The Data Integrity Imperative
How Automated Data Validation is Revolutionizing Financial Services and Mitigating Multi-Million Dollar Risks.
The Staggering Cost of Inaction
According to Gartner, poor data quality is not just a technical issue; it's a massive financial drain on organizations.
$12.9 Million
Average Annual Financial Impact of Poor Data Quality
The Escalating Cost of a Single Error: The 1-10-100 Rule
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$1
To Prevent an Error
Proactive validation and verification at the point of entry is the most cost-effective stage.
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$10
To Correct an Error
Once bad data enters the system, it requires manual effort, resources, and time to cleanse and remediate.
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$100
To Live with a Failure
If an error goes uncorrected, it leads to flawed reports, poor decisions, compliance fines, and lost customers.
Pervasive Data Challenges Across Financial Services
From investment banking to consumer finance, every sector faces a unique but critical set of data validation hurdles that impact everything from regulatory compliance to profitability.

Introducing QuerySurge: The AI-Powered Solution
QuerySurge is the smart data testing solution engineered to automate validation across your entire data landscape, from mainframes to cloud data lakes, ensuring data quality at speed and scale.
From Hours to Minutes: The Automated Validation Workflow
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AI Test Creation
Generative AI automatically creates validation tests from data mappings.
⚙️
Automated Execution
Tests run automatically on a schedule or triggered by ETL jobs in your CI/CD pipeline.
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100% Data Comparison
Compares up to 100% of source and target data, pinpointing every discrepancy.
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Actionable Reporting
Generates detailed reports and analytics, delivered automatically to stakeholders.
The Transformative Impact of Automation
The Tangible Return on Investment
45%
Reduction in Data Processing Errors
50%
Faster Loan Processing Cycles
1000x
Faster Than Manual Comparison
✓
Enhanced Regulatory Compliance
Trust Your Data. Drive Your Business.
With QuerySurge, financial institutions can finally shift from reactive data fire-fighting to proactive data quality assurance, building a foundation of trust that powers innovation, compliance, and growth.