Data Observability Agent | Monitor, Detect & Automate Data Reliability

Releasing: Decision Intelligence for Enterprises #2025

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Intelligent Observability Features

From continuous monitoring to automated remediation, the Data Observability Agent ensures your pipelines stay healthy, reliable, and optimized — without manual oversight

Intelligent Anomaly Detection

Identify drifts, schema shifts, and latency issues in real time using adaptive, AI-driven monitoring

By leveraging advanced machine learning models, it distinguishes between expected fluctuations and true anomalies, reducing false positives and ensuring precise detection

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Context-Aware Root Cause Analysis

Correlate performance metrics, quality signals, and lineage data to pinpoint and resolve issues faster

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Proactive Alerting and Auto-Remediation

Receive contextual alerts and enable automated corrective workflows — minimizing downtime and human intervention

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How It Works - Agentic Observability in Action

The Data Observability Agent provides end-to-end visibility — transforming fragmented operational data into continuous intelligence

Financial Data Integrity & Compliance Assurance

Ensure accuracy in transaction data, risk models, and compliance reporting. The Data Observability Agent detects anomalies in payment pipelines, validates data integrity across systems, and prevents reporting delays — helping financial institutions maintain trust and regulatory compliance

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Proactive Insights

Detect and fix data issues early to keep analytics accurate and decisions uninterrupted

Reduced Downtime

Identify root causes quickly and automate recovery to restore pipelines up to 4x faster

Trusted Pipelines

Maintain full data lineage and transparency to ensure reliable, governed, end-to-end data operations

Operational Efficiency

Automate monitoring and remediation to cut manual effort and boost overall team productivity

Key Use Cases for Intelligent Data Observability

Empower teams to maintain always-on, high-quality data operations through proactive detection, contextual insights, and autonomous resolution

01

Pipeline Reliability Monitoring

Ensure every data pipeline runs smoothly and on time

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Root Cause Diagnosis

Accelerate troubleshooting through automated intelligence

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Data Drift Detection

Stay ahead of unseen shifts in data behavior

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Pipeline Reliability Monitoring

Ensure every data pipeline runs smoothly and on time

Detection: Detect slowdowns, data drifts, or dropped records instantly
Prevention: Prevent SLA breaches with predictive, context-aware alerts

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02

Root Cause Diagnosis

Accelerate troubleshooting through automated intelligence

Correlation: Correlate performance, quality, and lineage metrics seamlessly
Diagnosis: Pinpoint the exact source of pipeline or system failures

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03

Data Drift Detection

Stay ahead of unseen shifts in data behavior

Monitoring: Continuously monitor schema, volume, and pattern changes
Anomaly: Detect anomalies that affect ML models or dashboards

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