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10 Key Takeaways from the 2022 Observability Forecast

Ishan Mukherjee
New Relic

Earlier this year, New Relic conducted an extensive survey of IT practitioners and decision-makers to understand the current state of observability: the ability to measure how a system is performing and identify issues and errors based on its external outputs. The company surveyed 1,614 IT professionals across 14 countries in North America, Europe and the Asia Pacific region. The findings of the 2022 Observability Forecast offer a detailed view of how this practice is shaping engineering and the technologies of the future.


Here are 10 key takeaways from the forecast:

1. Observability improves service-level metrics. Organizations see its value and plan to invest more

Respondents to the New Relic survey plan aggressive observability deployments, with 72% planning to maintain or increase their observability budgets over the next year. More than half expected their budgets to increase, while 20% expect to maintain current spending levels.

2. Most organizations will have robust observability practices in place by 2025

The 2022 Observability Forecast identified 17 observability capabilities that comprise a mature practice. Nearly all respondents expected to deploy key capabilities like network monitoring, security monitoring and log management by 2025. The majority expected to have 88–97% of the 17 capabilities deployed, but just 3% of respondents already maintain all 17 capabilities today.

3. Observability is now a board-level imperative

Tech executives have recognized the value and importance of observability: 73% of respondents reported that their C-suite executives are supporters of observability, and 78% saw observability as a key enabler for achieving core business goals. Furthermore, of those who had mature observability practices by the report's definition, 100% indicated that observability improves revenue retention by deepening their understanding of customer behaviors.

4. For many organizations, large sections of tech stacks are still not being fully observed or monitored

Despite the overall enthusiasm for observability and the fact that most organizations are practicing some form of observability, only 27% of respondents' organizations have achieved full-stack observability as defined in the report. The overall lack of adoption of full-stack observability signals that many organizations have an opportunity to make rapid improvements to their observability practices over the next year.

5. Organizations must tackle fragmentation of data, tools and teams

Many organizations use a patchwork of tools to monitor their technology stacks, requiring extensive manual effort only to gain a fragmented view of IT systems. More than 80% of respondents used four or more observability tools, and a third of respondents had to detect outages manually or from complaints. Just 7% of respondents said their telemetry data is unified in one place, and only 5% had a mature observability practice. Recognizing the challenges of fragmentation, respondents reported the need for simplicity, integration, seamlessness, and more efficient ways to complete high-value projects.

6. Telemetry data is often siloed

Siloed and fragmented data lead to a painful user experience, but slightly more than half (51%) of respondents still have siloed data in their tech stacks. Of those who have entirely siloed data, 77% stated they would prefer a single, consolidated platform. Those who struggle the most to juggle data across different silos long for more simplicity in their observability solutions.

7. There is a strong correlation between full-stack observability and faster mean time to detection (MTTD)

Respondents from organizations that have achieved full-stack observability, as well as those who have already prioritized full-stack observability, were more likely than others to experience the fastest mean time to detect an outage — less than five minutes. The data supports a strong correlation between achieving or prioritizing full-stack observability and a range of performance benefits, including fewer outages, improved outage detection rates, and improved resolution.

8. One of the biggest roadblocks to achieving observability is a failure to understand the benefits

The 2022 Observability Forecast asked respondents to name the biggest challenges preventing full-stack observability. The top responses were a lack of understanding of the benefits of observability and the belief that current IT performance is adequate (28% for each). Other leading roadblocks were a lack of budget (27%) and too many monitoring tools (25%).

9. Despite that, IT professionals recognize the bottom-line benefits of observability

Survey respondents named a wide range of observability benefits. These include improved uptime and reliability (cited by 36% of respondents), increased operational efficiency (35%), proactive detection of issues (33%) and an improved customer experience (33%). Respondents also said that observability improves the lives of engineers and developers, with 34% saying it helped to increase productivity and 32% crediting observability for supporting cross-team collaboration.

10. Organizations expect to need observability for AI, IoT and key business applications

C-suite executives see observability playing a major role in the development of new technologies. More than half of respondents said they would need observability most for artificial intelligence (AI) applications, while 48% mentioned the Internet of Things, 38% cited edge computing, and 36% highlighted blockchain applications. Observability in AI was mentioned across industries, with a majority of respondents in services/consulting (62%), energy/utilities (60%), government (58%) and IT/telco (51%) mentioning the need for observability in their AI projects.

