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Analytics engineering emerged alongside probably the most vital technological revolution of the previous decade – the rise of cloud computing. In the present day, we’re experiencing even a larger transformation, one fueled by the meteoric rise of synthetic intelligence (AI), which is reshaping how analytics engineers strategy knowledge challenges
AI has rapidly develop into an integral a part of the day by day workflows for 80% of knowledge professionals, up from 30% final yr. It’s additionally altering how knowledge groups work, with 70% of execs now utilizing AI to help with code growth, and 40% reporting that their knowledge groups are rising. Whereas funding in AI instruments is main the way in which, knowledge high quality stays a persistent problem, with over 56% of practitioners highlighting it as a key concern.
These insights come from dbt Labs’ 2025 State of Analytics Engineering Report, the third version of their annual publication, which dives into how AI is redefining knowledge groups, the place budgets are being prioritized, and why constructing belief in knowledge is extra important than ever.
A key discovering of the report is that AI is augmenting, not changing knowledge groups, as many had anticipated it to. As an alternative of changing human experience, AI adoption is altering how folks work. It’s permitting professionals to spend much less time on redundant duties and focus extra on specialised work. The report highlights that greater than two-thirds (70%) of respondents use AI for analytics growth in some type.
The rising investments in AI instruments to help knowledge groups foster a extra constructive notion of their contributions. In consequence, 75% of respondents agree that their organizations extremely worth and belief their knowledge groups.
“AI is disrupting the way in which that groups work with organizational knowledge,” mentioned Mark Porter, CTO of dbt Labs. “As corporations enhance AI investments, leaders are prioritizing the groups accountable for knowledge high quality and governance—the important basis for AI effectiveness.”
“On the similar time, knowledge engineers are turning to AI to automate routine duties, fully altering how knowledge is delivered to the enterprise. Due to this, the strategic function of the information workforce continues to develop, with AI because the catalyst. It’s a symbiotic relationship – knowledge professionals make AI higher, and AI makes knowledge groups higher.”
Analytics engineering is rising past tech, with extremely regulated industries like finance (15%) and healthcare (10%) adopting it to handle complicated, compliance-heavy knowledge. Tech stays the biggest sector at 34%, although its share has declined by 3% this yr.
In line with dbt Labs, organizations are investing in knowledge once more after a cautious interval. AI instruments are the highest precedence, with 45% planning to spend extra on them within the subsequent yr. Knowledge high quality and observability come subsequent, with 38% specializing in fixing key knowledge challenges.
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AI instruments lead funding priorities, with 45% of respondents planning to spice up spending on this space over the subsequent yr. Knowledge high quality and observability observe, with 38% aiming to extend funding to sort out pressing knowledge high quality challenges. A number of different studies have highlighted the pressing want for organizations to sort out knowledge high quality points, and this was a recurring theme all through this yr’s dbt Labs report.
The highest use circumstances for AI embrace code growth (70%), adopted by documentation (50%), and answering knowledge questions with SQL era (22%). The report reveals that knowledge groups are counting on general-purpose LLMs comparable to OpenAI’s ChatGPT and Gemini.
Nonetheless, as a result of these instruments aren’t tailor-made for particular analytics duties, organizations are more and more adopting specialised GenAI brokers. At the moment, 25% of respondents are utilizing AI options constructed into their growth tooling
The report’s findings additionally reveal that curiosity in semantic layers, instruments that make knowledge clearer and extra structured, can also be rising, with 27% planning to take a position extra on this space. There may be additionally a larger concentrate on empowering nontechnical customers to work with reworked, ruled datasets, which might enhance knowledge effectivity – a key focus for analytics engineering.
There’s a rising push to empower nontechnical customers. Practically 65% of respondents consider that enabling enterprise stakeholders to create and work with reworked and ruled datasets would considerably enhance organizational knowledge effectivity. Nonetheless, this highlights a core problem in analytics engineering: sustaining knowledge integrity whereas guaranteeing broader accessibility.
dbt Labs CEO Tristan Helpful
On the dbt Cloud Launch Showcase occasion in Might 2024, dbt Labs CEO Tristan Helpful, highlighted the influence of AI for knowledge professionals. He mentioned, “And whereas this cloud transition remains to be taking part in out, AI goes to be the subsequent large change in our lives as knowledge professionals. The adjustments we’ll see over the approaching years can be simply as dramatic as these we’ve seen play out over the previous decade.”
dbt Labs makes a speciality of analytics engineering and is well-positioned to supply insights into the evolving subject. This yr’s report is predicated on a survey of 459 knowledge professionals, together with particular person contributors (70%) and managers (30%). Among the many particular person contributors, 48% have been analytics engineers, 36% have been knowledge engineers, and 16% have been knowledge analysts.
Later this month, dbt Labs will host the 2025 State of Analytics Engineering Digital Occasion. The occasion’s agenda will embrace discussions on the report’s key findings, together with broader methods for constructing efficient knowledge organizations, integrating GenAI, and addressing ongoing business challenges.
