Trust Your Data
Enterprise Data Quality for All Data Types
Say goodbye to manual data quality rules. Anomalo is the AI-powered platform that ensures data quality across structured, semi-structured, and unstructured data. Proactively detect, root cause, and resolve data issues before they impact your operations, analytics, or AI initiatives – all at scale and with no code.
Trusted by the world’s most data-driven enterprises
The Only Data Quality Solution Backed by Databricks and Snowflake
Snowflake and Databricks—leaders in data and AI—know that AI is only as powerful as the data behind it. That’s why they’ve both chosen to back Anomalo. Our automated approach to data quality helps enterprises build trustworthy data foundations, catch issues before they escalate, and unlock reliable AI outcomes.
With deep, native integrations across the modern data stack, Anomalo ensures your data is accurate, complete, and ready—wherever it lives—so your business can move faster, smarter, and with confidence
Data Quality Software
Anomalo is the easiest way to continuously monitor all your enterprise data, without writing a single line of code.
1.
Connect your Data
Easily integrate with cloud data lakes, warehouses, orchestrators, and ETL tools.
2.
Enable AI-Powered Monitoring
Unsupervised machine learning detects anomalies across all your data (structured, semi-structured, and unstructured) without manual configuration.
3.
Customize with Rules and KPIs
Use our no-code interface to define business logic and key metrics, or programmatically via API.
4.
Detect, Alert, and Resolve at Speed
Get automated alerts, root cause analysis, and data lineage tools to rapidly mitigate issues.
Anomalo Data Proof Points
Take an AI-First Approach to Data Quality
“Anomalo takes an unusual approach to data quality, with its AI engine that profiles data and its values and detects statistically significant differences in data from what is expected and has been historically normal. It is also unusual in being able to apply the same approach to documents and other unstructured data. Anomalo should be on the shortlist of anyone looking at a modern data quality solution, especially where a lot of data is involved and where the scope extends to unstructured data.”
“Anomalo replaces traditional approaches with unsupervised machine learning that automatically detects issues within data content across formats. Their platform provides comprehensive coverage across data types and use cases, from analytics dashboards to data science algorithms and generative AI workflows. By automating data quality detection, Anomalo helps enterprises replace homegrown solutions and legacy platforms, enabling more reliable and efficient data utilization at scale.”
Torsten Volk
Principal Analyst
“Data is critical to the life blood of enterprises and data quality problems are real. IDC research consistently shows that there is a lack of trust in data, with data management and improvement being a high priority investment as we enter into the era of AI Everywhere. Anomalo is differentiated in its AI-first approach to data quality. Anomalo automatically builds ML models for each dataset based on the history, patterns and structure of the data, and these models can spot when issues arise in the data, enabling pro-active data quality resolution which can result in improved operations, analytics and AI, leading to better business outcomes.”
Stewart Bond
Research VP, Data Intelligence and Integration Software Research
A data quality platform that works with your data stack
Partners
Integrations
Our Customers
What Customers Say
“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.”
Prakash Jaganathan
Senior Director of Enterprise Data Platforms
“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.”
Cliff Miller
Enterprise Data Architect
“We were delighted with the functionality Anomalo provided, and their approach to monitoring matched our essential requirements.”
Stephan Claus
Director of Data
“Anomalo has transformed our data incident response pipeline so we’re no longer searching for a needle in a haystack.”
Angelo Sisante
Product Manager - Data Products
Featured Resources
Learn more about how Anomalo delivers best-in-class enterprise data quality monitoring.
Blog
Afterpay: Promoting Data Trust with Anomalo
Trust takes years to build and seconds to break—a phrase we often hear applied to relationships, but what about data? The story is the same.
Case Study
Discover’s Approach to Scaling Enterprise Data Quality Monitoring
You may have never needed to know the metric prefix zetta, which is of a scale a trillion times bigger than giga. You will be glad you know now, because by the year 2025 the volume of data created, captured, and consumed worldwide is projected to grow to more than 180 zettabytes.
Blog
Anomalo Earns Google Cloud Ready for BigQuery and AlloyDB
Anomalo, the complete data quality platform company, today announced that it has successfully achieved Google Cloud Ready – BigQuery and Google Cloud Ready – AlloyDB designations. Anomalo and Google Cloud are used by customers globally.
FAQ
Frequently Asked Questions
If you have additional questions, we are happy to answer them.
What kind of custom data quality monitoring does Anomalo offer?
While Anomalo’s automation offers immense value out of the box, you can also set user-defined validation rules or track specific business metrics for your key tables. You can do all of that from Anomalo’s UI, without even needing to write code. If you need more control, write checks in SQL or even integrate with our API to migrate existing checks. Anomalo’s flexibility makes it an ideal solution for data professionals looking to enhance their data integration processes and improve data quality across diverse datasets.
What data quality monitoring techniques does Anomalo utilize?
Anomalo uses a mix of data quality monitoring techniques to ensure coverage across your entire data warehouse. First, you can set up low-cost, metadata-based data observability monitoring for all your tables to ensure on-time delivery and completeness. Then, for the tables where you want to additionally monitor the data values themselves, you can set up Anomalo’s automated data quality checks, including AI-based anomaly detection. This uses unsupervised machine learning to learn the historical patterns in your data and look for unexpected changes or poor data quality. Finally, for tables where you want even more custom monitoring, you can set up user-defined validation rules and track key metrics.
Why is data quality monitoring important?
Data quality monitoring is essential to ensure that bad data doesn’t lead to poor business outcomes. Low-quality data can cause all kinds of problems, from broken products and user experiences, to inaccurate dashboards and reports, to machine learning and generative AI models that behave erratically. Data quality monitoring is important for compliance and data governance as well. If your business users don’t believe that they can rely on your data, they are less likely to make data-driven decisions. Without data management tools like Anomalo, you may end up investing a lot of time and resources to modernize your data stack, only to find that your efforts to get value from your data are blocked by inconsistency, inaccuracy, and overall lack of trust.
How does Anomalo ensure data quality at scale?
To offer data quality management for any data warehouse, even with tens of thousands of tables, Anomalo provides a suite of data quality checks. First, we offer data observability checks, which are a low-cost way to monitor your entire warehouse using table metadata. From there, you can configure tables to automatically measure data quality in one click. Automated data quality checks go deeper than observability to sample and inspect the data values themselves. You can also track KPIs about your data to identify trends and changes in segments. Finally, for that subset of key business tables where you need to write strict validation rules that your data must conform to, you can define those in Anomalo’s UI.
For most businesses, it’s impossible to write rules about every column and every table. That’s why Anomalo is considered one of the best data quality tools on the market, with our algorithms that use AI/machine learning to understand patterns, set thresholds, and know when to alert. These algorithms learn and adapt as your data assets grow and change over time.
Does Anomalo provide data profiling and analysis?
We do. Tables that are configured for data monitoring in Anomalo can display visual data profiling information, such as the distribution of data values in each column. Furthermore, each data quality check offers rich visualizations to understand why and how your data is passing or failing. For additional analysis, you can monitor key data quality metrics, which will generate more charts for exploration and notify you when there are significant changes.
Does Anomalo provide data lineage tools?
Yes! For each table that you monitor in Anomalo, you can see data lineage information that is pulled directly from your data warehouse/lakehouse, including a mapping of how data flows upstream and downstream. This allows you to quickly diagnose data issues and understand the potential impact of any failures, resulting in high-quality data for your company.
Ready to Trust Your Data? Let’s Get Started
Meet with our team to see how Anomalo transforms data quality from a challenge into a competitive edge.