Ishan Mukherjee is SVP of Growth at New Relic

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10 Key Takeaways from the 2022 Observability Forecast

Ishan Mukherjee
New Relic

Earlier this year, New Relic conducted an extensive survey of IT practitioners and decision-makers to understand the current state of observability: the ability to measure how a system is performing and identify issues and errors based on its external outputs. The company surveyed 1,614 IT professionals across 14 countries in North America, Europe and the Asia Pacific region. The findings of the 2022 Observability Forecast offer a detailed view of how this practice is shaping engineering and the technologies of the future.


Here are 10 key takeaways from the forecast:

1. Observability improves service-level metrics. Organizations see its value and plan to invest more

Respondents to the New Relic survey plan aggressive observability deployments, with 72% planning to maintain or increase their observability budgets over the next year. More than half expected their budgets to increase, while 20% expect to maintain current spending levels.

2. Most organizations will have robust observability practices in place by 2025

The 2022 Observability Forecast identified 17 observability capabilities that comprise a mature practice. Nearly all respondents expected to deploy key capabilities like network monitoring, security monitoring and log management by 2025. The majority expected to have 88–97% of the 17 capabilities deployed, but just 3% of respondents already maintain all 17 capabilities today.

3. Observability is now a board-level imperative

Tech executives have recognized the value and importance of observability: 73% of respondents reported that their C-suite executives are supporters of observability, and 78% saw observability as a key enabler for achieving core business goals. Furthermore, of those who had mature observability practices by the report's definition, 100% indicated that observability improves revenue retention by deepening their understanding of customer behaviors.

4. For many organizations, large sections of tech stacks are still not being fully observed or monitored

Despite the overall enthusiasm for observability and the fact that most organizations are practicing some form of observability, only 27% of respondents' organizations have achieved full-stack observability as defined in the report. The overall lack of adoption of full-stack observability signals that many organizations have an opportunity to make rapid improvements to their observability practices over the next year.

5. Organizations must tackle fragmentation of data, tools and teams

Many organizations use a patchwork of tools to monitor their technology stacks, requiring extensive manual effort only to gain a fragmented view of IT systems. More than 80% of respondents used four or more observability tools, and a third of respondents had to detect outages manually or from complaints. Just 7% of respondents said their telemetry data is unified in one place, and only 5% had a mature observability practice. Recognizing the challenges of fragmentation, respondents reported the need for simplicity, integration, seamlessness, and more efficient ways to complete high-value projects.

6. Telemetry data is often siloed

Siloed and fragmented data lead to a painful user experience, but slightly more than half (51%) of respondents still have siloed data in their tech stacks. Of those who have entirely siloed data, 77% stated they would prefer a single, consolidated platform. Those who struggle the most to juggle data across different silos long for more simplicity in their observability solutions.

7. There is a strong correlation between full-stack observability and faster mean time to detection (MTTD)

Respondents from organizations that have achieved full-stack observability, as well as those who have already prioritized full-stack observability, were more likely than others to experience the fastest mean time to detect an outage — less than five minutes. The data supports a strong correlation between achieving or prioritizing full-stack observability and a range of performance benefits, including fewer outages, improved outage detection rates, and improved resolution.

8. One of the biggest roadblocks to achieving observability is a failure to understand the benefits

The 2022 Observability Forecast asked respondents to name the biggest challenges preventing full-stack observability. The top responses were a lack of understanding of the benefits of observability and the belief that current IT performance is adequate (28% for each). Other leading roadblocks were a lack of budget (27%) and too many monitoring tools (25%).

9. Despite that, IT professionals recognize the bottom-line benefits of observability

Survey respondents named a wide range of observability benefits. These include improved uptime and reliability (cited by 36% of respondents), increased operational efficiency (35%), proactive detection of issues (33%) and an improved customer experience (33%). Respondents also said that observability improves the lives of engineers and developers, with 34% saying it helped to increase productivity and 32% crediting observability for supporting cross-team collaboration.

10. Organizations expect to need observability for AI, IoT and key business applications

C-suite executives see observability playing a major role in the development of new technologies. More than half of respondents said they would need observability most for artificial intelligence (AI) applications, while 48% mentioned the Internet of Things, 38% cited edge computing, and 36% highlighted blockchain applications. Observability in AI was mentioned across industries, with a majority of respondents in services/consulting (62%), energy/utilities (60%), government (58%) and IT/telco (51%) mentioning the need for observability in their AI projects.

Ishan Mukherjee is SVP of Growth at New Relic

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

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 ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

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 ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...