Anomalo - Data Quality Monitoring Platform
Open siteResearch & Data Analysis
Introduction
Automated AI to detect and resolve data quality issues.
Anomalo - Data Quality Monitoring Platform Product Information
Anomalo is an all-in-one automated data quality monitoring platform for enterprises that uses AI to detect anomalies, validate data, govern data integrity, and provide observability across the data stack. It enables rapid identification, root-cause analysis, and resolution of data quality issues to mitigate impact on operations, analytics, and AI models. The platform emphasizes ease of integration, scalability, and no-code/low-code rule definitions with optional SQL/API customization.
How Anomalo Works
- Connect your data sources. Integrate with your data lake/warehouse and connect to data orchestrators and ETL tools in one click.
- Enable AI data quality monitoring. Activate autonomous monitoring that uses unsupervised learning to understand historical patterns, delivery times, and seasonal changes across datasets.
- Add validation rules and KPIs. Use a no-code UI to define custom validation checks or write SQL rules and access API integrations for more control.
- Detect, alert, and resolve. Receive notifications, perform automated root-cause analysis, and view data lineage to understand impact and resolve issues quickly.
Core Features
- Anomaly Detection: Identify abnormal patterns and deviations in data
- Data Validation: Ensure accuracy, completeness, and consistency of data
- Data Governance: Maintain data integrity, compliance, and security
- Data Observability: Low-cost, scalable monitoring for the entire data warehouse
- Automated Data Lineage Tools: Visualize data flows from source to destination
- Data Profiling & Analysis: Metadata-based profiling and rich visualizations for data quality
- Integrations & Ecosystem: Connects with various data stacks and tooling
- No/Low-Code Rules: Build validation checks via UI or SQL/API for advanced control
- Scalable for tens of thousands of tables: Designed to monitor large-scale data environments
Why It Matters
- Proactively detect data quality issues before they impact operations, analytics, or AI models.
- Build trust in data with transparent lineage, profiling, and governance capabilities.
- Reduce manual effort with AI-driven anomaly detection and automated root-cause analysis.
Use Cases
- Enterprise data quality monitoring at scale
- Data pipeline health checks and delivery-time monitoring
- Validation of critical business tables and metrics
- Data governance and compliance verification
Getting Started
- Request a Demo to see how Anomalo fits your data stack.
- Connect data sources and enable AI monitoring with a few clicks.
- Define essential validation rules and KPIs to begin proactive monitoring.
Safety and Compliance Considerations
- Data quality monitoring helps ensure reliable analytics and trustworthy AI outputs. Configure governance controls to meet organizational and regulatory requirements.