Anomalo

Automatically traces upstream and downstream data lineage to help identify the source and impact of data quality issues across the data stack.

Unsupervised machine learning-powered platform that automatically detects data quality issues, missing data, and anomalies across tables without manual configuration.

Provides instant root cause analysis for detected data quality issues with samples, visualizations, and lineage information to help teams investigate and resolve problems quickly.

Cost-effective solution designed to monitor millions of tables with bulk configuration, efficient hourly queries, and integrations with orchestration and catalog tools.

Sophisticated alert routing system with false positive suppression that delivers the right alerts to the right person, minimizing notification noise.

User-friendly interface allowing non-technical users to create custom validation rules and key metric checks without requiring API or code knowledge.

Conversational AI agent that allows users to ask questions, perform data analyses, and compose data-driven reports through natural language on any data source, with organizational memory that improves with corrections.

Intelligent alert routing and false positive suppression that sends the right alerts to the right people with minimal noise.

Intelligent alert system with false positive suppression, rich alert experience, and sophisticated routing to ensure notifications reach the right team members without noise.

Comprehensive autonomous data platform with nine specialized AI agents that autonomously monitor, investigate, surface, and report on data quality and insights.

A conversational analytics agent that enables natural language queries, data analysis, and report composition on enterprise data. Learns from user interactions to continuously improve system knowledge.

Automatically identifies missing and anomalous data registered in Anomalo, helping detect data quality issues before they impact downstream processes.

Always-on monitoring of data availability, freshness, and schema consistency across data pipelines using agentic AI to ensure data moves as expected without manual intervention.

Continuously monitors business metrics defined in natural language and alerts on unexpected changes and anomalies without requiring manual dashboard checks.

Integrations with data catalogs to provide unified data governance and quality monitoring across data discovery and metadata management platforms.

Enables natural language interaction with data for analysis and reporting, leveraging deep understanding of data nature, usage patterns, and query methods.

Intelligent Data Analyst that enables natural language queries for data analysis, visualization, and report composition without coding.

Builds continuously-updated dashboards and reports via natural language with built-in time-series predictive modeling for anomaly detection.

Automatically creates comprehensive and up-to-date documentation by integrating catalog metadata, existing documentation, and chat conversations.

Proactively identifies noteworthy changes in data and delivers analyst-grade reports without requiring manual prompts or analyst intervention.

Automatically investigates and responds to data quality alerts, assesses impact and criticality, and follows established runbooks to escalate or resolve issues.

Automated upstream and downstream data lineage tracking to understand data dependencies and impact across the data stack.

Automatically monitors data quality deviations defined through natural language to prevent issues from breaking dashboards, reports, and AI models.

Unsupervised machine learning-powered data quality checks that automatically identify data issues without requiring manual rule configuration.

Automated data quality monitoring platform using unsupervised machine learning to detect missing, anomalous, and problematic data across tables at scale.

No-code interface for creating custom data validation rules and key metric checks, enabling non-technical users to define their own quality standards.

Cost-effective monitoring capability for millions of tables with bulk configuration, efficient hourly queries, and orchestration integrations for enterprise-wide data quality management.

Automatically evaluates experiments and A/B tests by applying correct statistical techniques and analyzing results across customer segments.

Integration capabilities with data orchestration platforms to enable seamless monitoring within existing data pipeline workflows.

Automatic root cause analysis that identifies the likely source of data quality problems, providing samples and visualizations to accelerate investigation and resolution.

Autonomous monitoring of data availability, freshness, and schema consistency to ensure data pipelines are functioning as expected.

Integrates with ticketing systems to enable automated triage and resolution workflows for data quality issues.

Built-in workflows for triaging and resolving data quality issues, including ticketing integrations for seamless issue management.

Powerful data quality checks powered by unsupervised machine learning that automatically identify anomalies and data quality issues without manual rule configuration.