2017 Application Performance Management Predictions - Part 1

The advent of the Holiday Season means it is time for the annual list of Application Performance Management (APM) predictions, the most popular post on APMdigest, viewed by tens of thousands of people in the IT community around the world every year. Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2017.

APMdigest not only covers APM, but also a variety of related technologies, and the predictions list does the same. In addition to APM, the related technologies covered include IT Operations Analytics (ITOA) also called Advanced IT Analytics (AIA), IT Service Management (ITSM), End-User Experience Management (EUEM), Network Performance Management (NPM), as well as infrastructure monitoring and cloud.

The 2017 APM Predictions List is the longest predictions list ever featured on APMdigest. Last year's list featured 30 prediction categories, while this year's list features 50 categories. This extensive list of predictions reflects the vast scope of digital transformation accompanied by revolutionary changes in the IT landscape. A few of the predictions overlap with last year's predictions, but most of the predictions are new ideas or next steps into IT's brave new world.

Although many of the 2017 APM predictions are new and specific, the general technologies covered are very familiar — Analytics and Cloud for example — but explored in new ways.

One of the most popular topics discussed on previous lists, DevOps now has its own complete list of predictions on DEVOPSdigest for the second consecutive year. Most DevOps and development related predictions will be found on the DEVOPSdigest, however, DevOps does make an appearance on this year's APM list as well.

Some of these predictions may come true in the next 12 months, while others may be just as valid but take several years to be realized. Still others may be wishful thinking or unbased fears. But taken collectively, this list of predictions offers an interesting and valuable snapshot of what the APM industry is thinking about, planning, expecting and hoping for next year.

The predictions will be posted in 5 parts over the next 5 days.

A forecast by the top minds in Application Performance Management today, here are the predictions:

1. ADVANCED ANALYTICS TRANSFORMS APM

Digital, customer and user experience will continue to lead the way in helping to unify development, IT operations, ITSM and business stakeholders. But most vendors will continue to market much too narrowly to the opportunity. Look for emerging advanced IT analytics (or what the industry calls ITOA) solutions optimized to assimilate application/infrastructure performance and business outcomes in real-time, and predictively, as emerging manager of managers to transform the APM/DevOps universe.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

Read Dennis Drogseth's blog: Transforming Operations, and IT as a Whole, with the Right Technology Investments

Monitoring is dead. Long live monitoring. In 2017 monitoring will remain critical to the successful delivery of stellar user experiences. But the real value will be from analytics, which harnesses the big data of application and infrastructure metrics and correlates them to business performance. Organizations will get insights never fully attainable before on user experience and its impact on customer loyalty and revenue.
Aruna Ravichandran
VP of DevOps Solution Marketing and Management, CA Technologies

Read Aruna Ravichandran's blog: 3 DevOps Predictions for 2017

I see that APM is going to play a pivotal role in the ITOA movement while leveraging Advanced IT Analytics (AIA). In order for this to occur, APM and ITOA will need a seamless integration with an AIA solution, whether by vendor acquisitions or open integration that is platform agnostic.
Larry Dragich
Director of Customer Experience Management at the Auto Club Group and Founder of the APM Strategies Group on LinkedIn.

Read Larry Dragich's blog: End User Experience - Perceptions of Performance

2. ARTIFICIAL INTELLIGENCE: THE NEXT STEP IN ANALYTICS

We've gotten past the hype of the Big Data wave and AI is replacing analytics.
Assaf Resnick
CEO, BigPanda

In 2017 we will see companies substantially rethinking the roles of the DevOps and operations teams. They will move away from viewing their main responsibilities as analyzing data and present results to their peers: in 2017, most of these analytic tasks will be taken over by intelligent monitoring software utilizing anomaly detection, big data analysis and artificial intelligence. There are two key drivers for this shift: 1) the ever-increasing complexity and scale of modern software and 2) scaling the teams needed to cover up to four times more of their application landscape is economically unviable to do. This fundamental move to AI-assisted intelligent monitoring systems will elevate monitoring to new stakeholders in the organization; especially executive management. Questions like "Is our application working fine?" or "Are our users happy?" will be answered instantly by voice-based multi-modal interfaces without requiring any human analysis.
Alois Reitbauer
VP, Chief Technology Strategist, Dynatrace

