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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

Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

APMdigest's Predictions Series continues with 2026 Data Center Predictions — industry experts offer predictions on how data centers will evolve and impact business in 2026 ...

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026. Part 2 covers data and data platforms ...