Anomalo named in the Forrester Wave and Why That Matters for AI-Driven Data Quality

Anomalo Named a Contender in the Forrester Wave for Data Quality Solutions

Forrester’s latest Wave makes it clear that data quality is no longer a background function. Organizations are racing to operationalize AI and quickly realizing that trusted, high-quality data is becoming the foundational infrastructure needed to achieve real results. 

The Path to AI Readiness Runs Through Data Quality

According to Forrester, the biggest hurdles to scaling AI initiatives aren’t model performance or infrastructure. It’s data readiness, access, and trust. Enterprises are grappling with increasingly complex data environments that now span structured, semi-structured, and unstructured sources. To meet this challenge, Forrester highlights the growing importance of:

• Deep profiling and classification across all data types to establish context and trust
• Continuous observability and automated anomaly detection to prevent downstream failures
• Support for multimodal and unstructured data, which now fuels AI, analytics, and digital transformation initiatives

To put it simply, data quality is becoming mission critical for AI readiness. That’s why we previously created the Data Quality Without Compromise framework, to help organizations understand how to evaluate vendors that can truly prepare their data for AI. 

Redefining a Category Means Being Measured by Yesterday’s Standards

When Anomalo set out to reinvent data quality, we knew we were building for a future that wouldn’t always map cleanly to how the category had historically been defined. Market assessments often reflect what has mattered most in the past. The Forrester Wave™ captures a data quality market in transition, with evaluation criteria shaped by an era of static data, manual rules, and centralized governance platforms. AI-driven enterprises now operate in a fundamentally different reality. Data changes constantly. Unstructured content increasingly fuels analytics and AI systems. And trust must be continuously earned, not periodically verified.

As an AI-native platform, Anomalo was built for this new reality from day one. Rather than extending legacy approaches, we focused on automation over configuration, learning systems over static rules, and depth of understanding over platform sprawl. As the market evolves, so will the way success is measured. History shows that the companies defining the future are rarely perfect fits for the past.

Why AI-Driven Enterprises Need a Different Kind of Data Quality

Forrester notes that Anomalo is a strong fit for organizations on modern data stacks that want an AI-first approach to data quality that works alongside their existing data management tools. This is a direct result of the intentional design choices we’ve made in our architecture from the beginning.

Notably, Anomalo was the only vendor in the Forrester Wave™ to receive an above-average score for customer feedback. This reflects the impact customers see when using Anomalo day to day. We’re seeing that same feedback from customers across the market including our recent recognition as a Strong Performer in the 2025 Gartner Peer Insights Voice of the Customer for Augmented Data Quality Solutions, based entirely on direct customer reviews and ratings.

Enterprises don’t need another platform claiming to be the control plane for data. They need trusted signals that move at the same speed and scale as the systems it supports. Teams need to see when distributions drift, when new patterns emerge, when context is lost between systems, and when schema changes subtly alter meaning without breaking pipelines. These aren’t edge cases. They’re the natural behavior of data in modern, AI-driven environments and enterprises need this level of deep data understanding to be successful.  

This means lower operational overhead for data teams and greater confidence in the analytics, applications, and AI systems that depend on reliable data. It’s why some of the world’s most demanding and regulated companies in industries like financial services and retail depend on Anomalo to power their data quality.  

What data leaders should take away

The Forrester Wave™ on Data Quality reinforces that data quality is now the foundation of AI trust. As AI, and more specifically agentic AI, moves from experimentation into production, the stakes for data quality have never been higher. The next phase of data quality won’t be won by platforms using legacy approaches or those using AI only to accelerate manual rule creation. It will be defined by systems that learn continuously, surface issues humans don’t know to look for, and scale trust faster than data and AI usage grow.

Enterprise buyers must weigh platform breadth against depth, autonomy, and AI readiness as data quality becomes foundational to analytics and agentic AI. As data leaders make this shift, Forrester recommends asking yourself these questions: 

• Can data issues be detected without knowing what to look for in advance?
• Can impact be understood early, before downstream systems break?
• Can data quality scale as data volumes, complexity, and AI usage continue to grow?

These questions reflect where the market is headed, or for Anomalo customers, where it already is. As AI becomes embedded in how organizations operate, data quality must evolve alongside it. It must become more continuous, more automated, and more deeply connected to how data is actually used across analytics, operations, and AI systems.

That’s the future Anomalo is building toward. Data quality that doesn’t just monitor data, but actually learns with it. Adapts as your data changes and provides the trust modern enterprises need to move fast without breaking confidence. 

If you’re operationalizing AI, or planning to, now is the moment to pressure-test your data quality foundation. Request a personalized demo to see how Anomalo has reinvented data quality to meet the evolving needs of modern enterprises.