The 2026 State of Analytics Engineering Report reveals data leaders' concerns over data quality amid rising need for reliability
Key findings:
- 72% of respondents now prioritize AI-assisted coding in their development workflows, while only 24% prioritize AI-assisted pipeline management, including testing and observability; this highlights an imbalance between acceleration and quality
- Trust in data and data teams as an organizational priority surged from 66% to 83% year over year, the steepest single-year increase of any measured objective; speed followed, climbing from 50% to 71%
- 71% of data professionals cite incorrect or hallucinated outputs reaching stakeholders as a top concern, which carries greater consequence as autonomous agents operate on top of organizational data at scale
- Data infrastructure costs are outpacing budget growth, with 57% reporting increased warehouse and compute spend, compared to just 36% reporting increased team budgets
PHILADELPHIA, April 14, 2026 /PRNewswire/ -- dbt Labs, a leader in standards for AI-ready structured data, today released its fourth annual State of Analytics Engineering Report, revealing a growing gap between the speed at which AI is transforming data work and the systems designed to ensure its reliability.
As AI becomes embedded in analytics workflows, organizations are producing data faster than ever, but governance, validation, and trust mechanisms are not keeping pace. As a result, trust in data has emerged as the most widely prioritized organizational objective, rising to 83% year over year. In this environment, organizations that invest in governance, validation, and data quality as strategic priorities are best positioned to scale AI-driven outcomes reliably and turn acceleration into sustainable impact.
AI moves from experimental to embedded
According to the survey, AI is scaling across two key areas of analytics engineering: AI-assisted coding that increases productivity and AI-generated, stakeholder-facing insights. The majority (72%) of respondents now prioritize AI-assisted coding in their development workflows, and 77% of leaders report pushing teams to improve productivity with AI.
"Two years ago, most analytics practitioners and leaders didn't expect to be generating the majority of their analytics code with AI. But today, that's where we are," said Jason Ganz, dbt Labs Director, Community, Developer Experience and AI. "This signals a fundamental shift in the role of data practitioners, away from manually creating code and toward building the systems that enable agentic data workflows at scale, while providing the trusted infrastructure those agents need to operate reliably. Organizations that treat governance as infrastructure, not an afterthought, are the ones that will make the most of what AI can do."
Trust and governance as key enablers of AI at scale
Even though technical integration challenges have declined (from 35% to 27% year over year), governance issues like ambiguous data ownership (41%) and poor data quality remain persistent obstacles. Nearly three-quarters (71%) of data professionals are concerned about incorrect data reaching stakeholders.
In parallel, trust and speed have emerged as the dominant priorities among respondents, clearly separating from cost reduction. The importance placed on increasing trust in data rose sharply from 66% in 2025 to 83% in 2026, while the priority of "shipping data products faster" climbed from 50% to 71%. An emphasis on cost reduction, however, increased by only 5% (from 48% to 53%).
"There's a real tension between moving fast and building trust, and you can't optimize for both without intention," said Pooja Crahen, senior manager of analytics engineering at Okta. "That's where discipline in modeling, validation, and ownership becomes a requirement, not a best practice."
On April 29, a panel of industry experts from Hex, Ramp and dbt Labs will host the 2026 State of Analytics Engineering Virtual Event. The conversation will focus on the report findings, what the year over year changes signal, and how trust isn't a constraint on AI-driven impact but the determining factor in how far it can scale. Register here: https://www.getdbt.com/resources/webinars/2026-state-of-analytics-engineering-virtual-event
Download the 2026 State of Analytics Engineering report at https://www.getdbt.com/resources/state-of-analytics-engineering-2026.
Methodology
dbt Labs collected survey responses in late 2025 and early 2026 from 363 data practitioners and leaders across industries and regions. Of the respondents, 73% identified as practitioners, and 27% as managers or executives overseeing data teams.
About dbt Labs
Since 2016, dbt Labs has been on a mission to help data practitioners create and disseminate organizational knowledge. dbt is the standard for AI-ready structured data. Powered by the dbt Fusion engine, it unlocks the performance, context, and trust that organizations need to scale analytics in the era of AI. Globally, more than 80,000 data teams use dbt, including those at Siemens, Roche, and Condé Nast.
Learn more at getdbt.com, and follow dbt Labs on LinkedIn, X, Instagram, and YouTube.
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