5 Takeaways from the Observability Forecast for Media and Entertainment

Nic Benders
New Relic
Learn more about New Relic

New research from New Relic shows how observability is being used to inform performance, reliability, and revenue across industries. In 2025, the company surveyed more than 1,700 IT and engineering leaders globally, including 120 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.

1. Outages cost M&E organizations an average of $2 million per hour

The M&E industry's average incident detection and resolution times — around 30 minutes to detect and 40 minutes to resolve — are in line with the global average, but the stakes are higher for this industry. An outage for an M&E organization can mean that millions of people miss out on a sporting event, a highly anticipated content premiere, or a livestream from their favorite gamer. It only takes minutes for downtime to significantly damage a streaming platform's brand.

Further, organizations that fail to deliver a reliable digital experience face substantial penalties in the form of lost subscriptions and ad revenue. 79% of respondents reported that outages cost one million dollars or more per hour, with the majority (33%) reporting an average cost of one to two million dollars per hour. 40% of respondents point to security failures as the primary cause of outages, followed by network failures (34%) and deployment errors (30%).

2. Observability pays off in less downtime and better customer experiences

Fortunately, M&E organizations report that their investments in observability are paying off in reduced downtime costs, improved ad performance, and subscriber retention. More than a third (39%) say that observability is increasing uptime and reliability, and a similar percentage (36%) say it's directly improving the user experience.

But the benefits of observability extend beyond technical metrics, increasingly helping organizations deliver more effectively on business objectives. Half of leadership respondents say observability helps them hit technical KPIs, and 36% say it strengthens their ability to drive tactical execution.

As a result, more than half (51%) of respondents report a 2-3x return on investment from their observability initiatives, higher than in industries such as finance and retail. And for a third of respondents, that ROI is showing up on their bottom line as business or revenue growth.

3. AI adoption is creating disruption and increasing complexity

M&E organizations have been eager to integrate AI across their businesses, from content recommendation engines and personalization algorithms to incident response, but they're cognizant of the risks it poses to stability and uptime. Nearly a third (30%) of respondents call AI adoption the primary driver of their observability strategy.

AI has complicated the observability landscape by introducing a more complex tech stack that is harder to manage than traditional software. At the same time, AI-powered observability is strengthening resilience by automating manual processes to accelerate incident detection and response. Observability platforms can detect quality drops in real time, roll back problematic deployments, and deliver ads without interruption.

4. Organizations are integrating business data and telemetry

To better quantify the business impact of downtime — and the benefits of observability — M&E organizations are increasingly integrating business data with telemetry. Nearly half (48%) of respondents say they have integrated, or plan to integrate, customer data, allowing leaders to see not just when a service goes down, but how outages immediately affect subscriber retention, ad delivery, and content engagement.

Many are extending this approach beyond customer data, integrating human resources (48%), operations (48%), and logistics data (46%) to create a more complete, real-time view of business health.

5. M&E organizations are making headway on tool sprawl

As software landscapes grow more complex, organizations use several observability tools on average that can handle a sprawling ecosystem of architectural patterns and applications. In M&E, tool sprawl is often exacerbated by the fact that content delivery, ad tech, and backend systems are monitored separately. The median number of observability tools in use by M&E organizations is four, and 18% of organizations run eight or more.

But a shift toward unified observability platforms is helping the industry chip away at tool sprawl. 40% of organizations now use three or fewer tools, up 10% from 2024. By moving away from siloed dashboards and toward unified platforms that provide full-stack visibility, organizations are strengthening their ability to deliver uptime and reliability while spending their observability budgets more efficiently.

Together, these findings show that observability is no longer a technical nice-to-have, but a business-critical capability for M&E organizations. As complexity and audience expectations continue to rise, teams that invest in unified, AI-driven observability will be best positioned to protect revenue, safeguard their brand, and deliver the seamless experiences viewers expect.

