Anomalo helps us to, with very low effort, increase the confidence in the data products provided by our data teams. Within a minute Anomalo can be set up to run its checks on a new table. The out-of-the-box checks provided by Anomalo, focused on freshness, completeness and table anomalies, improve the observability of our data. Running Anomalo independent of our data pipelines, provides an additional layer of checks that helps us to increase the trust in our datasets. Additional checks on key metrics and validation rules can be configured through the UI, with no coding required. This enables business users themselves to set up checks according to their needs, without the data team becoming a blocker. Additionally, having an external tool for anomaly detection helps us to apply industry best practices as Anomalo emphasizes nudging users to leverage the tool according to these best practices.

We were able to detect data issues quickly using Anomalo and get the alerts quickly so that we could fix them quickly and move on. We are using this tool to detect infra issues in data pipelines, data quality issues and even monitoring business KPIs - this is how much we trust this tool!

I like how consistent Anomalo is at being able to monitor and alert us about any discrepancies that stand out. You're also able to adjust the threshold making it more or less noisy depending on how important the check is. Anomalo has definitely helped my team be more proactive in catching issues.