DataOps Is Maturing

Learn more about Unravel Data

DataOps as a practice is maturing, according to a survey from Unravel Data.

This year, more than 44% of respondents reported they are actively employing DataOps methodologies, compared to just less than a quarter (21%) of respondents in 2022, representing a 110% increase from the year prior.

Further demonstrating the maturing DataOps practice, only 20% of respondents in this year's survey said they were at the beginning stage compared to 41% last year.


Jump to infographic below

Additional survey findings include:

Cloud spending is now a critical KPI for majority of data teams

More than two-thirds of data teams surveyed said that cloud spending has become a KPI of high strategic importance. When responses were broken down by role, almost 80% of business stakeholders said cloud spending was a critical KPI while just over half (55%) of data practitioners indicated the same.

Cloud resources are underutilized

In addition to cloud spending being elevated as a top KPI, almost half (44%) of all respondents in this year's survey also believe they are leaving money on the table when it comes to their public cloud utilization.

Almost a quarter of respondents (23%) said they were unable to even estimate what percentage of their cloud resources went unused.

FinOps interest is high yet adoption lags

Despite the fact that data teams have reported a lack of visibility into cloud spending, the adoption of mature FinOps practice was not viewed as an immediate priority among respondents with just over 20% reporting that their data teams have an established FinOps practice, while a third of data teams are still in the early planning phase of implementing FinOps.

Data reliability emerges as the top challenge

This year when participants were asked what they viewed as the top challenge with operating their data stack, 41% of respondents cited the lack of data quality as their most significant obstacle while 35% noted that the lack of visibility across their environments was the second biggest obstacle to managing their data stack.


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DataOps Is Maturing

Learn more about Unravel Data

DataOps as a practice is maturing, according to a survey from Unravel Data.

This year, more than 44% of respondents reported they are actively employing DataOps methodologies, compared to just less than a quarter (21%) of respondents in 2022, representing a 110% increase from the year prior.

Further demonstrating the maturing DataOps practice, only 20% of respondents in this year's survey said they were at the beginning stage compared to 41% last year.


Jump to infographic below

Additional survey findings include:

Cloud spending is now a critical KPI for majority of data teams

More than two-thirds of data teams surveyed said that cloud spending has become a KPI of high strategic importance. When responses were broken down by role, almost 80% of business stakeholders said cloud spending was a critical KPI while just over half (55%) of data practitioners indicated the same.

Cloud resources are underutilized

In addition to cloud spending being elevated as a top KPI, almost half (44%) of all respondents in this year's survey also believe they are leaving money on the table when it comes to their public cloud utilization.

Almost a quarter of respondents (23%) said they were unable to even estimate what percentage of their cloud resources went unused.

FinOps interest is high yet adoption lags

Despite the fact that data teams have reported a lack of visibility into cloud spending, the adoption of mature FinOps practice was not viewed as an immediate priority among respondents with just over 20% reporting that their data teams have an established FinOps practice, while a third of data teams are still in the early planning phase of implementing FinOps.

Data reliability emerges as the top challenge

This year when participants were asked what they viewed as the top challenge with operating their data stack, 41% of respondents cited the lack of data quality as their most significant obstacle while 35% noted that the lack of visibility across their environments was the second biggest obstacle to managing their data stack.


Related Links

Hot Topics

The Latest

OpenTelemetry: A Complete Telemetry Framework that Grows with You

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

The Quiet Power of Milliseconds in High Traffic Systems

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

Are AI "Autonomous Coworkers" Ready for Payroll?

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI Reaches Pivotal Inflection Point: From Automation to Agentic Intelligence

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The Real Reason Enterprise AI Stalls: It Doesn't Fit in the Box

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

AI's Inflection Point Will Redefine Enterprise Readiness in 2026

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Turning Foresight into Resilience: Reclaiming Prevention in the Age of Exposure

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

MLOps Meets DataOps: Creating Unified Backbone for AI

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

2026 Network Monitoring Trends

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

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APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...