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Majority of Organizations Cannot Realize Full Potential of Observability

Although 78% of organizations surveyed have an observability practice in place, 91% said they face challenges that prevent them from realizing the full potential of the systems they have already deployed, according to Observability and Demystifying AIOps, a report from Chronosphere and the Enterprise Strategy Group (ESG).

Scalability and reliability of the observability tools were cited as the top concerns.

Additional survey findings include:

Observability tool sprawl is expanding

A majority of organizations reported at least 6 different tools in use, with more than half (52%) using 11-20 different tools. The report also shows that 72% of organizations agree that the number of tools they use adds complexity.

Explosive observability data growth

Additionally, 69% of survey respondents reported that the most costly line item for most observability solutions, data storage, is growing. Respondents stated that "the amount of observability data is growing at a concerning rate" and one in five respondents reported that this explosive data growth was their top concern.

To address data growth, organizations are taking a mix of steps to rein in costs, including:

■ increasing storage spend (52%)

■ limiting the number of observed applications in their environment (44%)

■ limiting the number of observed metrics per application (43%).

Cloud is greatest Observability challenge

Many respondents also noted that applications deployed in the cloud were harder for them to monitor and few of them felt that their observability solution was helping them meet their availability goals.

When asked about their biggest observability challenges, 60% agreed that "Lack of visibility into our cloud applications makes achieving SLAs a challenge," and only 20% chose "Improved SLA Performance" as one of their monitoring/observability strategy's most impactful benefits.

Methodology: TechTarget’s Enterprise Strategy Group (ESG) surveyed 374 IT (58%) and DevOps/AppDev (42%) professionals responsible for evaluating, purchasing, managing, and using observability at large midmarket (500 to 999 employees) (11%) and enterprise (1,000+ employees) (89%) organizations in North America (US and Canada).

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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 ...

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Majority of Organizations Cannot Realize Full Potential of Observability

Although 78% of organizations surveyed have an observability practice in place, 91% said they face challenges that prevent them from realizing the full potential of the systems they have already deployed, according to Observability and Demystifying AIOps, a report from Chronosphere and the Enterprise Strategy Group (ESG).

Scalability and reliability of the observability tools were cited as the top concerns.

Additional survey findings include:

Observability tool sprawl is expanding

A majority of organizations reported at least 6 different tools in use, with more than half (52%) using 11-20 different tools. The report also shows that 72% of organizations agree that the number of tools they use adds complexity.

Explosive observability data growth

Additionally, 69% of survey respondents reported that the most costly line item for most observability solutions, data storage, is growing. Respondents stated that "the amount of observability data is growing at a concerning rate" and one in five respondents reported that this explosive data growth was their top concern.

To address data growth, organizations are taking a mix of steps to rein in costs, including:

■ increasing storage spend (52%)

■ limiting the number of observed applications in their environment (44%)

■ limiting the number of observed metrics per application (43%).

Cloud is greatest Observability challenge

Many respondents also noted that applications deployed in the cloud were harder for them to monitor and few of them felt that their observability solution was helping them meet their availability goals.

When asked about their biggest observability challenges, 60% agreed that "Lack of visibility into our cloud applications makes achieving SLAs a challenge," and only 20% chose "Improved SLA Performance" as one of their monitoring/observability strategy's most impactful benefits.

Methodology: TechTarget’s Enterprise Strategy Group (ESG) surveyed 374 IT (58%) and DevOps/AppDev (42%) professionals responsible for evaluating, purchasing, managing, and using observability at large midmarket (500 to 999 employees) (11%) and enterprise (1,000+ employees) (89%) organizations in North America (US and Canada).

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 ...