Digna is an AI-powered data quality platform that utilizes machine learning algorithms to detect anomalies in real-time, ensuring the integrity of data across various databases and data warehouses. By continuously monitoring data without the need for predefined rules, it addresses issues such as missing or incorrect data, enabling organizations to maintain high-quality data effortlessly.
Funding
Funding not disclosed
Founders
Product
Problem
Maintaining data integrity across diverse databases and data warehouses is challenging, especially with increasing data volume and sources. Traditional data quality checks relying on predefined rules struggle to detect anomalies in real-time, leading to issues like missing, incorrect, or inconsistent data. This can result in flawed insights and compromised decision-making.
Solution
Digna is an AI-powered data quality platform that leverages unsupervised machine learning to automatically detect data anomalies in real-time, without requiring predefined rules. The platform profiles data over time, capturing key metrics and using these to train forecasting models that predict future values. By continuously monitoring data and self-adjusting anomaly detection thresholds, Digna provides early warnings for deviations, ensuring data integrity across various databases and data warehouses. The platform calculates metrics using SQL within the database, eliminating the need for data export and ensuring data privacy.
Target Audience
Digna targets data warehouse product managers, data engineers, and data analysts who need to ensure data quality and reliability in their data pipelines. It also serves organizations in both the private and public sectors dealing with large volumes of data from diverse sources.
Features
- Automated machine learning for anomaly detection without manual setup
- Real-time data monitoring and anomaly detection
- Self-adjusting thresholds for early warnings of data deviations
- SQL-based metric calculation within the database, ensuring data privacy
- Intuitive dashboards for real-time data health monitoring
- Instant notifications for detected anomalies
- Command Line Interface (CLI) and RESP API support for integration into ETL processes
- On-premise or cloud installation options