Data quality you can measure, monitor, and prove
The Saudi data economy runs on trusted data. Goava profiles every connected source automatically, applies measurable quality tests and SLAs, and turns failures into managed incidents — so your teams act on data they can defend.
Automated Profiling
Understand your data before you govern it
Goava's profiler scans your connected sources and builds a statistical picture of every table: how many rows it holds, how complete each column is, and how fresh the data is. Profiling runs automatically, so the picture stays current as your data changes — and every profile is visible right inside the catalog entry, next to ownership, classifications, and lineage.
- Table and column statistics: row counts, distributions, and value summaries
- Null and completeness rates for every column, tracked over time
- Freshness monitoring that shows when each dataset was last updated
Tests, SLAs & Incidents
From quality checks to accountable resolution
Data quality is not a one-time check — it is an operating discipline. Apply built-in or custom tests to your critical datasets, set SLAs that define what acceptable quality means, and let observability alerts notify the right owner the moment a test fails. Every failure becomes a managed incident with an assignee, a status, and a documented resolution.
- Built-in and custom quality tests applied per table or column
- SLAs that turn quality expectations into measurable commitments
- Incident management: assign, track, and resolve failures with full history
- Observability alerts that reach data owners before consumers are affected
Everything you need for reliable data
Six capabilities that work together to keep your data accurate, fresh, and accountable.
Automated Profiler
Statistics, null rates, and freshness computed automatically for connected tables, so you always know the state of your data.
Built-in Quality Tests
A library of ready-made checks covering nulls, value ranges, row counts, and freshness — applied in a few clicks.
Custom Tests
Define your own test logic for business-specific rules when the built-in library is not enough.
Quality SLAs
Set measurable expectations for data freshness and reliability, and track whether each dataset meets them.
Incident Management
When a test fails, open an incident, assign an owner, and track resolution — with full history that supports audits.
Observability Alerts
Notify data owners and stewards the moment quality degrades, before downstream dashboards and reports are affected.
Frequently asked questions
What does the data profiler measure?
The profiler automatically computes table and column statistics: row counts, value distributions, null and completeness rates, and data freshness. It runs on your connected sources, and the results appear directly on each asset's page in the data catalog — alongside ownership, classifications, and lineage.
What is the difference between quality tests and SLAs?
A test is a single assertion about your data — for example a check on nulls, value ranges, or freshness. An SLA defines the expected level of quality over time. When a test breaches its SLA, Goava opens an incident and alerts the dataset's owners automatically, so expectations become commitments with clear accountability.
How does data quality relate to the NDMO framework?
Data quality is a core data management discipline. Goava supports quality work with measurable tests, SLAs, and documented incident workflows, helping Data Management Offices maintain evidence that can be reviewed during assessments. See the governance mapping page for the broader picture.
Which data sources can we monitor?
Goava ships with around 80 native connectors, including Snowflake, BigQuery, Databricks, Redshift, Oracle, SQL Server, PostgreSQL, MongoDB, and Kafka. The platform runs self-hosted on-premises or in the cloud inside Saudi Arabia, with a fully bilingual Arabic and English interface.
Make data quality measurable — and provable
See how profiling, tests, SLAs, and incident management work together on your own data.