I see the future challenge for APM vendors to be around analytics. Solution providers will need to introduce Artificial Intelligence based on machine learning algorithms to help humans cope with the incredible complexity of the technology. With this, they will be able to better predict performance issues and their impact. When issues strike, solutions could execute self-healing remediation actions or if not possible provide automated root-cause analysis stats and the best recommendations to restore IT services within the shortest timeframe.
Vincent Geffray
Senior Director of Product Marketing, IT Alerting and IoT, Everbridge

With the surge in business data, investing in analytics will no longer be a choice; it will become a necessity driven by need to have a competitive edge. With plenty of analytics solutions available in the market, the key factors that enterprises look for will be the ease of use to derive the insights they need. In line with this, analytics solutions that can leverage emerging technologies such as advanced machine learning, and AI capabilities to analyze data across complex data systems for better decision making, will find larger adoption.
Pritika Ramani
Product Analyst, ManageEngine

3. APM CATCHES WAVE OF MACHINE LEARNING

The big prediction is the wave of Machine Learning experiments/products that will be applied to aspects of APM.
Martijn Verburg
CEO, jClarity

2017 will mark the year of Machine Learning. Every major IT vendor is including ML in their various solutions and offering this on their cloud. For Application Performance Management this means a shift towards cloud based, intelligent, solutions that can digest both structured (e.g. traditional graph structured data, end user sessions, transaction traces, etc) as well as unstructured data (e.g. logs, crash dumps, etc). Powered by Machine Learning, the APM solution will be able to intelligently detect anomalies, cluster and correlate (un)structured data and predict outages faster, discover dependencies, topologies and relationships automatically among the hybrid IT estate. No matter how often and quickly it changes and throughout the entire application lifecycle from development into production.
Daniel Schrijver
Senior Principal Product Marketing Director, Oracle

Two big trends could affect IT Service Management in 2017: Machine Learning and Big Data Analytics. With the application of machine learning, IT service desks can turn "intelligent" with contextual assistance to end users and enable faster resolution of tickets by technicians. The focus on big data analytics would help IT service desk teams mine large volumes of data in the service desk and glean insights that lead to high impact service delivery.
Kumar Ramakrishnan
Product Manager, ManageEngine

4. ALGORITHMIC IT OPS EMERGES

The sheer volume of data generated by applications and infrastructure will only increase, resulting in data overload. For the first time, IT Operations teams will embrace an algorithmic approach – also known as Algorithmic IT Operations, or AIOps – to detect signal from noise to ensure successful service delivery. AIOps platforms will provide IT Operations teams with situational awareness and diagnostic capabilities that were not previously possible using manual, non-algorithmic techniques.
Michael Butt
Senior Product Marketing Manager, BigPanda

Read Michael Butt's blog: Preventing Outages During the Holiday Shopping Season

5. AIOPS CONFUSION

We predict rising confusion over Gartner's poorly thought out AIOps (Algorithmic Infrastructure Operations), as people think "AI" stands for "artificial intelligence," and Gartner's guidance on algorithms is out-of-date and overly simplistic.
Jason Bloomberg
President, Intellyx

6. IT GAME CHANGER: INTELLIGENT ALERTING

The APM market will continue to experience growth, however, intelligent routing technologies for alerts will become increasingly important and will commoditize the APM market. Intelligent alerting will be big next year as more and more companies realize that monitoring alone isn't good enough. Knowing that there is a problem is one thing, but telling a person who can address a specific problem is an IT game changer.
Scott Willson
Product Marketing Director and DevOps Evangelist, Automic Software

