Skip to main content

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

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 ...

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

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 ...