How Agentic AI Transforms Data Quality Management

Poor data quality isn't just a technical inconvenience—it's a multi-million dollar business risk that can erode customer trust, waste countless hours of valuable resources, and lead to decisions based on faulty information. In our data-driven world, where autonomous agents and AI applications increasingly rely on structured, quality data, the stakes have never been higher.

Traditional approaches to data quality monitoring have left organizations struggling with manual processes, unclear priorities, and reactive solutions that often miss what matters most. But what if there were a way to transform data quality from a siloed chore into a strategic advantage?

In a recent webinar, "Redefining Data Quality: Smarter Monitoring with Alation's Agentic AI," Kyle Johnson, Product Manager at Alation, and Steve Wooledge, VP of Product Marketing, unveiled an innovative approach that's already helping organizations achieve smarter, faster, and more actionable data quality monitoring right out of the box.

The data quality crisis: 3 key challenges

The challenges facing data professionals today are stark. As Wooledge noted, "Data scientists can waste 60% of their time here, I've heard it as high as 80% in the traditional data management world. Organizations can lose money in terms of $5 million annually, as an estimate for 25% of organizations from Forrester."

The root of these challenges lies in three critical areas that have plagued data quality initiatives:

What to monitor: In massive ecosystems spanning multiple databases, BI tools, and data sources, teams struggle to identify which assets truly matter to their business outcomes.

How to monitor: Determining the right quality dimensions—completeness, accuracy, consistency—and applying them at scale becomes overwhelming without proper tooling.

Where to take action: Even when issues are identified, teams often lack clear ownership structures and efficient remediation workflows.

These pain points create a vicious cycle where "data quality programs can stall or stay reactive," as Wooledge explained, leading to issues that "often go unnoticed until it's too late."

Behavioral intelligence: Focusing on what matters

Alation's approach begins with a fundamental shift in how organizations prioritize their data quality efforts. Instead of blanket monitoring across all data assets, the platform leverages behavioral intelligence to identify the most critical data based on real usage patterns.

"We leverage our behavioral analysis engine, or what we call BAE, which helps teams discover which assets are being used, how often, and by whom, to eliminate the guesswork because we're using real telemetry data," Wooledge explained. This approach analyzes query logs from BI tools and other data sources to surface the assets that truly drive business value.

During the live demo, Johnson demonstrated how this works in practice: "When you connect to the catalog, we do things like metadata extraction and on top of that query log ingestion... we can go down to the underlying data source, parse through the query logs, and look at the most queried tables."

This behavioral intelligence approach delivers immediate value by helping teams "target your most high-impact assets. So that means less noise, more signal, and a better return on your time and your data quality investment."

How agentic AI does the heavy lifting of data quality

The second pillar of Alation's solution addresses the manual burden that has long plagued data quality initiatives. Traditional approaches require teams to write custom SQL rules for every check—a time-consuming process that doesn't scale effectively across large data estates.

Alation's Data Quality Agent changes this paradigm entirely. As Johnson demonstrated, "what's happening here is we're using the power of metadata that exists in the catalog, along with LLMs" to automatically generate comprehensive data quality checks.

The results are impressive. In the demo, Johnson showed how the agent analyzed a credit score table and automatically recommended nine different checks across multiple data quality dimensions. "A credit score has a defined range, for example, of 300 and 850. So I'm looking for some sort of accuracy check here that's compliant within that range... let's see what it returned. As you can see here, I defined a min check of 300, a max check of 850, so that's great."

This automated approach means teams can "scale that quality coverage across thousands of assets in minutes, not months," making data quality accessible even to "business domain users who might not be technical. You don't need to know SQL."

Proactive resolution through a unified experience

The third differentiator addresses the operational challenge of acting on data quality insights. Rather than isolating alerts in separate systems, Alation embeds data quality signals directly into existing workflows.

"We're making sure that it's exposed and visible throughout the catalog," Wooledge emphasized. "I mentioned Slack, Teams, Messaging, Chrome, directly with BI tools... so that you can drive immediate awareness and action and be able to collaborate with the data stewards."

Johnson's demonstration showed this integration in action, with real-time Slack notifications appearing when data quality checks failed. "Here's our credit score monitor with a total of seven checks. And because one of the checks failed… an alert was triggered proactively where I can go and I could click on this monitor to get brought into back into the Alation catalog."

This unified approach ensures that "analysts can see if a data set is trustworthy before they use it. Executives can see directly in their dashboard the data quality health or the BI tool that they're looking at. And stewards can resolve issues from where they're managing the metadata."

Real-world impact: Early customer results

The solution isn't just theoretical—it's already delivering results for organizations in production. As Johnson noted, "We've been running with a number of customers. We've also got a number of customers to sign up and we're now deployed in their production environments."

Early adopters are finding that "DQ allows us to get down to some really operational use cases within our existing customers, like solving SLA requirements or key KPIs to monitor within their business requirements."

The platform's flexibility accommodates diverse organizational needs, from basic table-level monitoring to complex multi-source scenarios. When asked about handling data from multiple systems, Johnson confirmed: "We have customers who are active using up to two or three different sources, bringing them into AlationDQ, writing checks on data within each of these."

The open ecosystem advantage

Importantly, Alation's native data quality solution doesn't replace its established partner ecosystem. Johnson emphasized a commitment to openness: "We've long had this open data quality framework that has allowed a number of great partners to connect via our APIs... And we fully plan to maintain this approach."

This philosophy recognizes that "there's a wide spectrum to this market, meaning what Alation DQ might do is different than what another vendor might do." Organizations can leverage Alation's behavioral intelligence and native capabilities while maintaining partnerships with specialized tools for specific use cases.

Scaling data quality in the AI era

As organizations increasingly deploy AI applications and autonomous agents, the importance of reliable data quality becomes exponential. Poor data quality doesn't just impact human decision-making—it can lead to "more hallucinations or errors, not just in... recommendation engines and machine learning models, but think about generative AI and how it could potentially struggle with structured data."

Alation's approach addresses this challenge by making data quality "not just visibility, it's the operational value, getting that alignment across the organization and making sure we drive outcomes where it matters most."

The future of intelligent data quality

The webinar revealed a vision where data quality transforms from reactive maintenance to proactive intelligence. By combining behavioral analytics, agentic AI, and integrated workflows, organizations can finally achieve the scale and effectiveness that traditional approaches have promised but failed to deliver.

As Wooledge concluded, this represents "embedded data intelligence" that goes beyond simple monitoring to drive real organizational alignment and business outcomes. For data professionals who have long struggled with the manual burden and unclear priorities of traditional data quality approaches, Alation's solution offers a compelling path forward.

The future of data quality isn't about working harder—it's about working smarter, with AI agents that understand your data, behavioral intelligence that prioritizes what matters, and integrated workflows that turn insights into action. For organizations ready to transform their data quality from a necessary chore into a strategic advantage, that future is available today.

Ready to see how Alation's Data Quality solution can transform your organization's approach to data monitoring? Download the whitepaper, Data Quality in the Agentic AI Era.