AI Drives Surge in Data Budgets

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs.

Data Budgets Spike

According to the report, data budgets are growing significantly this year with 30% of participants reporting budget growth compared to just 9% last year.

Additionally, AI tooling was recognized as the largest area of investment for the year ahead with 45% of respondents citing it as a key priority.

Data team sizes are increasing too (40% reported growth, compared to 14% last year), clearly demonstrating how AI is driving demand for larger teams that can better ensure high quality, governed data.

Additionally, as data budgets increase, so have salaries in North America, with 80% of individual contributors making over $100,000 (compared to 69% last year) and 49% of managers making over $200,000 (compared to 32% last year).

Data Teams Increase AI Usage

AI isn't just fueling investment, it's also disrupting the way data professionals operate. The report found that 80% of respondents are using AI in their daily workflow, compared to just 30% last year. Of those using AI daily, 70% said they use AI for code development and 50% use it for documentation.

Organizations are clearly increasing investments in tools to accelerate — not replace — their data teams, and leveraging AI in the data workflow is improving developer productivity while bolstering data quality in the process. As a result, company perception of data teams is positive, with respondents overwhelmingly agreeing (75%) that their organization values the data team.

"AI is disrupting the way that teams work with organizational data," said Mark Porter, CTO of dbt Labs. "As companies increase AI investments, leaders are prioritizing the teams responsible for data quality and governance — the essential foundation for AI effectiveness. At the same time, data engineers are turning to AI to automate routine tasks, completely changing how data is delivered to the business. Because of this, the strategic role of the data team continues to grow, with AI as the catalyst. It's a symbiotic relationship — data professionals make AI better, and AI makes data teams better."

Looking to the Future

Effective AI requires high quality inputs, and poor data quality continues to be the challenge most frequently reported (56% of respondents). That's why building trust in data is cited as the top priority for growing data teams, accentuating the importance of data governance and observability — and data professionals are hopeful that AI can help. Data teams are optimistic about the potential impact of AI in the analytics workflow, citing its ability to help bridge the data quality gap with features like proactive data monitoring and pipeline debugging.

Methodology: dbt Labs surveyed 459 data practitioners and leaders from October 8, 2024 through December 27, 2024. 70% of survey respondents are individual contributors (ICs) and 30% are managers. Analytics engineers made up 48% of IC respondents, 36% of IC respondents are data engineers, and 16% of IC respondents are data analysts.

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AI Drives Surge in Data Budgets

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs.

Data Budgets Spike

According to the report, data budgets are growing significantly this year with 30% of participants reporting budget growth compared to just 9% last year.

Additionally, AI tooling was recognized as the largest area of investment for the year ahead with 45% of respondents citing it as a key priority.

Data team sizes are increasing too (40% reported growth, compared to 14% last year), clearly demonstrating how AI is driving demand for larger teams that can better ensure high quality, governed data.

Additionally, as data budgets increase, so have salaries in North America, with 80% of individual contributors making over $100,000 (compared to 69% last year) and 49% of managers making over $200,000 (compared to 32% last year).

Data Teams Increase AI Usage

AI isn't just fueling investment, it's also disrupting the way data professionals operate. The report found that 80% of respondents are using AI in their daily workflow, compared to just 30% last year. Of those using AI daily, 70% said they use AI for code development and 50% use it for documentation.

Organizations are clearly increasing investments in tools to accelerate — not replace — their data teams, and leveraging AI in the data workflow is improving developer productivity while bolstering data quality in the process. As a result, company perception of data teams is positive, with respondents overwhelmingly agreeing (75%) that their organization values the data team.

"AI is disrupting the way that teams work with organizational data," said Mark Porter, CTO of dbt Labs. "As companies increase AI investments, leaders are prioritizing the teams responsible for data quality and governance — the essential foundation for AI effectiveness. At the same time, data engineers are turning to AI to automate routine tasks, completely changing how data is delivered to the business. Because of this, the strategic role of the data team continues to grow, with AI as the catalyst. It's a symbiotic relationship — data professionals make AI better, and AI makes data teams better."

Looking to the Future

Effective AI requires high quality inputs, and poor data quality continues to be the challenge most frequently reported (56% of respondents). That's why building trust in data is cited as the top priority for growing data teams, accentuating the importance of data governance and observability — and data professionals are hopeful that AI can help. Data teams are optimistic about the potential impact of AI in the analytics workflow, citing its ability to help bridge the data quality gap with features like proactive data monitoring and pipeline debugging.

Methodology: dbt Labs surveyed 459 data practitioners and leaders from October 8, 2024 through December 27, 2024. 70% of survey respondents are individual contributors (ICs) and 30% are managers. Analytics engineers made up 48% of IC respondents, 36% of IC respondents are data engineers, and 16% of IC respondents are data analysts.

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Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

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