ALM integration, and shareable web reports to unify cross-team efforts.
Empower both technical and business users to build, schedule, and execute validations using prompt-based automation.
Detect broken visuals or logic changes post-upgrade and data refreshes.
Compare visuals and data across environments and BI tools with zero manual effort.
Simulate concurrent user access to measure response times and report load failures.
Ensure only authorized groups have access to the correct records and reports.
Identify formatting inconsistencies, filter changes, and layout drift with each release.
Intelligent Data Validation and Analytics Testing Platform with Agentic AI.
Automated Data Validation and ETL Testing with Agentic AI.
Smarter BI Validation For Power BI, Tableau, Oracle Analytics – Accelerated by AI Agents.
Proactive Data Quality with Agentic AI – Predict, Prevent, Govern.
Generate compliant and realistic test data for all your testing needs, enabled by Agentic AI.
