Anomalo recognized for first time in Gartner’s Magic Quadrant for Augmented Data Quality Solutions

Enterprise Data Quality, Reinvented.

Anomalo has been recognized for the first time in the Gartner Magic Quadrant for Augmented Data Quality Solutions. This recognition builds on last year’s inclusion in Gartner’s Market Guide for Data Observability Tools.

As the only modern data quality vendor to be added this year, we’re honored that Gartner has recognized a newer vendor with a novel approach in a long-standing field. Gartner highlights several capabilities that set us apart, including our use of AI/ML, powerful data profiling technology, and support for monitoring unstructured data—crucial for more robust GenAI applications and workflows. 

This validation is a useful opportunity for us to share why and how we do things differently and why the most data-intensive enterprises like Discover Financial and ADP trust Anomalo for data quality.

Same Goals, Reinvented Approach

We say that Anomalo is Enterprise Data Quality Reinvented. Reinvention starts with our scope: rather than being one part of a data stack like many vendors on this Magic Quadrant are, we are singularly focused on data quality. We’re also proud of being a newer player, unencumbered by the challenges of legacy software. 

The goals of data quality monitoring remain universally true: to detect, alert, and resolve data issues. But we get there a different way. Anomalo is easy to adopt, straightforward to use, quick to market with new features—and most importantly, uses a novel and very successful approach to monitoring data quality. Let’s explain. 

Detection, Reinvented

Our foundation is a machine learning first approach, which scales quickly and keeps pace with the complexity of modern data. While the Anomalo platform incorporates traditional DQ methods like observability and validation rules, our hallmark is unsupervised machine learning.  Our unsupervised ML algorithms & models can monitor nearly limitless amounts of data for anomalies, automatically detecting issues with minimal setup and no user input, while preventing alert fatigue by focusing on truly meaningful issues. By contrast, traditional data quality methods, such as writing data quality rules or “eyeballing” metrics, can’t keep pace with the complexity and rapid evolution of today’s enterprise data landscape and often require constant maintenance in order to avoid endless false positives.  

Legacy data quality monitoring software is centered on rules, so their recent embrace of AI/ML for automation usually means speeding up the creation of more and more of those rules. That’s an acceptable way to increase the number of specific things to look for on more tables—and in fact, Gartner notes our ability to automate rule creation as a strength—but we know it’s possible to do much more with AI/ML. To us, automating rule creation is the equivalent of inventing machines to feed and clean up after horses. We’d rather build cars.

We’re not alone in seeing the value of an ML-first approach, but we are distinguishing ourselves by doing it very well. One newly onboarded Fortune 1000 customer spent two years of 15 FTEs’ time attempting to build a sophisticated, unsupervised data quality engine in-house—only to switch to Anomalo after seeing our platform outperform their own.  (For a deeper technical dive, we recommend the O’Reilly book by my co-founder for the science behind our approach.)

Alerting, Reinvented 

The primary value proposition of data quality monitoring is increased confidence in data quality, which is certainly lacking today: our research shows a nearly universal deficit of trust in data quality. We’ve built our alerting system to work with modern data stacks to restore business users’ faith in their company’s data.

First, let’s talk about alert fatigue. There’s no value in data quality monitoring if too much noise means the people who can fix issues ignore them until they become big problems. Our goal is to minimize false positives to only show alerts worth investigating. We also make it easy to assign clear responsibility to any given individual or class of tables, so it’s explicit who needs to fix what.

But alerting is about more than just remediation, it’s about bringing visibility and insight to everyone on the data team who has a stake in quality. Anomalo achieves this through deep, native integrations with leading data catalogs like Atlan, Alation, and Databricks Unity. This allows business users to see data quality status, table profiling, and flagged issues for the data they are working with, as well as understand what data has been validated as ready to use. 

Our customers are thrilled with this approach. Nationwide’s director of data governance called it “the best day of my life” when the company created a policy of cataloging in Alation and data quality with Anomalo.

Resolution, Reinvented

So you know of an issue. Now what? Some solutions promise automatic fixes, but without the benefit of human judgment, this approach often further compounds data quality issues. This “fix the symptom, not the problem” approach creates hidden or compounding errors that become even costlier and harder to remediate. We’re big believers in automation when it can do the job better, but in this case, human oversight ensures that the right root causes are addressed before changes are made. 

However, legacy data quality monitoring services tend to offer little guidance to aid problem-solving. The remediation process can take a lot of time (and hence cost money) if you have to first figure out where the problem even lies. That’s why we’ve developed an automated root cause analysis interface, an end-to-end triage tool, and integrations with JIRA and ServiceNow, giving data teams a clear roadmap for finding issues so they can isolate and resolve anomalies much faster. 

“Data engineers can concentrate on their core work and get involved in data incident resolution after analysts have used Anomalo to identify an issue’s root cause,” says Angelo Sisante, data product PM at Included Health “It’s been a cultural shift—data quality issues feel tractable now and something for which everybody can do their part.”

What Are We Reinventing Next? 

Our credo of reinvention takes us beyond tables, to thinking about what quality means across all data types and all data use cases. 

These days, that means unstructured data: the enormous collections of text, transcripts, and PDFs that the large language models of generative AI are trained on and consume in most generative AI workflows (including RAG, etc.). Because these models are only as good as the data they’re consuming, our customers are increasingly looking to us for assurance that their unstructured data is both high-quality and fit for purpose for their AI use cases. In fact, Gartner highlighted our unstructured data monitoring product as a strength, and we’re eager to keep solving this challenge with our customer base. 

Growing Confidence from Industry Leaders

Our modern ML-first approach has also attracted partners who share our vision for the future of data and AI. Databricks and Snowflake, for instance, have recently invested in Anomalo and Anomalo was also named the Databricks Emerging Partner of the Year. These platforms see firsthand the challenges of large-scale data quality and the needs of modern data teams, and have bet on Anomalo’s comprehensive, automated approach to solve them.

Nothing brings us more joy than hearing of data teams who spend less time firefighting data issues, and more time using data to make confident decisions and build products their customers love. Let’s reinvent the future of your data together. Book a demo today.