7. USERS DEMAND ROOT CAUSE ANALYSIS

We believe that web complexity will only continue to increase, as companies try harder to deliver strong digital experiences, extending their infrastructure to include third-party services such as CDNs and cloud providers. Organizations will realize there can be a nasty side effect: more potential points of failure and a harder time finding the root cause of issues, with each added second of delay leading to more lost revenues. Organizations will understand that this sea of information, no matter how big, is useless without actionable insights. As a core foundation of their APM efforts, they will demand solutions that can pinpoint root causes with unparalleled speed and accuracy.
Dennis Callaghan
Director of Industry Innovation, Catchpoint

We've seen companies running 200 IT ops tools or more. Each of these tools triggers separate feeds into different dashboards, and – in spite of providing effective monitoring for their "slice" of the application stack – has created a new problem of monitoring overload. Customers will start to add a layer of analytics on top of these monitoring tools to "connect the dots" across the entire stack. By analyzing performance data and alerts combined with the relevant application topology, users can recognize the difference between inconsequential anomalies and actual performance problems. What customers are asking for is a root cause analysis, not more alarms.
JF Huard, Ph.D.
Founder and CTO, Perspica

8. DEEP LEARNING API ACCELERATES ITOA CONVERGENCE

Democratization of deep learning APIs will accelerate convergence of IT Operations Analytics by easing the merge of distinct data sources like APM, change management, operations management infrastructure monitoring for delivering even more intelligent insight to IT Operations leaders.
Yann Guernion
Product Marketing Director, Workload Automation, Automic Software

9. ITOA CHALLENGE: METRICS AVAILABILITY

Enterprises will realize that APM metrics (response time, throughput, errors, etc. per transaction) are the single most important set of metrics in their ITOA strategies. But enterprises and APM vendors will struggle to make these crucial metrics available for the broad set of custom developed and purchased applications and transactions that most enterprise live with. Integration of these most important metrics with metrics that capture the behavior of cloud based infrastructures will prove to be a massive challenge.
Bernd Harzog
CEO, OpsDataStore

Read 2017 Application Performance Management Predictions - Part 2

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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 ...

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Why VM and Container Monitoring Is Not Enough for Cloud-Hosted Autonomous Agents

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

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2017 Application Performance Management Predictions - Part 1

The advent of the Holiday Season means it is time for the annual list of Application Performance Management (APM) predictions, the most popular post on APMdigest, viewed by tens of thousands of people in the IT community around the world every year. Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2017.

APMdigest not only covers APM, but also a variety of related technologies, and the predictions list does the same. In addition to APM, the related technologies covered include IT Operations Analytics (ITOA) also called Advanced IT Analytics (AIA), IT Service Management (ITSM), End-User Experience Management (EUEM), Network Performance Management (NPM), as well as infrastructure monitoring and cloud.

The 2017 APM Predictions List is the longest predictions list ever featured on APMdigest. Last year's list featured 30 prediction categories, while this year's list features 50 categories. This extensive list of predictions reflects the vast scope of digital transformation accompanied by revolutionary changes in the IT landscape. A few of the predictions overlap with last year's predictions, but most of the predictions are new ideas or next steps into IT's brave new world.

Although many of the 2017 APM predictions are new and specific, the general technologies covered are very familiar — Analytics and Cloud for example — but explored in new ways.

One of the most popular topics discussed on previous lists, DevOps now has its own complete list of predictions on DEVOPSdigest for the second consecutive year. Most DevOps and development related predictions will be found on the DEVOPSdigest, however, DevOps does make an appearance on this year's APM list as well.

Some of these predictions may come true in the next 12 months, while others may be just as valid but take several years to be realized. Still others may be wishful thinking or unbased fears. But taken collectively, this list of predictions offers an interesting and valuable snapshot of what the APM industry is thinking about, planning, expecting and hoping for next year.

The predictions will be posted in 5 parts over the next 5 days.