Nic Benders is Chief Technical Strategist at New Relic

Related Links

Hot Topics

Related Vendors

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

5 Takeaways from the Observability Forecast for Media and Entertainment

Nic Benders
New Relic
Learn more about New Relic

New research from New Relic shows how observability is being used to inform performance, reliability, and revenue across industries. In 2025, the company surveyed more than 1,700 IT and engineering leaders globally, including 120 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.

1. Outages cost M&E organizations an average of $2 million per hour

The M&E industry's average incident detection and resolution times — around 30 minutes to detect and 40 minutes to resolve — are in line with the global average, but the stakes are higher for this industry. An outage for an M&E organization can mean that millions of people miss out on a sporting event, a highly anticipated content premiere, or a livestream from their favorite gamer. It only takes minutes for downtime to significantly damage a streaming platform's brand.

Further, organizations that fail to deliver a reliable digital experience face substantial penalties in the form of lost subscriptions and ad revenue. 79% of respondents reported that outages cost one million dollars or more per hour, with the majority (33%) reporting an average cost of one to two million dollars per hour. 40% of respondents point to security failures as the primary cause of outages, followed by network failures (34%) and deployment errors (30%).

2. Observability pays off in less downtime and better customer experiences

Fortunately, M&E organizations report that their investments in observability are paying off in reduced downtime costs, improved ad performance, and subscriber retention. More than a third (39%) say that observability is increasing uptime and reliability, and a similar percentage (36%) say it's directly improving the user experience.

But the benefits of observability extend beyond technical metrics, increasingly helping organizations deliver more effectively on business objectives. Half of leadership respondents say observability helps them hit technical KPIs, and 36% say it strengthens their ability to drive tactical execution.

As a result, more than half (51%) of respondents report a 2-3x return on investment from their observability initiatives, higher than in industries such as finance and retail. And for a third of respondents, that ROI is showing up on their bottom line as business or revenue growth.

3. AI adoption is creating disruption and increasing complexity

M&E organizations have been eager to integrate AI across their businesses, from content recommendation engines and personalization algorithms to incident response, but they're cognizant of the risks it poses to stability and uptime. Nearly a third (30%) of respondents call AI adoption the primary driver of their observability strategy.

AI has complicated the observability landscape by introducing a more complex tech stack that is harder to manage than traditional software. At the same time, AI-powered observability is strengthening resilience by automating manual processes to accelerate incident detection and response. Observability platforms can detect quality drops in real time, roll back problematic deployments, and deliver ads without interruption.

4. Organizations are integrating business data and telemetry

To better quantify the business impact of downtime — and the benefits of observability — M&E organizations are increasingly integrating business data with telemetry. Nearly half (48%) of respondents say they have integrated, or plan to integrate, customer data, allowing leaders to see not just when a service goes down, but how outages immediately affect subscriber retention, ad delivery, and content engagement.

Many are extending this approach beyond customer data, integrating human resources (48%), operations (48%), and logistics data (46%) to create a more complete, real-time view of business health.

5. M&E organizations are making headway on tool sprawl

As software landscapes grow more complex, organizations use several observability tools on average that can handle a sprawling ecosystem of architectural patterns and applications. In M&E, tool sprawl is often exacerbated by the fact that content delivery, ad tech, and backend systems are monitored separately. The median number of observability tools in use by M&E organizations is four, and 18% of organizations run eight or more.

But a shift toward unified observability platforms is helping the industry chip away at tool sprawl. 40% of organizations now use three or fewer tools, up 10% from 2024. By moving away from siloed dashboards and toward unified platforms that provide full-stack visibility, organizations are strengthening their ability to deliver uptime and reliability while spending their observability budgets more efficiently.

Together, these findings show that observability is no longer a technical nice-to-have, but a business-critical capability for M&E organizations. As complexity and audience expectations continue to rise, teams that invest in unified, AI-driven observability will be best positioned to protect revenue, safeguard their brand, and deliver the seamless experiences viewers expect.

Nic Benders is Chief Technical Strategist at New Relic

Related Links

Hot Topics

Related Vendors

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...