Announcing Alation Data Quality: Restoring Trust in Your Data

Today, quality data can often spell the difference between business success and failure. In fact, Gartner projects that poor data quality costs the average business about $12.9 million each year. Small wonder, as poor data quality leads to flawed AI models, operational errors, and costly decisions – creating distrust between data producers and consumers. This lack of trust can severely hinder an organization's ability to make informed decisions and achieve desired outcomes.

Enter Alation Data Quality (DQ). This new, AI-native solution is designed to restore trust in data by identifying and proactively monitoring critical data assets while automatically applying relevant quality rules. By leveraging the power of AI alongside rich metadata within the Alation Data Intelligence Platform, Alation Data Quality Agent empowers organizations to prioritize, monitor, and remediate data quality issues at an enterprise scale.

The top three data quality challenges—and how Alation solves them

Poor data quality isn’t just an inconvenience—it’s a business risk. Inconsistent, unreliable, or incomplete data can lead to financial and reputational damage, making it difficult for teams to determine which data assets to trust, what to monitor, and how to enforce quality standards efficiently. The top three data quality challenges organizations face today are:

• High data volumes make it impossible to prioritize the right information

• A trust gap between data producers and consumers

• Manual, siloed data quality efforts waste time and resources

Alation DQ integrates data quality monitoring directly into the Alation Data Intelligence Platform, embedding it within metadata, governance, and behavioral insights. This allows organizations to proactively detect, prioritize, and resolve data quality issues where they matter most: at the point of consumption.

Here are the three biggest data quality challenges—and how Alation solves them:

Challenge #1: Prioritizing the data that matters

With enterprises managing massive volumes of data, teams struggle to identify which datasets require attention. This often results in either wasted effort monitoring everything or overlooking critical data assets. Alert fatigue further compounds the problem, as teams become overwhelmed with low-priority notifications.

Alation DQ Agent ensures focus by:

• Leveraging metadata-driven insights—such as usage frequency, dependencies, and governance policies—to prioritize high-value data assets.

• Applying AI-powered automation to detect and enforce relevant data quality rules based on data type, lineage, and business context.

• Reducing alert fatigue by surfacing only actionable, high-priority data quality signals.

By cutting through the noise, Alation helps teams focus on the data that truly drives business success.

Challenge #2: Ensuring data reliability to rebuild trust

Inconsistent, incomplete, or outdated data erodes trust between data producers and consumers, forcing teams to rely on manual workarounds to validate data reliability. This delays decisions and leads to inefficiencies.

Alation restores confidence by:

• Identifying trusted data assets through behavioral metadata, tracking usage patterns, and highlighting frequently queried datasets as proxies for reliability.

• Embedding real-time data quality signals within workflows, which allows users to assess data health, lineage, and governance directly in the catalog.

• Automating data accuracy, completeness, and consistency checks, reducing reliance on manual validation.

With quality insights surfaced at the point of consumption, users can make informed decisions without second-guessing their data or the teams who have supplied it.

Challenge #3: Eliminating siloed, manual efforts

Many traditional data quality tools require manual setup, complex configurations, and ongoing maintenance. These factors can introduce inefficiencies and force organizations into hard trade-offs—deciding whether to monitor data quality effectively or control costs.

Most data quality tools today are priced based on the number of checks or assets monitored, making comprehensive coverage prohibitively expensive. As a result, organizations must constantly weigh the value of monitoring against budget constraints, leading to limited visibility into data quality risks.

Alation unifies data quality management by:

• Automating rule generation based on semantic context, reducing manual configuration.

• Integrating governance, lineage tracking, and quality enforcement within a single system for seamless oversight.

• Providing end-to-end visibility for root cause analysis and proactive issue resolution.

By embedding data quality into governance workflows, Alation shifts data quality management from a reactive, manual process to an intelligent, proactive system—ensuring reliable data at scale.

Enhancing the open data quality ecosystem

Alation remains committed to its Open Data Quality Initiative (ODQI), ensuring that organizations can leverage the best data quality and observability tools available to meet their unique needs. While ODQI enables seamless integrations with best-of-breed solutions like Anomalo and Monte Carlo, many customers have expressed the need for native data quality capabilities—a way to monitor and assess their most critical data with the right level of alerting, without adding complexity.

Alation DQ is designed to complement—not replace—existing data quality and observability solutions. It provides an on-ramp to data quality, giving teams an easy way to start assessing data reliability within Alation before deciding on deeper integrations or standalone tools. This tiered approach allows organizations to scale their data quality efforts based on business priorities.

Why build native data quality capabilities?

Customers told us they need:

• A fast, integrated starting point for monitoring essential and critical data elements (CDEs) without requiring a separate tool or additional expertise.

• Embedded data quality insights within Alation to unify observability across multiple data sources and platforms.

• Automated, policy-driven data quality checks that align with governance and compliance efforts while minimizing alert fatigue.

By offering native data quality functionality alongside our open ecosystem, Alation provides more choice and flexibility. Organizations can quickly implement foundational data quality practices while maintaining the freedom to integrate best-of-breed solutions as their needs evolve. Our mission remains unchanged: to help customers maximize trust in their data—on their terms.

The future of data quality: Metadata-driven, AI-powered, and seamlessly integrated

Ensuring high-quality data shouldn’t be a disconnected, manual process—it should be an inherent part of data intelligence and governance. Alation is uniquely positioned to make this vision a reality. By tracking metadata, it provides visibility into data lineage, usage, and trustworthiness. Its deep integration of governance, policies, and user behavior ensures that data quality decisions are made in the right business context. With AI-driven automation, Alation enables proactive monitoring, detection, and application of quality rules at scale.

With Alation Data Quality, organizations can intelligently prioritize, monitor, and remediate data quality issues—ensuring that trusted data powers every decision, AI model, and business process.

To learn more:

• See the Alation Agentic Platform, Documentation Agent, and Alation Data Quality product pages

• Explore the Data Products Marketplace

• Read the press releases: Alation Agentic Platform, Data Products Marketplace, Data Quality

• Explore our professional services offering, the Data Products Playbook