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DataOps Is Maturing

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

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.


Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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 ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...