Anomalo Unveils the 6 Pillars of Data Quality Critical for AI Success

 Anomalo, the company reinventing enterprise data quality, outlined the 6 Pillars of Data Quality that empower enterprises to achieve AI success without compromise: Enterprise-Grade Security; Depth of Data Understanding; Comprehensive Data Coverage; Automated Anomaly Detection; Ease of Use; and Customization and Control.

Every era of technology has its defining moment—the point when the foundations either prove resilient or collapse under new demands. For enterprises entering the age of AI, that foundation is trusted data. Yet most organizations are still building on unstable ground which is why 95% of generative AI pilots are failing to deliver measurable business value. This challenge is compounded by sprawling data stacks and more than 80% of enterprise information unstructured which creates blind spots that undermine AI initiatives and put transformation at risk.

For decades, enterprises have been forced to accept tradeoffs in data quality, with depth or scale, automation or control, coverage or security. Those compromises may have been tolerated when data simply informed quarterly reports. In the AI era, however, flawed data means flawed models that make billion-dollar decisions in real time. The cost of compromise is now unacceptable.

“Every compromise on data quality slows your AI initiatives and gives competitors an edge. When your competitors are moving faster, even small compromises widen the gap in accuracy, outcomes and decision-making. We built Anomalo from the ground up as an AI-first platform so our customers can trust every dataset without trade-offs,” said Elliot Shmukler, co-founder and CEO of Anomalo.

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Anomalo is the only data quality provider backed by both Databricks and Snowflake Ventures. Leading enterprises across every major industry trust Anomalo to deliver data quality without compromise. Drawing on this experience with some of the world’s most data-driven organizations, Anomalo developed the Six Pillars of Data Quality to provide enterprises with a foundation they can trust in the AI era.

To ensure enterprises can fully trust the data fueling their analytics and AI deployments, Anomalo has defined the Six Pillars of Data Quality:

1. Enterprise-Grade Security. In the age of AI, security, compliance, and scalability are not differentiators—they are baseline requirements. A true enterprise-grade solution must be deployed in your owned environment, only use LLMs approved by your organization, meet strict compliance mandates, and operate reliably at the massive volumes demanded by real-time AI workloads.
2. Depth of Data Understanding. Some approaches to data quality rely on metadata checks, branded as ‘data observability’, to detect data quality issues. But surface-level checks come at a steep cost. They miss abnormal values, hidden correlations, and subtle distribution shifts that quietly distort dashboards, analytics, and AI models. The price of these misses is significant. Industry research¹ indicates poor data quality costs organizations an average of $12.9 million annually, while also compounding long-term complexity and eroding decision-making. True data quality requires direct, intelligent inspection of the contents of the data itself.
3. Comprehensive Data Coverage. Enterprises today manage sprawling data estates with tens of thousands of tables with billions of rows. Monitoring only a few high-profile tables isn’t enough; an issue with any table, structured or unstructured, can cascade across business processes. With 80-90% of enterprise data now unstructured, blind spots are dangerous, especially as organizations scale AI. Coverage must extend across all data types and use cases.
4. Automated Anomaly Detection. The size and complexity of enterprise data stacks makes manual or rules-based monitoring unsustainable. Some providers attempt to “scale” rules with AI-generated checks, but this merely scales yesterday’s methods with today’s tools. Rules, no matter who or what writes them, only catch anticipated issues. Enterprises need AI-native anomaly detection that uncovers unexpected and emerging issues at scale, without any user input needed.
5. Ease of Use. Data quality insights are only useful if teams can act on them. Business analysts, operations leaders, and data engineers all need to quickly understand, validate, and act on data quality issues quickly. A modern solution must democratize data quality across the enterprise without requiring specialized coding skills. By providing an intuitive no-code UI and empowering all data users, it reduces dependency on data engineering teams and accelerates issue resolution.
6. Customization and Control. Every enterprise has unique business rules, regulatory obligations, and operational priorities. A one-size-fits-all solution fails to meet those needs. Enterprises must be able to customize monitoring to track the metrics that matter most, integrate with existing tools and workflows, and to direct alerts to the right teams. Without this adaptability, organizations risk alert fatigue, unnecessary noise, and eroded trust in the system.

For enterprises that demand trusted data, Anomalo delivers uncompromising data quality at enterprise scale. Legacy tools force organizations to choose between depth and breadth of coverage, ease of use and security, and automation and control. Anomalo eliminates those trade-offs. With comprehensive coverage across both structured and unstructured data, seamless integration into modern data stacks, and enterprise-grade security, Anomalo enables organizations to trust their data everywhere it resides. This is the foundation required to move faster, make confident decisions, and fully prepare data for the AI era.

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