Request a demo today and see how Anomalo can help you streamline your data quality monitoring
Anomalo is the automated data quality monitoring platform, allowing customers to automatically detect data issues and understand their root causes, before anyone else.
Anomalo’s unsupervised machine learning automatically monitors data quality, allowing you to identify issues before they impact your business.
Anomalo offers a variety of visualizations that help you understand data quality problems in context, making it easier to identify the root cause.
Anyone can be a data quality champion and create their own validation rules or key metric checks using Anomalo’s no-code UI.
Anomalo’s incredibly powerful unsupervised machine learning uses automatic secondary checks to weed out false positives.
When data quality issues are detected, Anomalo provides an instant root-cause analysis that points to the likely source of the problem, saving you time and effort to investigate.
“Discover has been using Anomalo in production for nearly 2 years with flourishing adoption and is continuing to integrate the platform across our entire organization. We are confident that Anomalo will enhance our ability to monitor data quality at scale and with less manual effort.”
“Anomalo has made a ton of difference around what we’ve been able to observe and keep track of. There’s the day-to-day, ‘How is everything looking?’ And there are also indicators about how our business is trending. You can do both—it’s not an either/or proposition.”
“When we’re bringing a bunch of different datasets together, whether it be external or internal, that’s where we sit Anomalo strategically to make sure those key datasets are fresh, accurate, and as expected. We literally went from nothing to having something that was automated and kicking out alerts to us.”
“We were delighted with the functionality Anomalo provided, and their approach to monitoring matched our essential requirements.”
“Anomalo has transformed our data incident response pipeline so we’re no longer searching for a needle in a haystack.”