DataGaps

What DataGaps does

Datagaps builds trust into data by powering confident BI analytics, compliant AI models, and clean, zero-defect migrations at enterprise scale. It is a Gartner-recognized platform in both DataOps Tools and Data Observability guides, replacing multiple tools with one platform built on shared rules, lineage, and governance. Its Agentic AI layer auto-generates tests, self-heals through schema changes, summarizes BI report differences, and recommends quality rules, freeing data teams to focus on decisions instead of defect hunting. The platform ensures consistent data definitions from source models to semantic layer, BI dashboards, and AI outputs, ensuring one trusted truth. It validates the entire data pipeline—ingestion, ETL, Data Quality, BI consumption, and AI model input—on a single platform.

Products

• DataOps SuiteA unified platform for end-to-end data testing, data observability, and analytics governance. It automates data validation and ETL testing with agentic AI, and is the comprehensive end-to-end data validation platform for automating Data Integration and Data Management projects.
• ETL ValidatorAutomated data validation and ETL testing with agentic AI. It simplifies testing of Data Integration, Data Warehouse, and Data Migration projects and helps compare data from multiple sources.
• BI ValidatorSmarter BI validation for Power BI, Tableau, and Oracle Analytics. It is a tool for Functional, Regression, Performance, and Stress Testing on BI platforms such as Microsoft Power BI, Tableau, Oracle Analytics, BusinessObjects, and Cognos.
• Data Quality MonitorProactive data quality with agentic AI to predict, prevent, and govern. It continuously assesses, scores, and improves enterprise data quality using rule-based and AI-powered validation, and ensures greater accuracy by closely monitoring data output.
• Test Data Manager (TDM)Compliant, realistic synthetic test data, enabled by agentic AI. It is a platform for Test Data Management with Data Masking and on-demand test data generation capabilities, generating high-quality synthetic test data securely while maintaining regulatory compliance with HIPAA, GDPR, and CCPA.

Key capabilities

• DataOps Validation
• Data Observability
• Analytics Governance
• Agentic AI layer for test generation, self-healing, BI report difference summarization, and quality rule recommendations
• End-to-end data pipeline validation (ingestion, ETL, Data Quality, BI consumption, AI model input)
• Shared rules, lineage, and governance across the platform
• Native data source connectors
• Automated data validation and ETL testing
• Smarter BI validation
• Proactive data quality monitoring
• Compliant, realistic synthetic test data generation
• US patent (US 201220290527 – Data extraction & testing methods ELV architecture)
• Informatica certification
• SOC 2 Type II certified
• Embedded LLM (Large Language Model)
• Automated SOX reconciliation
• HIPAA-compliant validation
• PII-safe AI pipelines
• Automated EHR migration testing
• Automated SAP and Oracle ERP migration validation
• FERPA-compliant validation for student systems
• PMS and OTA feed validation
• Validated POS, supplier data, and demand forecasts
• BI Tool Data Sources support
• BI Functional Testing
• Transformation Testing
• Data Reconciliation
• Data Profiling
• Mapping Manager
• Metadata Testing
• Stress Testing
• BI Regression Testing
• BI Upgrade Testing
• BI Security Testing
• BI Analyzer
• Filter Datasets
• Ticketed 9x5 Support
• Knowledge Resources
• Regular Upgrades
• Roadmap briefings
• Low-Code Integration
• State-Specific Templates for APCD
• Alerts and Reporting for APCD
• Automated Rule Application for APCD (150+ rules per file)
• End-to-End Encryption and Data Handling for APCD
• Flat File Testing (with file watcher)
• Identify differences between Report & SQL output
• Unified Automation for APCD Compliance
• Automated Data Quality Checks at Scale (uniqueness, completeness, domain accuracy, orphan records)
• AI-Driven Anomaly Detection and Alerts (data drift, outliers, ML-based statistical methods, IQR-based profiling)
• Low-Code Rule Configuration with Data Rule Wizard
• Graphical Scoring and Monitoring Dashboard
• CI/CD and Cloud Integration Ready
• AI-Powered Synthetic Test Data Generation (masking PII/PHI)
• Support for Diverse Data Formats and Models (JSON, XML, CSV, relational, hierarchical)
• Secure, Policy-Driven Data Masking (deterministic, reversible, random)
• Flexible Deployment Across Cloud or On-Prem
• 100% Data Validation Across Pipelines (Spark-powered parallel execution)
• Automated Metadata and Transformation Testing (schema mismatches, business rules)
• Seamless Collaboration and Governance (role-based access, ALM integration, shareable web reports)
• Low-Code/No-Code Test Creation with AI (prompt-based automation)
• Automated Regression Testing Across BI Reports
• Cross-Platform Validation of Reports and Dashboards
• Performance and Load Testing for BI Assets
• Guaranteed Auditability via Automated Evidence
• Shift from Periodic to Continuous Compliance
• Complete Traceability and Reproducibility
• Compliance Evidence & Audit Readiness
• Enterprise Collaboration (central repository, scheduled runs, email/web reporting)
• DB Metadata Testing (audits schema changes)
• Enables Continuous Integration for BI (Jenkins/GitLab, CLI/REST)
• Scheduling & Email Notifications
• Continuous Data Integrity Assurance
• Baselining Capabilities for incremental ETL/SCDs/regression
• Compare different environments (Dev/UAT/Prod)

