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Home Industries Life Sciences

Validation-Grade Data Assurance for Pharma,
Biotech, and Medical Device Companies

One platform validates every clinical and commercial data migration, monitors real-world data quality continuously,
and generates de-identified synthetic trial data built for 21 CFR Part 11, GAMP 5, and GxP

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The Challenges

Five Business Risks Every Life Sciences Team Faces

What weak data validation costs life sciences in regulatory risk, revenue leakage,pharma and biotech.

Clinical Trial Data Migration Risk for Life Sciences

Clinical Trial Data Migration Risk

What does it cost when study data or commercial records don't reconcile after go-live?

Every pharma company is facing a platform migration right now: legacy EDC and CTMS into Veeva Vault, and a commercial CRM move that's reshaping field operations. Migration is where trust breaks. A single undetected mapping error can turn into an FDA finding or a broken rebate payout downstream.

83%

of clinical data migration projects fail or overrun; FDA findings from migration errors can delay approvals by years

21 CFR Part 11 & GAMP 5 Validation Evidence For Life Sciences

21 CFR Part 11 & GAMP 5 Validation Evidence

What's the cost of a computer system validation that can't survive an FDA inspection?

FDA mandates computer system validation with complete audit trails, electronic records integrity, and documented evidence. Without automated validation, every system change demands weeks of manual IQOQ/PQ protocol execution.

$300M+

in FDA consent decrees annually tied to data-integrity and computer-system-validation failures

Pharmacovigilance & Adverse Event Data Quality for Life Sciences

Pharmacovigilance & Adverse Event Data Quality

What's the cost of an incomplete or late adverse event report reaching regulators?

Adverse event reporting is a regulatory obligation with zero tolerance for delays. When FAERS or EudraVigilance submission data has completeness or timeliness gaps, the consequences are Warning Letters, import bans, and EU GVP fines.

€20M

maximum EU GVP penalty per violation; US FDA can issue Warning Letters and import bans for PV failures

Real-World Data Quality for RWE Studies For Life Sciences

Real-World Data Quality for RWE Studies

What's the cost of an RWE submission the FDA can't trust?

Real-World Evidence is increasingly required for label expansions, regulatory submissions, and HTA dossiers. But poor source data quality and inconsistent coding invalidate RWE submissions before they reach FDA reviewers.

67%

of RWE studies delayed or rejected due to inadequate source data quality documentation

Patient & Subject Data in Non-Production Environments For Life Sciences

Patient & Subject Data in Non-Production Environments

What's the cost of trial subject data appearing in a test environment?

GCP, GDPR, HIPAA, and 21 CFR Part 11 all mandate strict separation of subject-level data from non-production environments. Yet trial subject PII routinely appears in QA, UAT, and development - a compliance exposure most teams discover only during an audit.

72%

of biopharma companies have trial subject data in at least one non-production environment

The Platform

How DataOps Suite Solves It

Each product maps directly to a life sciences data validation problem — deployed independently or as a unified, GxP-ready suite.

ETL Validator for Industry icon

ETL Validator

Solves: Clinical & commercial system migration

Record-level, non-sampled reconciliation across EDC, CTMS, LIMS, and legacy system migrations. AI-generated test cases produce IQ/OQ/PQ-ready validation evidence, cutting manual protocol execution time significantly

21 CFR Part 11
EDC / CTMS Migration
LIMS Validation
BI Validator for Industry Icon

BI Validator

Solves: Regulatory & clinical analytics dashboards

Cell-level regression across Power BI, Tableau, and other BI Platforms. Cross-layer traceability links every KPI to its source CRF or lab record — the audit chain FDA inspectors expect during data-integrity reviews.

DQMF Compliance
Clinical Dashboards
Audit Traceability
Data Quality Monitor for Industry Icon

Data Quality Monitor

Solves: Pharmacovigilance & RWD quality

Continuous six-dimension DQ monitoring on adverse event, clinical, and real-world data feeds. Catches completeness gaps, coding inconsistencies, and timeliness failures before they reach FAERS, EudraVigilance, or FDA regulatory submissions.

Continuous Monitoring
FAERS / EudraVigilance
RWD Quality
Test Data Manager for Industry icon

Test Data Manager

Solves: Trial subject data in non-production

GDPR- and HIPAA-aligned deterministic masking generates realistic synthetic trial subject and patient data without exposing real PII — letting AI/ML and analytics teams work with statistically representative clinical data safely.

AI / ML Ready
GCP / GDPR Masking
Synthetic Trial Data
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.

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Proven Outcomes

Impact Across Life Sciences Teams

60%

Faster Validation Cycles

AI-generated test cases cut IQOQ/PQ protocol execution time by over half

100%

Record-Level Coverage

Every clinical record not just sample checked, not a statistical subset

Zero

Real PII in Test Labs

GCP-aligned masking removes the need for trial subject data in non-production environments

FDA-Ready

Validation Evidence

IQ/OQ/PQ-compatible audit trails generated automatically for every validation run

Use Cases

Life Sciences Use Cases by Product

Clinical Data Migration (EDC / CTMS / LIMS → Cloud)

100% source-to-target reconciliation for migrations to Veeva Vault, Medidata Rave, or Oracle Health Sciences. AI-generated test cases produce IQ/OQ/PQ-ready evidence, cutting manual CSV protocol effort by over half.

