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Bigeye

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.

bigeye.comSan Francisco, California, United StatesFounded 2018

Last updated May 11, 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
Connections

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