2025 Year in Review: Why Trusted Data Became the Prerequisite For Enterprise AI

One of the best aspects of my job at Anomalo is having a front-line seat to the evolution of the world of data. And let me tell you, the pace of this evolution is something to behold. Just a few years ago, we were all discussing the Modern Data Stack and the centralization of data in modern cloud-based data warehouses, such as BigQuery, Databricks, and Snowflake. Now, everyone is talking about AI, whether it’s the use of conversational analytics to access your data, or the data-based foundations of Agentic AI. 

With all the hype, and the fast pace of change, it’s easy to get distracted. Which is why at Anomalo, we try to always work backwards from what our customers actually need. 

Working Backwards From What Enterprises Need

When you strip away trends, and focus on what organizations actually require to operate on their data with speed and confidence, four needs emerge:

• Data that users can trust and access at all times
• Systems that allow data teams to proactively address any failures and issues, rather than being reactive and fighting fires 
• Governance and visibility across the entire data estate, including all data types
• Interfaces and experiences that make trusted data usable by every team, not just technical experts

These needs formed the basis of the six pillars we outlined in Data Quality Without Compromise. And throughout the year, enterprises reinforced the message that they can no longer afford to make trade-offs on depth or breadth, automation or control, ease of use or rigor. These “either/or” choices don’t align with how data leaders expect modern systems to work.  

Here at Anomalo, we reject the tyranny of “or.” Enterprises should expect comprehensive data quality coverage, meaningful depth and automation, and enterprise-grade security, all in a single data quality platform. That’s why our work this year centered on expanding the systems and capabilities that help enterprises build durable, trustworthy data foundations without compromising on scope, quality, or control.

From that foundation, four themes defined how we advanced our platform in 2025.

#1 Automation through an AI-based approach 

Anomalo was the first company to realize that autonomous systems can continuously detect  data quality issues at scale, without adding the operational burdens of setting up manual rules and thresholds. That’s why Anomalo’s approach continues to invest in our proprietary unsupervised machine learning (UML) to learn the normal behavior of each dataset directly, enabling automated systems to monitor data quality continuously and comprehensively. And as new advances in AI emerge, we continue to refine these capabilities so enterprises gain a deeper, more proactive understanding of their data.

#2 Converting Unstructured Data into First-Class Data

Unstructured data has historically been hard to use because it’s messy and difficult to trust. As a result, most enterprises still make limited use of their unstructured data, even though it represents the majority of what they collect. In early 2025, we introduced Anomalo Unstructured, which uses generative and agentic AI to help data teams understand and evaluate the quality of their text-heavy content such as documents, transcripts, and other formats  that don’t fit neatly into rows and columns. Understanding  this data matters because unstructured content often carries the context that explains why events occur, insights that  traditional structured data alone cannot provide.

#3 Making High-Quality Data Usable for Everyone with AIDA

Quality data is only valuable if people can actually access and query it. Broadening access to trustworthy data reduces bottlenecks, accelerates decision-making, and ensures that high-integrity information reaches the teams closest to the work. In October, we announced AIDA, Anomalo’s Intelligent Data Analyst, to address this need. AIDA brings natural-language interaction to the data already monitored and understood by Anomalo, enabling anyone—not just SQL or Python experts—to query data, answer their data-driven questions, and obtain data-driven insights. With AIDA, we give enterprises a clear path to democratize access to data, without compromising accuracy or security. 

#4 Strengthening the Enterprise Data Ecosystem

Enterprises increasingly expect their core platforms, such as data warehouses, governance tools, AI frameworks, and operational systems, to work together without friction. That expectation only grows as AI becomes embedded across workflows. This year we expanded our partnerships across Databricks, Google Cloud, Snowflake, and leaders in catalogs and governance such as Atlan. We invested in shared architectures, native integrations, and joint product launches, all designed to make data quality a first-class capability in the modern stack.

Signals From the Market

The strongest confirmation of our strategy came from our customers who helped shape the evolution of our platform this year, not just as users, but as partners in defining what enterprise-grade data quality needs to look like in an AI-driven world. That alignment was further reflected in the recent Gartner Peer Insights report, where Anomalo was named a Strong Performer in the 2025 Voice of the Customer for Augmented Data Quality Solutions. This recognition is meaningful because it is based entirely on direct customer reviews and satisfaction. According to the report, 95% of customers were willing to recommend Anomalo, the highest in the category. 

Perhaps more importantly, customers saw measurable impact with Anomalo:

• For a major financial services company, Anomalo identified more issues than their previous rules-based system. That system had required maintaining more than 3,000 manual rules, demonstrating the limits of legacy approaches.
• One enterprise company achieved a 15% increase in new customer acquisition after improving the quality of the data powering its marketing programs.
• A payroll services company empowered their data stewards with robust data quality and governance. They went from having just 700 checks to over 25,000 daily checks, and from spending their time on very granular, micromanagement activities to more value-added activities.
• A major financial services firm simplified data access by establishing a single data quality layer that connects privately to cloud data in Snowflake, Databricks, and on-premises databases.
• After a global financial services firm’s multi-million-dollar, two-year internal project to build a crucial streaming data lake failed due to complexity and the inability to manage data quality, Anomalo was deployed. By successfully monitoring the complex streaming data, Anomalo achieved in weeks what a large internal team could not in years, restoring trust and readiness for AI initiatives in their mission-critical data and making the data ready for AI.

These outcomes highlight a broader pattern: when data quality becomes a strategic capability, its impact compounds across the enterprise. 

We also saw strong validation across the broader industry this year:

• Strategic investment in Anomalo by Snowflake Ventures 
• Recognition in Gartner’s Magic Quadrant for Data Quality and Forrester’s Data Quality Solutions Landscape
• Named one of CRN’s Coolest Data Management Companies of 2025
• Expanded to the AWS Marketplace and now running across all major public clouds
• Launch partners for Databricks Unity Catalog Metrics, Snowflake Intelligence, and the Atlan App Framework

These signals matter not because they reflect well on us, but because they reinforce how essential trusted data has become in the enterprise. 

What Enterprises will Require in 2026

As I look toward 2026, the work ahead is becoming clear. As enterprises adopt agentic AI, their structured and unstructured data will form a critical foundation for any successful AI deployment. This will require an even greater level of quality, trust, and documentation of all of their most important data. 

We are already building in this direction. In 2026, we will bring an even greater level of data context, understanding, and insights to the Anomalo platform – pushing toward an environment where modern AI agents can simply connect to the key data they need without extensive (and often manual) integration work. 

This is the foundation of true enterprise intelligence. The kind of intelligence you ultimately need to make AI successful.

Our mission at Anomalo is to ensure enterprises never have to guess whether their data can be trusted. To make the data foundation strong, scalable, and accessible so enterprises can ultimately use their data and their AI with confidence, rather than caution.

We are just getting started.