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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Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

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