Use cases

• Confident BI analytics
• Compliant AI models
• Clean, zero-defect data migrations
• Data warehouse testing automation
• OBIEE testing automation
• BI testing
• ETL testing
• Flat file testing
• Database testing
• Data migration testing
• Cloud migration testing
• DataOps
• Big Data testing
• Data quality
• Test data management
• Data reconciliation
• Data monitoring
• Solving silent data degradation across the pipeline
• Addressing BI metrics that don’t match the source
• Preventing data loss post-migration
• Mitigating AI initiative failure and compliance overhead due to bad training data
• Automated SOX reconciliation across trading and risk pipelines
• HIPAA-compliant validation
• PII-safe AI pipelines
• Automated EHR migration testing
• Automated SAP and Oracle ERP migration validation
• FERPA-compliant validation for student systems
• PMS and OTA feed validation
• Validated POS, supplier data, and demand forecasts
• APCD compliance automation (state rulesets, validation, secure submissions)
• Ensuring data accuracy and compliance with state-specific requirements for medical insurance companies
• Managing high volume of rules per file required by APCDs
• Real-time data validation for APCD submissions
• Reducing risk of financial penalties for non-compliance
• Automating operations for cost reduction in finance
• Accelerating time-to-market in financial sector
• Risk mitigation in finance through quality assurance
• Validating complex Oracle-to-Snowflake data migrations
• Scaling ETL testing across large enterprise datasets
• Improving data quality while reducing manual testing effort
• Automated regression testing across BI reports
• Cross-platform validation of reports and dashboards
• Performance and load testing for BI assets
• Simplifying regulatory compliance for SOX, APCD, and NAIC MAR
• Continuous compliance monitoring
• Ensuring data integrity and reliability for data integration and data management projects
• Functional, regression, performance, and stress testing on BI platforms
• Data masking
• On-demand test data generation
• Automating data integration and data management projects
• Ensuring data integrity and reliability in life sciences
• Efficient data processing in life sciences
• Data integration in life sciences
• Data quality assurance in life sciences
• Smooth data migration in life sciences
• Dashboard validation in life sciences
• Data visualization assurance in life sciences
• Performance testing of BI tools in life sciences
• Reference data testing in life sciences
• Precision in merchandising, shelf optimization, and sales maximization in retail
• Elevating retail software’s performance, reliability, and security
• Rapid response capability in retail
• Enhanced decision-making with data-driven insights in retail
• Personalization at scale in retail
• Building trust through data integrity in retail
• Effective segmentation and cross-sell strategies in retail
• Unified view of inventory and supply chain in retail

AI approach

Datagaps uses an "Agentic AI layer" and "Agentic AI" across its products (ETL Validator, DQ Monitor, TDM) to automate test generation, self-heal through schema changes, summarize BI report differences, recommend quality rules, predict/prevent data quality issues, and generate compliant synthetic test data. It also uses AI for anomaly detection, ML-based statistical methods, and IQR-based profiling for data quality, and prompt-based automation for low-code/no-code test creation.

Tech named: Agentic AI, LLM, ML anomaly detection, ML-based statistical methods, IQR-based profiling, Generative AI, Spark

Industries served

• Banking & Financial Services
• Healthcare & Life Sciences
• Consumer Packaged Goods
• Higher Education & Research
• Hospitality
• Retail
• Insurance
• Government & Public Sector

What it says sets it apart

• Only platform Gartner recognizes in both DataOps Tools and Data Observability guides
• Replaces three-plus tools (ETL testing, BI validation, data quality monitoring, test data management) with one platform
• Built on shared rules, lineage, and governance
• Agentic AI layer for auto-generating tests, self-healing, summarizing BI report differences, and recommending quality rules
• US patent (US 20122029027 – Data extraction & testing methods ELV architecture)
• Informatica certification
• SOC 2 Type II certified
• Embedded LLM (Large Language Model) ensures data never leaves the environment
• Validates the entire pipeline (ingestion, ETL, Data Quality, BI consumption, AI model input) on a single platform
• 100% record-level validation coverage
• 70% QA cycle time reduction
• 80% faster BI regression testing
• Zero post-go-live data defects
• No limits on supported data sources or data volume
• Licenses apply across development, testing, and production environments
• Offers both named-user and concurrent license models
• Pre-built rulesets for each state for APCD compliance
• Automates over 150 rules per file for APCD compliance
• AI-driven anomaly detection and alerts
• Low-code rule configuration with Data Rule Wizard
• AI-powered synthetic test data generation
• Secure, policy-driven data masking
• Spark-powered parallel execution for billions of records validation
• Low-code/no-code test creation with AI (prompt-based automation)
• Guaranteed auditability via automated evidence
• Shift from periodic to continuous compliance
• Complete traceability and reproducibility
• Experts in data testing automation with direct access to product development team