dbt Labs unveils AI tools to boost data autonomy & governance

dbt Labs has released a suite of AI-powered features aimed at providing data analysts with greater autonomy without compromising data governance.

The newly launched capabilities include dbt Canvas, a visual drag-and-drop model development tool; dbt Insights, an AI-assisted query environment; and an enhanced dbt Catalog for data asset discovery. These features are designed to support analysts with varying technical skills, offering both natural language and visual interfaces within a version-controlled setting managed by dbt workflows.

The announcement comes as many organisations grapple with the balance between data self-service and the need for strong governance, a concern highlighted by a recent Gartner prediction stating, "by 2027, 60% of organisations will fail to realise the full value of their AI use cases due to fragmented data governance frameworks." Gartner identifies the proliferation of ungoverned data workflows among analysts as a driver for compliance risks, increased operational costs, and compromised data quality.

Tristan Handy, Founder and Chief Executive Officer of dbt Labs, commented on the issue, saying, "Data teams today face a fundamental tension – analysts need speed and independence, while organisations require strong governance and security. Our new AI-powered solutions break down these traditional barriers for data analysts across any skill level and collaborate with developers in the same platform, which will have a significant, positive impact throughout the business."

At the centre of the launch is dbt Canvas, which provides users who prefer drag-and-drop interfaces the ability to model and edit data. Integrated with dbt Copilot, it leverages natural language processing to help teams with limited SQL experience construct data models efficiently. The tool preserves organisational data governance and quality standards while promoting team collaboration and productivity. dbt Canvas is now generally available.

dbt Insights, currently in preview, allows analysts to query, validate, and visualise data using either SQL or plain English. The tool takes into account a company's data models, lineage, and governance protocols, helping users conduct analysis and share insights within a governed workspace, reducing reliance on centralised data teams to fulfil requests.

The expanded dbt Catalog now offers a consolidated search and exploration experience across Snowflake assets, even those not previously managed in dbt. This feature streamlines discovery and helps analysts understand and trust their data sources without moving between different platforms. While the catalogue is generally available, Snowflake asset exploration remains in preview, with further integrations promised.

Dan Jewett, Senior Vice President, Product Management at Tableau, welcomed the update, stating, "Lowering the technical barrier to entry for data analysts has been important to Tableau from the beginning of the company. dbt's expanded offering is a game changer for customers that are looking to reduce the sizable burden on their data engineering teams, while simultaneously enabling analysts across the business in a meaningful way. It's a massive step forward for the future of data teams and one we're thrilled to continue to partner on."

The company's customer WHOOP, a health monitoring technology provider, noted the impact of these tools on analyst self-service. William Tsu, Senior Analytics Engineer at WHOOP, said, "As our data needs evolve, empowering analysts with seamless self-exploration becomes increasingly critical. By keeping them within the familiar dbt Catalog they already use daily, dbt's new analyst offerings enhance discoverability and enable faster, more intuitive, and governed self-service."

dbt Labs systems integrator InterWorks sees value in these changes for organisations seeking to scale analytics. James Wright, Chief Strategy Officer at InterWorks, commented, "dbt Canvas is unlocking a future where analysts can build confidently alongside engineers within the same trusted and governed workflows. We're excited about how this new development environment will help our customers unlock true self-service while maintaining the standards, security, and collaboration required to scale analytics responsibly."

New cost management tools have also been introduced, with a dashboard that offers organisations visibility over data warehouse expenditure, project-level consumption, and cost-saving opportunities achieved through standardisation on dbt. This cost management dashboard, powered by the dbt Fusion engine, is in preview for Snowflake users.

The dbt Fusion engine, recently developed following dbt Labs' acquisition of SDF Labs, underpins these updates. According to dbt Labs, Fusion delivers significantly improved performance, including data transformation speeds up to 30 times faster than previous iterations. The engine also introduces enhanced developer capabilities such as real-time code feedback and more efficient use of warehouse computing resources.