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90% Report Reducing Engineering Toil to Scale Tools Puts Focus on Business Bottom Line

Stela Udovicic
Era Software

Modern IT and security organizations often need to manage petabytes of observability (logs, metrics, traces) data in real time. The adoption of cloud, modern application architectures, Kubernetes, and edge is behind this massive growth in observability data volumes. And for some organizations, log data volumes are approaching the exabyte range.

IT teams face many obstacles when managing massive amounts of observability data, from siloed tooling to prolonged incident resolution and security risks such as accidental exposure of personal identifiable information (PII) or credential data.

To shed light on key trends, challenges, and approaches these teams take to resolve those challenges, in Feb 2022, we ran a survey of professionals across various industries and roles within IT organizations. We are excited to share today the results of our 2022 State of Observability and Log Management report.

Over 315 IT executives, cloud application architects, DevOps, and site reliability engineers (SRE) took the survey, sharing perspectives on the current state of exploding data and the struggle to gather valuable insights from the data. These professionals are responsible for managing the availability of cloud application and infrastructure environments with at least 10 TB of log data, and their companies have at least 100 employees.

The survey results show that IT teams have difficulty with the massive growth of log data and use various methods to manage data volumes and their associated costs. These include only storing the most critical data to prematurely deleting log data. However, according to 78% of the respondents, attempts to manage volumes of log data have had mixed or unwanted results, such as increased incident response times or inability to access needed data.

Two-thirds of IT organizations require engineering time to manage their log management tools; larger organizations with more log data are more likely to have dedicated teams for tool management.
For the purposes of our survey, we defined observability as an evolution of traditional monitoring towards understanding deep insights from analyzing high volumes of log, metrics, and trace data, collected from a wide variety of modern applications and infrastructure environments.

Compared to similar research conducted in 2021, organizations report that observability adoption jumped by 180%. In addition, as organizations mature in implementing observability, the value of critical insights from their log data is more significant.

Participants also shared details about their current streaming data use. Streaming data connects, filters, processes, and routes log data between different observability tools (commercial or open source) or offline cold storage (S3, Google Cloud Services, etc.) and is sometimes called observability pipeline or observability data management. According to responses, streaming observability pipelines adoption is a work in progress, with 20% of organizations reporting full deployments while 36% are evaluating or considering options.

Report findings also reveal:

■ Observability log data is critically important for organizations. 83% of respondents report that business stakeholders outside of IT use insights from log data. In addition, 68% say log data is necessary, but it's tough to work with.

■ IT continues to struggle to keep up with data volumes. 78% work to reduce volumes and costs, but they miss needed data or troubleshooting, and security analyses are impacted.
■ Existing log management tools present challenges and risks related to scalability, 97% of respondents report.

■ Log data is key to observability, and innovation is needed. 79% of respondents believe the overall cost of observability data management, including log management activities, will skyrocket in 2022 if current practices and tools don't evolve.

■ Problems are beyond storing data. For example, 96% report the need also to use the data to solve business problems.

■ 90% report reducing engineering toil to scale tools helps IT focus on more important work.

■ Volumes of log data in organizations are exploding, according to 96% of IT professionals surveyed.

Survey Demographics: Roles include a third IT executives, a third enterprise (cloud or application) architects, and a third in DevOps/SRE/Ops roles. Companies are in the following regions: AMER (77%), followed by EMEA (20%) and APAC (3%), and include a variety of industry verticals, including financial, technology, healthcare, service, retail, manufacturing, etc.

Stela Udovicic is SVP, Marketing, at Era Software

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90% Report Reducing Engineering Toil to Scale Tools Puts Focus on Business Bottom Line

Stela Udovicic
Era Software

Modern IT and security organizations often need to manage petabytes of observability (logs, metrics, traces) data in real time. The adoption of cloud, modern application architectures, Kubernetes, and edge is behind this massive growth in observability data volumes. And for some organizations, log data volumes are approaching the exabyte range.

IT teams face many obstacles when managing massive amounts of observability data, from siloed tooling to prolonged incident resolution and security risks such as accidental exposure of personal identifiable information (PII) or credential data.

To shed light on key trends, challenges, and approaches these teams take to resolve those challenges, in Feb 2022, we ran a survey of professionals across various industries and roles within IT organizations. We are excited to share today the results of our 2022 State of Observability and Log Management report.

Over 315 IT executives, cloud application architects, DevOps, and site reliability engineers (SRE) took the survey, sharing perspectives on the current state of exploding data and the struggle to gather valuable insights from the data. These professionals are responsible for managing the availability of cloud application and infrastructure environments with at least 10 TB of log data, and their companies have at least 100 employees.

The survey results show that IT teams have difficulty with the massive growth of log data and use various methods to manage data volumes and their associated costs. These include only storing the most critical data to prematurely deleting log data. However, according to 78% of the respondents, attempts to manage volumes of log data have had mixed or unwanted results, such as increased incident response times or inability to access needed data.

Two-thirds of IT organizations require engineering time to manage their log management tools; larger organizations with more log data are more likely to have dedicated teams for tool management.
For the purposes of our survey, we defined observability as an evolution of traditional monitoring towards understanding deep insights from analyzing high volumes of log, metrics, and trace data, collected from a wide variety of modern applications and infrastructure environments.

Compared to similar research conducted in 2021, organizations report that observability adoption jumped by 180%. In addition, as organizations mature in implementing observability, the value of critical insights from their log data is more significant.

Participants also shared details about their current streaming data use. Streaming data connects, filters, processes, and routes log data between different observability tools (commercial or open source) or offline cold storage (S3, Google Cloud Services, etc.) and is sometimes called observability pipeline or observability data management. According to responses, streaming observability pipelines adoption is a work in progress, with 20% of organizations reporting full deployments while 36% are evaluating or considering options.

Report findings also reveal:

■ Observability log data is critically important for organizations. 83% of respondents report that business stakeholders outside of IT use insights from log data. In addition, 68% say log data is necessary, but it's tough to work with.

■ IT continues to struggle to keep up with data volumes. 78% work to reduce volumes and costs, but they miss needed data or troubleshooting, and security analyses are impacted.
■ Existing log management tools present challenges and risks related to scalability, 97% of respondents report.

■ Log data is key to observability, and innovation is needed. 79% of respondents believe the overall cost of observability data management, including log management activities, will skyrocket in 2022 if current practices and tools don't evolve.

■ Problems are beyond storing data. For example, 96% report the need also to use the data to solve business problems.

■ 90% report reducing engineering toil to scale tools helps IT focus on more important work.

■ Volumes of log data in organizations are exploding, according to 96% of IT professionals surveyed.

Survey Demographics: Roles include a third IT executives, a third enterprise (cloud or application) architects, and a third in DevOps/SRE/Ops roles. Companies are in the following regions: AMER (77%), followed by EMEA (20%) and APAC (3%), and include a variety of industry verticals, including financial, technology, healthcare, service, retail, manufacturing, etc.

Stela Udovicic is SVP, Marketing, at Era Software

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 ...