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Organizations Struggle to Observe Their Data

Tucker Callaway
Mezmo

Enterprises today are increasingly collecting massive amounts of data to help make better-informed business decisions as fast as possible. Faced with this unprecedented volume of data, their interest in observability is soaring. In fact, Gartner declared that observability is at the "peak of inflated expectations." Enterprises are starting to shift their focus from monitoring systems to discover issues to observing systems to understand why issues occur.


Although observability has become essential for many organizations, 74% of enterprises struggle to achieve it, according to a LogDNA survey of engineering professionals. And lack of investment in observability tools is not the problem. Two-thirds of respondents spend $100,000 or more annually and 38% spend $300,000 or more annually, with many using more than four different tools.

Enterprises wrestle with true observability because most observability data remains dark or unexploited. The scale, complexity, variety of data consumers, and runaway costs make it difficult for enterprises to get value from their machine data. There are other technical and organizational challenges, such as data and department silos, the complexity of managing data in cloud-native and hybrid cloud environments, and the inefficiency of single-pane-of-glass approaches to route data to appropriate destinations.

Let's take a look at three of the most pervasive pain points, according to the survey, holding enterprises back from observability nirvana:

Difficulty Using Current Tools

As enterprises strive to get more value from their observability data, particularly log data, which underpins all applications and systems, one of the biggest problems is that the tools are difficult to use. Many enterprises are dissatisfied, with more than half of respondents indicating that they would like to replace their tools. They cited issues with usability (66%) and challenges with routing security events (58%). Other problems include difficulty ingesting data into a standard format (32%) and routing it into multiple tools for different use cases (30%).

Hard to Collaborate Across Teams

More than 80% of enterprises indicate that multiple stakeholders need access to the same log data. On average, more than three teams require access to this data, including development, IT operations, site reliability engineering (SRE), and security. But the tools make it hard for multiple stakeholders to extract actionable insights, with 67% of respondents saying the barriers to collaboration across teams are a problem. As a result, companies are spending more time trying to resolve issues.

Controlling Costs

Log data is critical to tracking application performance and capacity resources, advising product improvements, and discovering threats and anomalous activity. However, organizations struggle to control costs as machine data skyrockets. To reduce costs, 57% limit the amount of log data they ingest or store, which hinders troubleshooting and debugging systems and applications. And 55% limit the amount of log data they route to their SIEM, which impedes incident response efforts and increases security risk.

For too long, enterprises made tough choices about how to use all of their machine data while managing costs. Despite most observability data being kept in the dark, organizations understand the value of this data, and 85% believe true observability is possible as new technology emerges to improve ease of use and facilitate stronger cross-team collaboration within budget. One approach to this is using an observability data pipeline to centralize observability data from multiple sources, enrich it, and send it to a variety of destinations. This level of flexibility ensures that everyone can use their tools of choice and avoid costly vendor lock-in. The right tool can also put controls in place to manage spikes so that everyone in an organization has access to the data they need in real time, without impacting the budget.

Tucker Callaway is CEO of Mezmo

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Organizations Struggle to Observe Their Data

Tucker Callaway
Mezmo

Enterprises today are increasingly collecting massive amounts of data to help make better-informed business decisions as fast as possible. Faced with this unprecedented volume of data, their interest in observability is soaring. In fact, Gartner declared that observability is at the "peak of inflated expectations." Enterprises are starting to shift their focus from monitoring systems to discover issues to observing systems to understand why issues occur.


Although observability has become essential for many organizations, 74% of enterprises struggle to achieve it, according to a LogDNA survey of engineering professionals. And lack of investment in observability tools is not the problem. Two-thirds of respondents spend $100,000 or more annually and 38% spend $300,000 or more annually, with many using more than four different tools.

Enterprises wrestle with true observability because most observability data remains dark or unexploited. The scale, complexity, variety of data consumers, and runaway costs make it difficult for enterprises to get value from their machine data. There are other technical and organizational challenges, such as data and department silos, the complexity of managing data in cloud-native and hybrid cloud environments, and the inefficiency of single-pane-of-glass approaches to route data to appropriate destinations.

Let's take a look at three of the most pervasive pain points, according to the survey, holding enterprises back from observability nirvana:

Difficulty Using Current Tools

As enterprises strive to get more value from their observability data, particularly log data, which underpins all applications and systems, one of the biggest problems is that the tools are difficult to use. Many enterprises are dissatisfied, with more than half of respondents indicating that they would like to replace their tools. They cited issues with usability (66%) and challenges with routing security events (58%). Other problems include difficulty ingesting data into a standard format (32%) and routing it into multiple tools for different use cases (30%).

Hard to Collaborate Across Teams

More than 80% of enterprises indicate that multiple stakeholders need access to the same log data. On average, more than three teams require access to this data, including development, IT operations, site reliability engineering (SRE), and security. But the tools make it hard for multiple stakeholders to extract actionable insights, with 67% of respondents saying the barriers to collaboration across teams are a problem. As a result, companies are spending more time trying to resolve issues.

Controlling Costs

Log data is critical to tracking application performance and capacity resources, advising product improvements, and discovering threats and anomalous activity. However, organizations struggle to control costs as machine data skyrockets. To reduce costs, 57% limit the amount of log data they ingest or store, which hinders troubleshooting and debugging systems and applications. And 55% limit the amount of log data they route to their SIEM, which impedes incident response efforts and increases security risk.

For too long, enterprises made tough choices about how to use all of their machine data while managing costs. Despite most observability data being kept in the dark, organizations understand the value of this data, and 85% believe true observability is possible as new technology emerges to improve ease of use and facilitate stronger cross-team collaboration within budget. One approach to this is using an observability data pipeline to centralize observability data from multiple sources, enrich it, and send it to a variety of destinations. This level of flexibility ensures that everyone can use their tools of choice and avoid costly vendor lock-in. The right tool can also put controls in place to manage spikes so that everyone in an organization has access to the data they need in real time, without impacting the budget.

Tucker Callaway is CEO of Mezmo

Hot Topics

The Latest

A new wave of tariffs, some exceeding 100%, is sending shockwaves across the technology industry. Enterprises are grappling with sudden, dramatic cost increases that threaten to disrupt carefully planned budgets, sourcing strategies, and deployment plans. For CIOs and CTOs, this isn't just an economic setback; it's a wake-up call. The era of predictable cloud pricing and stable global supply chains is over ...

As artificial intelligence (AI) adoption gains momentum, network readiness is emerging as a critical success factor. AI workloads generate unpredictable bursts of traffic, demanding high-speed connectivity that is low latency and lossless. AI adoption will require upgrades and optimizations in data center networks and wide-area networks (WANs). This is prompting enterprise IT teams to rethink, re-architect, and upgrade their data center and WANs to support AI-driven operations ...

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...