Datagusto is a data discovery platform that automates the identification of inconsistencies and silent failures across complex data ecosystems, ensuring data accuracy and reliability for AI applications. By providing real-time alerts and a unified view of data systems, it minimizes operational disruptions and reduces costs associated with data quality issues.
Funding
$2.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
As data ecosystems become more complex, maintaining consistent and synchronized data is essential for effective AI systems. Existing solutions often struggle to manage the rising risks and costs of inconsistencies and disruptions, including silent failures that can compromise outcomes.
Solution
Datagusto is a data discovery platform that automates the identification of inconsistencies, synchronization issues, and silent failures across complex data ecosystems. By mapping data flow and analyzing pipeline code, Datagusto provides a unified view of data systems, ensuring data accuracy and reliability for AI applications. The platform proactively uncovers integration gaps and hidden issues, preventing disruptions before they occur and maintaining AI model accuracy. Datagusto streamlines integration, detects changes, and predicts the impact of inconsistencies, enabling users to address problems before they lead to costly errors.
Target Audience
Datagusto targets data and cloud heads of engineering, chief data officers, ML engineers, and data architects who need to ensure data quality, seamless data integration, and data pipeline reliability across complex data ecosystems.
Features
- Automated data system cataloging and documentation updates
- Real-time alerts for data inconsistencies and silent failures
- Data lineage mapping to visualize data connections and dependencies
- AI-powered analysis of raw values and SQL queries to create a detailed data catalog
- Proactive data quality management to prevent disruptions and maintain AI model accuracy
- Streamlined integration processes to reduce unnecessary costs
- Disruption risk management to pinpoint and resolve issues without impacting operations