Automated Quality Control & Issue Resolution
Transform manual error-hunting into automated detection. Loome's central rule repository and continuous validation pipeline catch issues early, while automated alerts and stewardship workflows streamline your resolution process.
Everything your organisation needs for trusted data.
Unified Discovery
Navigate data pipelines and quality rules across multiple platforms through a single intuitive interface.
Centralised Rules
Manage and execute data quality rules across your entire data estate.
Automated Validation
Run continuous data quality checks within your pipelines.
Stewardship Workflow
Assign and track data quality issues to resolution.
Alert Management
Configure and deliver targeted alerts to operational staff.
Time Series Analytics
Store and analyse quality and performance metrics of your data warehouse or lakehouse.
Why organisations choose Loome for Data Ops
Accelerate Issue Resolution
- Automated detection and alerts
- Streamlined stewardship workflow
- Real-time operational feedback
Strengthen Data Trust
- Centralised quality rule management
- Automated reconciliation checks
- Complete audit trail of resolutions
Drive Accountability
- Role-based ownership
- Integrated feedback collection
- Comprehensive issue tracking
Start your free trial and see how Loome can transform your data quality operations
Built for teams who care about data integrity
Data Mapping
We provide a lightweight and cost competitive way to capture your ETL design and data model decisions so you manage your data engineering team decisions and can easily publish this in situ within your reports.
Manage reference data online
We provide a simple way to manage manual reference data for your analytics solution so you can stop worrying about having to build integration processes for all of your disparate spreadsheets. A cloud native alternative to SQL Server Master Data Services at a fraction of the cost.
Proactively monitor data quality issues
We provide a unique workbench that allows analysts and data stewards to participate in your data quality workflow rather than it falling on data engineering teams. Assign responsibility and track actions to resolution.