A forecast by the top minds in Application Performance Management today, here are the predictions:

1. ADVANCED ANALYTICS TRANSFORMS APM

Digital, customer and user experience will continue to lead the way in helping to unify development, IT operations, ITSM and business stakeholders. But most vendors will continue to market much too narrowly to the opportunity. Look for emerging advanced IT analytics (or what the industry calls ITOA) solutions optimized to assimilate application/infrastructure performance and business outcomes in real-time, and predictively, as emerging manager of managers to transform the APM/DevOps universe.
Dennis Drogseth
VP of Research, Enterprise Management Associates (EMA)

Read Dennis Drogseth's blog: Transforming Operations, and IT as a Whole, with the Right Technology Investments

Monitoring is dead. Long live monitoring. In 2017 monitoring will remain critical to the successful delivery of stellar user experiences. But the real value will be from analytics, which harnesses the big data of application and infrastructure metrics and correlates them to business performance. Organizations will get insights never fully attainable before on user experience and its impact on customer loyalty and revenue.
Aruna Ravichandran
VP of DevOps Solution Marketing and Management, CA Technologies

Read Aruna Ravichandran's blog: 3 DevOps Predictions for 2017

I see that APM is going to play a pivotal role in the ITOA movement while leveraging Advanced IT Analytics (AIA). In order for this to occur, APM and ITOA will need a seamless integration with an AIA solution, whether by vendor acquisitions or open integration that is platform agnostic.
Larry Dragich
Director of Customer Experience Management at the Auto Club Group and Founder of the APM Strategies Group on LinkedIn.

Read Larry Dragich's blog: End User Experience - Perceptions of Performance

2. ARTIFICIAL INTELLIGENCE: THE NEXT STEP IN ANALYTICS

We've gotten past the hype of the Big Data wave and AI is replacing analytics.
Assaf Resnick
CEO, BigPanda

In 2017 we will see companies substantially rethinking the roles of the DevOps and operations teams. They will move away from viewing their main responsibilities as analyzing data and present results to their peers: in 2017, most of these analytic tasks will be taken over by intelligent monitoring software utilizing anomaly detection, big data analysis and artificial intelligence. There are two key drivers for this shift: 1) the ever-increasing complexity and scale of modern software and 2) scaling the teams needed to cover up to four times more of their application landscape is economically unviable to do. This fundamental move to AI-assisted intelligent monitoring systems will elevate monitoring to new stakeholders in the organization; especially executive management. Questions like "Is our application working fine?" or "Are our users happy?" will be answered instantly by voice-based multi-modal interfaces without requiring any human analysis.
Alois Reitbauer
VP, Chief Technology Strategist, Dynatrace

I see the future challenge for APM vendors to be around analytics. Solution providers will need to introduce Artificial Intelligence based on machine learning algorithms to help humans cope with the incredible complexity of the technology. With this, they will be able to better predict performance issues and their impact. When issues strike, solutions could execute self-healing remediation actions or if not possible provide automated root-cause analysis stats and the best recommendations to restore IT services within the shortest timeframe.
Vincent Geffray
Senior Director of Product Marketing, IT Alerting and IoT, Everbridge

With the surge in business data, investing in analytics will no longer be a choice; it will become a necessity driven by need to have a competitive edge. With plenty of analytics solutions available in the market, the key factors that enterprises look for will be the ease of use to derive the insights they need. In line with this, analytics solutions that can leverage emerging technologies such as advanced machine learning, and AI capabilities to analyze data across complex data systems for better decision making, will find larger adoption.
Pritika Ramani
Product Analyst, ManageEngine

3. APM CATCHES WAVE OF MACHINE LEARNING

The big prediction is the wave of Machine Learning experiments/products that will be applied to aspects of APM.
Martijn Verburg
CEO, jClarity

2017 will mark the year of Machine Learning. Every major IT vendor is including ML in their various solutions and offering this on their cloud. For Application Performance Management this means a shift towards cloud based, intelligent, solutions that can digest both structured (e.g. traditional graph structured data, end user sessions, transaction traces, etc) as well as unstructured data (e.g. logs, crash dumps, etc). Powered by Machine Learning, the APM solution will be able to intelligently detect anomalies, cluster and correlate (un)structured data and predict outages faster, discover dependencies, topologies and relationships automatically among the hybrid IT estate. No matter how often and quickly it changes and throughout the entire application lifecycle from development into production.
Daniel Schrijver
Senior Principal Product Marketing Director, Oracle

