Denodo unveils DeepQuery to provide AI-powered deep analysis
The data virtualization specialist's new GenAI feature lets users dig deep into their data to discover the reasons underlying what's happening within their organization.
With the introduction of DeepQuery, Denodo aims to enable users to go beyond retrieving facts with generative AI to instead do complex analysis.
The tool was unveiled Monday and is now in private preview. In addition, Denodo unveiled support for model context protocol (MCP), joining Databricks and Snowflake -- among many other data management providers -- in its embrace of the open standard for how large language models (LLMs) interact with data sources.
Many data management and analytics vendors during the past two years have developed generative AI (GenAI) capabilities that enable users of all technical skill levels to ask questions of their organization's data using natural language rather than code. However, such tools often respond to queries with only basic facts, showing what happened, perhaps accompanied by a summarization, but no explanation about why something happened.
DeepQuery -- which will be part of the Denodo AI SDK once generally available -- takes the next step. It enables Denodo users to ask questions about the reason something happened, investigating data across multiple systems and sources and returning responses rooted in real-time information.
Given that DeepQuery does more than deliver basic responses, the tool is a valuable addition for Denodo users, according to William McKnight, president of McKnight Consulting Group.
"These capabilities allow users to gain a deeper understanding of their data, make more informed decisions, and uncover hidden patterns and relationships, ultimately driving business value and innovation through more intuitive and accessible data analysis," he said.
Based in Palo Alto, Calif., Denodo is a metadata management specialist whose platform connects data using a data virtualization architecture. Competitors include data virtualization specialists such as AtScale and Datameer, as well as broad-based data management vendors providing data integration tools.
Digging deep into data
Denodo was one of the many data management and analytics vendors to provide users with GenAI capabilities that enabled them to query proprietary data.
Through integrations with OpenAI's ChatGPT and Microsoft's Azure OpenAI, Denodo developed a natural language query tool that enabled users to query the metadata housed within the vendor's platform. The feature employed retrieval-augmented generation (RAG) to access data relevant to a given query and text-to-SQL translation capabilities so the AI pipeline could understand the user's query and respond in natural language.
These capabilities allow users to gain a deeper understanding of their data, make more informed decisions, and uncover hidden patterns and relationships, ultimately driving business value and innovation through more intuitive and accessible data analysis.
William McKnightPresident, McKnight Consulting Group
But the feature could respond to only factual questions, such as what happened and when it happened, according to Pablo Alvarez, Denodo's vice president of product management.
"These capabilities empower business users and analysts to get quick, contextual answers without needing deep familiarity with the data or writing complex queries, [which] has improved self-service and reduced dependency on technical teams," he said.
However, data-informed decisions often require more than mere facts, Alvarez continued. They require understanding why something happened and what should be done next.
"DeepQuery is built for exactly that," Alvarez said.
DeepQuery does more than simply rephrase existing content, as Denodo's previous natural language query capabilities did. The feature can answer open-ended questions by searching data across multiple internal systems and adding context from other available data sources, such as publicly available data and data from trading partners when applicable.
Before DeepQuery, Denodo users would have had to piece together reports and other data sources to answer a question such as, "Why did sales drop during the spring of 2025 compared with the spring of 2024," and the question would have taken analysts days to answer. Now, it takes minutes, according to Denodo.
As a result of its potential to enable deep data analytics in a minimal amount of time, DeepQuery is an encouraging addition for Denodo users, according to Michael Ni, an analyst at Constellation Research.
"DeepQuery promises to deliver the logical AI-powered extension of Denodo's virtualization strength," he said.
In addition, DeepQuery is a potential differentiator for Denodo, Ni continued. He noted that LLMs offer deep analysis of public web data and BI tools enable deep analysis of pre-indexed proprietary data, while data virtualization vendors typically focus on providing access to data and analytics.
"Denodo's unique play is leveraging its data virtualization foundation to provide explainable deep research-type answers by investigating, synthesizing and providing multi-hop reasoning across all governed enterprise data in real time," Ni said.
McKnight similarly suggested that DeepQuery has the potential to help Denodo stand apart from its competitors. However, with technology evolving faster than ever before, differentiation can be short-lived.
"DeepQuery's innovative features, such as answering complex questions and providing explainable answers, may differentiate it from some competitors, but the landscape is constantly evolving," McKnight said.
Looking ahead
With DeepQuery now in preview and MCP part of Denodo's AI SDK, GenAI will remain a major focus for Denodo with AI governance, security and semantic consistency all points of emphasis, according to Alvarez.
"As organizations move beyond centralizing analytics in lakehouses, they're realizing that high-impact decisions require integrating and analyzing data across multiple systems, apps and even third-party sources," he said. "We believe the best place to support GenAI-based decision-making is in a logical layer that spans the entire enterprise, not in isolated silos."
Ultimately, however, Denodo may need to provide more than a layer that supports GenAI to continue serving the needs of its users and potentially attract new ones, according to Ni.
"To stay ahead, Denodo will need to evolve from a logical data layer into a full participant in AI-driven decision orchestration, deepening its AI integration, expanding metadata intelligence and rationalizing its role in distributed data and agentic workflows," he said.
McKnight, meanwhile, suggested that Denodo add agentic AI capabilities to remain competitive.
The vendor provides strong real-time analytics data governance and integration capabilities across diverse data sources, he noted. Adapting to emerging trends, therefore, will be key to maintaining its standing.
"Denodo could further leverage agentic AI to enhance its data virtualization platform by automating data integration, improving data governance and providing predictive analytics and personalized data experiences," McKnight said.
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.
