New 2025 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions - Download Report

Platform
- DQLabs Platform
One unified platform for data leaders, engineers, and analysts—offering observability, quality, discovery, and remediation.
- Agentic AI Data Management
An autonomous, Agentic AI driven capabilities to continuously improve management of data issues.
- DQLabs Capabilities
Automated data quality, anomaly detection, semantics-driven discovery, and issue resolution for trusted and reliable data.
- Data Observability
Monitor data, pipelines, and usage—detect anomalies, ensure reliability, and optimize the health of your data systems.
- Data Quality
Automate data quality with no-code checks, anomaly detection, lineage, and governance for trusted, accurate, and reliable data.
- Data Discovery
Discover data with semantics-driven categorization, advanced search, and catalog integration for faster, actionable insights.
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- 10 Ways to Optimize Your Data Observability ROI
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- About Us
DQLabs is an automated, modern data quality platform that delivers reliable and accurate data for better business outcomes.
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Data Quality Monitoring and Incident Management with BigPanda

Overview

BigPanda is an event correlation and automation platform designed to streamline IT operations and incident management. By correlating and consolidating alerts from various sources, BigPanda helps teams quickly identify and resolve issues, reduce alert noise, and improve response times.

Integrating BigPanda with DQLabs enhances these capabilities by adding real-time data quality monitoring and observability. This integration ensures that the data driving BigPanda’s alerts is accurate, reliable, and actionable—enabling faster, more informed decision-making and reducing the risk of false positives or overlooked anomalies.

The integration of DQLabs with BigPanda enhances IT operations by correlating data quality issues with system incidents, enabling faster root cause analysis and resolution. DQLabs continuously monitors data pipelines, detecting missing records, schema changes, and anomalies that could disrupt analytics and business operations. BigPanda’s AI-driven event correlation then maps these data quality alerts to relevant infrastructure and application incidents, reducing noise and preventing false alarms.

By linking operational failures with data health insights, IT teams can quickly identify whether an issue stems from system failures or bad data, avoiding prolonged troubleshooting. This proactive approach ensures reliable data for analytics, dashboards, and AI models, improving decision-making while minimizing downtime and operational risks.

Effective Data Quality Management with BigPanda

Proactive Issue Detection and Prevention

Automatically monitor data quality before alerts are sent to BigPanda, preventing data issues from escalating into operational incidents.

Data Quality-Driven Incident Resolution

Correlate data quality metrics with operational events to provide context for faster incident resolution and informed decision-making.

Ensure Accurate Alerts and Reporting

By embedding data quality validation directly into the BigPanda workflow, ensure that only relevant and accurate alerts are triggered, reducing noise and improving response times.

Optimize IT Operations with Clean Data

Improve the effectiveness of automated IT workflows by ensuring that data is complete, accurate, and free from inconsistencies before it impacts incident management.

Minimize Downtime and Business Disruptions

Detect and resolve data quality issues that could impact system performance, AI models, and analytics dashboards, ensuring continuous and reliable operations.

Unified Observability Across IT & Data Ecosystems

Enable a holistic view of both operational incidents and data health by integrating DQLabs’ data observability metrics with BigPanda’s infrastructure monitoring, providing end-to-end visibility.

Seamlessly Integrate with your
Modern Data Stack

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