Observability Maturity Brings Higher Productivity, Code Quality and End-User Satisfaction

Learn more about Honeycomb

More than half (61%) of respondents reported that their teams are practicing observability, an 8% increase from 2020, signaling that overall adoption is on the rise, according to a 2021 survey from Honeycomb with over 400 responses from across multiple industries and organization sizes.

However, the majority of respondents indicated their teams are at the earliest stages of observability maturity.

Key findings include:

Observability is gaining traction

61% of respondents reported that their teams are currently practicing observability, an increase of 8% from last year. That increase is sharply reflected across individual teams (up 7%) as opposed to entire organizations (up only 1%).

Mature teams realize more benefits

Teams on the higher end of the maturity spectrum realize more benefits than their less-mature counterparts. Teams that are mature in their observability practice realize even more impactful business outcomes, including deploying more frequently, being able to find bugs more quickly before and after pushing to production, and reduced burnout.

Mature teams deliver higher customer satisfaction

More-mature teams are also 3X more likely to deliver higher customer satisfaction. Teams that have achieved Intermediate or Advanced-level maturity reported their end-user customers are "Always Satisfied" with their service quality and capability at a rate of three times more than teams that do not practice observability.

Lack of implementation skills is a barrier

Lack of implementation skills is a disproportionate barrier for observability adoption. While interest in observability has gained significant momentum, organizations at the earliest stages of observability maturity report lack of implementation skills as the second-largest hurdle to observability adoption, indicating a need for more training options. All respondents indicated their primary hurdle was competing with other initiatives.

The Honeycomb maturity model outlines a progression of five distinct stages ranging from "Planning" or "Novice" (with limited observability capability and processes) to "Advanced" (with comprehensive processes). The highlights of this year's report indicate that:

■ 10% of those surveyed reported a combination of practices and tooling that reflect a highly observable system in the "Advanced" and "Intermediate" groups. These two groups highly prioritize observability: 50% practice observability across the organization and 43% on a team-by-team basis. Respondents also reported high public cloud use, and most work at large enterprises (57%) and in the tech industry (46%).

■ 37% of survey respondents fall into the "Novice" group. This group is more likely to self-report that they are practicing observability because they are using tools like logs, metrics, and traces. However, they also do not report having the key capabilities associated with practicing observability, such as having a comprehensive understanding of their systems, which suggests that respondents in this group may be focusing on the data needed for observability but not yet fully adopting the tools or practices of observability.

■ One in four teams are at the "Planning" stage or the very beginning of their observability journey and are starting to practice on a team-by-team basis. In this group, approximately one in five respondents do not currently practice or use observability tooling but have plans to do so within the next year.

The research verifies that teams on the higher end of the maturity spectrum are more likely to have:

■ Code that is well understood, well maintained, and fewer bugs than average.

■ The ability to follow predictable release cycles because they confidently address issues that arise.

■ Understanding of the end-to-end performance of their systems and how technical debt is costing their organization.

■ The ability to visualize context-rich events that allow efficient, focused, and actionable on-call processes.

■ The ability to prioritize responsiveness to user behavior and feedback.

■ Completely automated or mostly automated releases, resulting in reduced toil.

■ The ability to set and measure service level objectives, resulting in better alignment between engineering and business goals.

"This year, we're seeing that teams focused on building up their observability capabilities are identifying problems faster and producing better business outcomes," said Christine Yen, CEO and co-founder of Honeycomb. "Our observability maturity model can be used as a roadmap for anyone to see how organizations across the industry are approaching a fundamentally new way of understanding their production services. Teams can understand what's working, what's not, and how early investments in observability adoption are creating meaningful business impacts, so that they can achieve similar results."

Methodology: The 2021 Observability Maturity Community Research Findings study was conducted by ClearPath Strategies, an independent strategic consulting and public opinion research firm.

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Observability Maturity Brings Higher Productivity, Code Quality and End-User Satisfaction

Learn more about Honeycomb

More than half (61%) of respondents reported that their teams are practicing observability, an 8% increase from 2020, signaling that overall adoption is on the rise, according to a 2021 survey from Honeycomb with over 400 responses from across multiple industries and organization sizes.

However, the majority of respondents indicated their teams are at the earliest stages of observability maturity.

Key findings include:

Observability is gaining traction

61% of respondents reported that their teams are currently practicing observability, an increase of 8% from last year. That increase is sharply reflected across individual teams (up 7%) as opposed to entire organizations (up only 1%).

Mature teams realize more benefits

Teams on the higher end of the maturity spectrum realize more benefits than their less-mature counterparts. Teams that are mature in their observability practice realize even more impactful business outcomes, including deploying more frequently, being able to find bugs more quickly before and after pushing to production, and reduced burnout.

Mature teams deliver higher customer satisfaction

More-mature teams are also 3X more likely to deliver higher customer satisfaction. Teams that have achieved Intermediate or Advanced-level maturity reported their end-user customers are "Always Satisfied" with their service quality and capability at a rate of three times more than teams that do not practice observability.

Lack of implementation skills is a barrier

Lack of implementation skills is a disproportionate barrier for observability adoption. While interest in observability has gained significant momentum, organizations at the earliest stages of observability maturity report lack of implementation skills as the second-largest hurdle to observability adoption, indicating a need for more training options. All respondents indicated their primary hurdle was competing with other initiatives.

The Honeycomb maturity model outlines a progression of five distinct stages ranging from "Planning" or "Novice" (with limited observability capability and processes) to "Advanced" (with comprehensive processes). The highlights of this year's report indicate that:

■ 10% of those surveyed reported a combination of practices and tooling that reflect a highly observable system in the "Advanced" and "Intermediate" groups. These two groups highly prioritize observability: 50% practice observability across the organization and 43% on a team-by-team basis. Respondents also reported high public cloud use, and most work at large enterprises (57%) and in the tech industry (46%).

■ 37% of survey respondents fall into the "Novice" group. This group is more likely to self-report that they are practicing observability because they are using tools like logs, metrics, and traces. However, they also do not report having the key capabilities associated with practicing observability, such as having a comprehensive understanding of their systems, which suggests that respondents in this group may be focusing on the data needed for observability but not yet fully adopting the tools or practices of observability.

■ One in four teams are at the "Planning" stage or the very beginning of their observability journey and are starting to practice on a team-by-team basis. In this group, approximately one in five respondents do not currently practice or use observability tooling but have plans to do so within the next year.

The research verifies that teams on the higher end of the maturity spectrum are more likely to have:

■ Code that is well understood, well maintained, and fewer bugs than average.

■ The ability to follow predictable release cycles because they confidently address issues that arise.

■ Understanding of the end-to-end performance of their systems and how technical debt is costing their organization.

■ The ability to visualize context-rich events that allow efficient, focused, and actionable on-call processes.

■ The ability to prioritize responsiveness to user behavior and feedback.

■ Completely automated or mostly automated releases, resulting in reduced toil.

■ The ability to set and measure service level objectives, resulting in better alignment between engineering and business goals.

"This year, we're seeing that teams focused on building up their observability capabilities are identifying problems faster and producing better business outcomes," said Christine Yen, CEO and co-founder of Honeycomb. "Our observability maturity model can be used as a roadmap for anyone to see how organizations across the industry are approaching a fundamentally new way of understanding their production services. Teams can understand what's working, what's not, and how early investments in observability adoption are creating meaningful business impacts, so that they can achieve similar results."

Methodology: The 2021 Observability Maturity Community Research Findings study was conducted by ClearPath Strategies, an independent strategic consulting and public opinion research firm.

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How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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