10 Key Takeaways from the 2023 Observability Forecast

Ishan Mukherjee
New Relic
Learn more about New Relic

Earlier this year, New Relic conducted a study on observability. The company surveyed 1,700 IT practitioners and decision-makers across 15 countries in North America, Europe and the Asia Pacific region to to understand the current state of the practice and the external forces influencing spending and adoption. The 2023 Observability Forecast reveals observability's impact on the lives of technical professionals and businesses' bottom lines.


Here are 10 key takeaways from the forecast:

1. Observability delivers 2x annual ROI

Respondents to the survey receive a median $2 of return per $1 of investment in observability, with 41% receiving more than $1 million total annual value. Almost all (96%) respondents expected a significant negative business outcome without observability — noting higher operation costs and revenue loss from downtime as the concrete financial impacts of not having observability.

2. Outages are expensive - observability is critical

32% of respondents said critical business app outages cost more than $500K per hour of downtime. Although respondents report a median annual outage cost of $7.75 million, those with full-stack observability experience a median outage cost 37% lower than those without full-stack observability.

3. Observability improves service-level metrics

Respondents with full-stack observability are more likely to experience the fastest mean time to resolution (MTTR) and were 19% more likely to resolve high-business-impact outages in 30 minutes or less compared to those without full-stack observability.

4. Observability adoption accelerating

While most organizations still don't monitor their full tech stack, this is changing. Full-stack observability increased 58% YoY. By mid-2026, at least 82% of respondents expected to deploy each of the 17 different observability capabilities.

5. Organizations want tool consolidation

Tool sprawl remains an obstacle for organizations of all sizes despite a 2-to-1 preference for a single, consolidated platform. However, the proportion using a single tool more than doubled year-over-year, and the average number of tools deployed has gone down by almost one tool.

6. Organizations still haven't fully unified telemetry data

Siloed and fragmented data make for a painful user experience. Among the 40% of respondents who had more siloed data, 68% indicated that they strongly prefer a single, consolidated platform. Respondents with more unified telemetry data were more likely to have fewer high-business-impact outages, a faster MTTD, and a faster MTTR than those with more siloed telemetry data.

7. Security is driving observability adoption

Modern applications typically run in the cloud and depend on hundreds of components, each introducing additional monitoring challenges and security risks. Nearly half (49%) said an increased focus on security, governance, risk, and compliance was driving the need for observability, making it the top choice two years in a row. The security focus reflects the rise of cybersecurity threats and complex cloud-native application architectures.

8. AI and apps are also important to organizations

About two in five (38%) said the integration of business apps into workflows and the adoption of artificial intelligence (AI) technologies was driving the need for observability. The focus on AI and business apps like enterprise resource planning (ERP) and customer relationship management (CRM) makes sense as organizations are competing to attract and retain customers by providing the best customer experience.

9. Clear business benefits of observability

Almost half (46%) of practitioners said observability increases their productivity so they can find and resolve issues faster. About a third (35%) of IT decision-makers said it helps them achieve technical key performance indicators (KPIs) and/or business KPIs (31%). Of the total respondents, two out of five (40%) said improved system uptime and reliability is a primary benefit — 13% more than last year — while 38% cited increased operational efficiency and 34% focused on security vulnerability management.

10. The increase in observability deployment is expected to continue

Most respondents (83%) expected to deploy at least one new capability in the next year, with more than half (51%) of respondents expected to deploy one to five, and nearly a third (32%) expected to deploy six or more. For 2024, at least 90% of organizations expect to deploy capabilities like network monitoring, database monitoring, security monitoring, and alerts. These findings indicate observability's strong growth potential in the near future.

Ishan Mukherjee is SVP of Growth at New Relic

Related Links

Hot Topics

Related Vendors

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

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 ...

5 Takeaways from the Observability Forecast for Media and Entertainment

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 ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

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

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

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 ...

10 Key Takeaways from the 2023 Observability Forecast

Ishan Mukherjee
New Relic
Learn more about New Relic

Earlier this year, New Relic conducted a study on observability. The company surveyed 1,700 IT practitioners and decision-makers across 15 countries in North America, Europe and the Asia Pacific region to to understand the current state of the practice and the external forces influencing spending and adoption. The 2023 Observability Forecast reveals observability's impact on the lives of technical professionals and businesses' bottom lines.


Here are 10 key takeaways from the forecast:

1. Observability delivers 2x annual ROI

Respondents to the survey receive a median $2 of return per $1 of investment in observability, with 41% receiving more than $1 million total annual value. Almost all (96%) respondents expected a significant negative business outcome without observability — noting higher operation costs and revenue loss from downtime as the concrete financial impacts of not having observability.

2. Outages are expensive - observability is critical

32% of respondents said critical business app outages cost more than $500K per hour of downtime. Although respondents report a median annual outage cost of $7.75 million, those with full-stack observability experience a median outage cost 37% lower than those without full-stack observability.

3. Observability improves service-level metrics

Respondents with full-stack observability are more likely to experience the fastest mean time to resolution (MTTR) and were 19% more likely to resolve high-business-impact outages in 30 minutes or less compared to those without full-stack observability.

4. Observability adoption accelerating

While most organizations still don't monitor their full tech stack, this is changing. Full-stack observability increased 58% YoY. By mid-2026, at least 82% of respondents expected to deploy each of the 17 different observability capabilities.

5. Organizations want tool consolidation

Tool sprawl remains an obstacle for organizations of all sizes despite a 2-to-1 preference for a single, consolidated platform. However, the proportion using a single tool more than doubled year-over-year, and the average number of tools deployed has gone down by almost one tool.

6. Organizations still haven't fully unified telemetry data

Siloed and fragmented data make for a painful user experience. Among the 40% of respondents who had more siloed data, 68% indicated that they strongly prefer a single, consolidated platform. Respondents with more unified telemetry data were more likely to have fewer high-business-impact outages, a faster MTTD, and a faster MTTR than those with more siloed telemetry data.

7. Security is driving observability adoption

Modern applications typically run in the cloud and depend on hundreds of components, each introducing additional monitoring challenges and security risks. Nearly half (49%) said an increased focus on security, governance, risk, and compliance was driving the need for observability, making it the top choice two years in a row. The security focus reflects the rise of cybersecurity threats and complex cloud-native application architectures.

8. AI and apps are also important to organizations

About two in five (38%) said the integration of business apps into workflows and the adoption of artificial intelligence (AI) technologies was driving the need for observability. The focus on AI and business apps like enterprise resource planning (ERP) and customer relationship management (CRM) makes sense as organizations are competing to attract and retain customers by providing the best customer experience.

9. Clear business benefits of observability

Almost half (46%) of practitioners said observability increases their productivity so they can find and resolve issues faster. About a third (35%) of IT decision-makers said it helps them achieve technical key performance indicators (KPIs) and/or business KPIs (31%). Of the total respondents, two out of five (40%) said improved system uptime and reliability is a primary benefit — 13% more than last year — while 38% cited increased operational efficiency and 34% focused on security vulnerability management.

10. The increase in observability deployment is expected to continue

Most respondents (83%) expected to deploy at least one new capability in the next year, with more than half (51%) of respondents expected to deploy one to five, and nearly a third (32%) expected to deploy six or more. For 2024, at least 90% of organizations expect to deploy capabilities like network monitoring, database monitoring, security monitoring, and alerts. These findings indicate observability's strong growth potential in the near future.

Ishan Mukherjee is SVP of Growth at New Relic

Related Links

Hot Topics

Related Vendors

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

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 ...

5 Takeaways from the Observability Forecast for Media and Entertainment

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 ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

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

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

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 ...