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Datagaps at AI Enterprise Conference 2026

Testing Time
Saved by

55%

Improved Functional
Testing Time by

25%

Saved QA
Cost by

30%

Overall Improvement
of TCO by

20%

📅 Date: September 1, 2026.

🏛️ Event Venue: Pier Sixty NYC | Chelsea, Manhattan.

📍 Location: New York City.

Helping data management teams build trusted, governed, and AI-ready data pipelines.

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About the Event

The AI Enterprise Conference, hosted by Data Science Connect, brings together Fortune 500 data and AI decision-makers under the theme AI for Enterprise: From Pilots to Production. The event focuses on running AI at true enterprise scale — covering AI systems, governance, data foundations, and AI economics — with speakers from Citi, Morgan Stanley, BlackRock, Vanguard, and Verizon.

Why Datagaps at AI Enterprise Conference?

We're here because trusted data is the foundation of production-ready AI

This year’s theme — AI for Enterprise: From Pilots to Production — is exactly the problem Datagaps solves. Every one of the conference’s content pillars (AI as a System, Governance That Runs, The Economics of AI, The Enterprise Foundation) depends on one thing underneath it: data you can trust. That’s what Datagaps is built for.

Agentic AI Governance

Validate the data powering your AI — before it causes decisions you can't trust.

AI-Ready Data Foundations

Move from fragmented, ungoverned data to a validated foundation that agentic systems can rely on.

Governance That Runs

Turn AI governance from policy decks into enforced, auditable, runtime controls.

ETL & Pipeline Validation

Catch data issues in your pipelines before they reach production — and before they reach your models.

Data Quality Monitoring

Detect anomalies and drift proactively across the data feeding your AI systems.

AI Economics & ROI

Reduce rework and wasted spend by validating data quality before it undermines AI initiatives.

Darius Tantanella
Darius Tantanella

Sr. Account Executive

Meet Darius at AI Enterprise Conference 2026

Meet the Datagaps team at the AI Enterprise Conference on September 1st in NYC. Come talk data quality, agentic AI governance, and what it really takes to trust the data behind enterprise AI.

📍 Location: New York City.

📅 Dates: September 1, 2026.

🏛️ Venue: Pier Sixty NYC | Chelsea, Manhattan.

Schedule time with Datagaps to learn how proactive data validation helps you move from AI pilots to production — without compromising trust.

Let’s Talk Data Trust

Stop by and meet the Datagaps team at the AI Enterprise Conference or book a dedicated session to see how we help enterprise data and AI teams eliminate errors, automate validation, and build the governed data foundation that production-grade AI requires.
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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
Financial Services
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

Academy

Blog
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Whitepapers
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Pricing

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ETL Validator + DQ Monitor Pricing
BI Validator Pricing
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ETL Validator Free Trial
BI Validator Free Trial
DQ Montior Free Trial
Test Data Manager Free Trial
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