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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

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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