Veeva / Salesforce CRM Migration Validation

Source-to-target reconciliation for Veeva-to-Vault-CRM and Salesforce Life Sciences Cloud migrations. Validates data flows from CRM through lake house to downstream HCP-facing applications ensuring the platform split doesn't break operations or rebate calculations.

Commercial & Clinical Dashboard Regression

Visual regression and pixel-to-pixel comparison across territory sales dashboards, clinical KPI reports, and QBR presentations on Power BI, Tableau, and other BI Platforms

Regulatory Report & Audit Traceability

Cross-layer traceability links every KPI to its source CRF, lab record, or commercial data mart the audit chain FDA inspectors expect during data integrity reviews.

Pharmacovigilance & FAERS Quality Monitoring

Continuous six-dimension quality monitoring on adverse event and safety data feeds. Catches completeness gaps, coding inconsistencies, and timeliness failures before they reach FAERS or EudraVigilance.

Rebates Process & Commercial Data Validation

Validates distributor sales data at ingestion, reconciles eligibility rules and unit calculations, and flags anomalies duplicate claims, missing NDC codes, volume spikes before rebate payouts are calculated.

GCP/GDPR-Safe Synthetic Trial Data

Deterministic, reversible masking generates realistic synthetic trial subject and patient data without exposing real PII letting AI/ML and analytics teams work with statistically representative clinical data safely.

AI/ML Training Data for Drug Discovery

Privacy-safe synthetic datasets for predictive modeling, compound screening, and clinical decision support without copying trial subject data into development environments.

GxP-Ready Unified Compliance Platform

Single audit trail covering 21 CFR Part 11, GAMP 5, GDPR, and GxP requirements. IQ/OQ/PQ-compatible evidence generated automatically for every validation run.

AI-Generated Test Cases for Life Sciences Pipelines

Auto-generate validation rules from EDC metadata schemas, mapping documents, and transformation specs. Self-healing tests survive schema changes across system upgrades and study amendments.

Proof Points

Customer Evidence in Life Sciences

Pharma Consulting Analytics

Scaling Power BI Testing Automation

Automated KPI card-to-trend-chart validation across 30+ production pipelines for multiple pharma clients. 70% time savings vs. manual validation, 80% reduction in turnaround time, with reusable template projects eliminating rebuild redundancy.

Read Case Study
Leading U.S. Pharmaceutical Company

Automated Tableau Testing for Reliable Reports

Automated regression, performance, and security testing across six Tableau production environments serving 11,000 users. 45-55% faster upgrade testing, 25% reduction in functional testing time, and 20% overall TCO reduction."

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Experience Datagaps Live - Trust your Data With Confidence

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

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SOC 2 Type II certified | ISO 27001 certified | No credit card required for trial | Your data never leaves your environment

FAQs: Life Science Services

 Common questions from Enterprise Buyers

How does Datagaps support GxP and 21 CFR Part 11 validated data requirements?

DataOps Suite runs inside a validated, audit-documented environment aligned to GxP and 21 CFR Part 11, with every validation run logged for inspection. See the full compliance approach on our Compliance Solutions page, and how it applies to life sciences specifically on our Life Sciences Data Testing page.

Can Datagaps reconcile clinical trial data across systems at real research scale?

Yes — Data Reconciliation and ETL Validator validate record-, field-, and aggregate-level matches across EDC, LIMS, and CTMS systems without sampling, so discrepancies surface before they reach a regulatory submission. Our App Integration Data Testing guide covers the full cross-system validation approach.

Does Datagaps generate de-identified data that preserves statistical validity for research use?

Yes. Test Data Manager de-identifies patient and trial data while preserving referential integrity and statistical distribution, so research and model-training datasets stay usable without exposing PHI.

How does Datagaps help in AI powered drug discovery or clinical decision support?

Reliable AI starts with AI-ready data — Data Quality Monitor continuously profiles and scores the datasets feeding drug-discovery and clinical-decision models, catching drift before it degrades model output. Our Data Quality for AI Readiness guide covers the full framework behind that scoring.

Can Datagaps help with the operational burden of profiling large pharma datasets?

Yes — Data Quality Monitor automates column-, pattern-, and dependency-level profiling across large pharma datasets instead of leaving it to manual spot-checks. See the methodology in our blog, Data Profiling in ETL: Types and 5 Best Practices.

Is Datagaps deployable in a fully validated, on-prem, or air-gapped environment?

Yes — DataOps Suite deploys entirely on-prem or in an air-gapped environment with no data leaving your perimeter, and every run is logged for validation-state audits. See the deployment and controls detail on our Compliance Solutions page, or request a demo to review your specific environment.

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

Whitepapers

Accelerating Databricks
Lakehouse

Accelerating Databricks Lakehouse: Automated Migration Validation and Trusted Analytics

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How Agentic AI Is Transforming BI Validation

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 – Accelerated by AI Agents.

Data Quality Monitor software

DQ Monitor

Proactive Data Quality with Agentic AI – 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

Data Migration Testing
Data Observability
Data Reconciliation
Data Quality Testing
Compliance
APCD Solutions

Cloud

Snowflake
Databricks
Azure Synapse
Salesforce
APCD Solutions

Analytics

Power BI
Tableau
Oracle Analytics

Industry

Healthcare
BFSI
Life Sciences
Retail
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

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