Bigeye
Bigeye provides automated data quality monitoring and anomaly detection to help data teams proactively identify and resolve data issues before they impact business decisions, ensuring data reliability and trust across the organization.
Last updated Feb 8, 2026
- Industry
- Data Infrastructure/Data Observability
- Business Model
- SaaS
- Target Market
- Mid-Market to Enterprise
- Employee Count
- 51-200
- Funding
- $65M
- API Available
- Yes
Market Position
Emerging player in the data observability market focused on automated data quality monitoring
Overview
Bigeye is a data observability platform founded to help data teams monitor and maintain the quality of their data pipelines. The company provides automated data quality monitoring, anomaly detection, and root cause analysis capabilities to ensure data reliability across modern data stacks. While Bigeye operates in the broader data infrastructure space rather than traditional advertising technology (AdTech), it serves companies across various industries including those in digital advertising and marketing technology who rely on accurate data for campaign optimization, audience targeting, and performance measurement. Bigeye's platform addresses the critical challenge of data quality and trust in organizations that have adopted cloud data warehouses and modern data architectures.
Products & Features
Autometrics
Automated metric generation and monitoring that learns data patterns without manual configuration
Anomaly Detection
Machine learning-based detection of data quality issues and anomalies in data pipelines
Root Cause Analysis
Tools to quickly identify the source and impact of data quality issues
Data Lineage
Visualization of data dependencies and impact analysis across the data stack
Key Features
Automated data quality monitoringML-based anomaly detectionRoot cause analysisData lineage trackingCustom metrics and rulesSlack and email alertingSQL-based monitoringDashboard and visualization tools
Use Cases
Data pipeline monitoringData warehouse quality assuranceAnalytics reliabilityML model data validationBusiness intelligence data qualityRegulatory compliance data monitoring
Customer Segments
Data teamsAnalytics teamsData engineersData scientistsBusiness intelligence teams