Two big trends could affect IT Service Management in 2017: Machine Learning and Big Data Analytics. With the application of machine learning, IT service desks can turn "intelligent" with contextual assistance to end users and enable faster resolution of tickets by technicians. The focus on big data analytics would help IT service desk teams mine large volumes of data in the service desk and glean insights that lead to high impact service delivery.
Kumar Ramakrishnan
Product Manager, ManageEngine

4. ALGORITHMIC IT OPS EMERGES

The sheer volume of data generated by applications and infrastructure will only increase, resulting in data overload. For the first time, IT Operations teams will embrace an algorithmic approach – also known as Algorithmic IT Operations, or AIOps – to detect signal from noise to ensure successful service delivery. AIOps platforms will provide IT Operations teams with situational awareness and diagnostic capabilities that were not previously possible using manual, non-algorithmic techniques.
Michael Butt
Senior Product Marketing Manager, BigPanda

Read Michael Butt's blog: Preventing Outages During the Holiday Shopping Season

5. AIOPS CONFUSION

We predict rising confusion over Gartner's poorly thought out AIOps (Algorithmic Infrastructure Operations), as people think "AI" stands for "artificial intelligence," and Gartner's guidance on algorithms is out-of-date and overly simplistic.
Jason Bloomberg
President, Intellyx

6. IT GAME CHANGER: INTELLIGENT ALERTING

The APM market will continue to experience growth, however, intelligent routing technologies for alerts will become increasingly important and will commoditize the APM market. Intelligent alerting will be big next year as more and more companies realize that monitoring alone isn't good enough. Knowing that there is a problem is one thing, but telling a person who can address a specific problem is an IT game changer.
Scott Willson
Product Marketing Director and DevOps Evangelist, Automic Software

7. USERS DEMAND ROOT CAUSE ANALYSIS

We believe that web complexity will only continue to increase, as companies try harder to deliver strong digital experiences, extending their infrastructure to include third-party services such as CDNs and cloud providers. Organizations will realize there can be a nasty side effect: more potential points of failure and a harder time finding the root cause of issues, with each added second of delay leading to more lost revenues. Organizations will understand that this sea of information, no matter how big, is useless without actionable insights. As a core foundation of their APM efforts, they will demand solutions that can pinpoint root causes with unparalleled speed and accuracy.
Dennis Callaghan
Director of Industry Innovation, Catchpoint

We've seen companies running 200 IT ops tools or more. Each of these tools triggers separate feeds into different dashboards, and – in spite of providing effective monitoring for their "slice" of the application stack – has created a new problem of monitoring overload. Customers will start to add a layer of analytics on top of these monitoring tools to "connect the dots" across the entire stack. By analyzing performance data and alerts combined with the relevant application topology, users can recognize the difference between inconsequential anomalies and actual performance problems. What customers are asking for is a root cause analysis, not more alarms.
JF Huard, Ph.D.
Founder and CTO, Perspica

8. DEEP LEARNING API ACCELERATES ITOA CONVERGENCE

Democratization of deep learning APIs will accelerate convergence of IT Operations Analytics by easing the merge of distinct data sources like APM, change management, operations management infrastructure monitoring for delivering even more intelligent insight to IT Operations leaders.
Yann Guernion
Product Marketing Director, Workload Automation, Automic Software

9. ITOA CHALLENGE: METRICS AVAILABILITY

Enterprises will realize that APM metrics (response time, throughput, errors, etc. per transaction) are the single most important set of metrics in their ITOA strategies. But enterprises and APM vendors will struggle to make these crucial metrics available for the broad set of custom developed and purchased applications and transactions that most enterprise live with. Integration of these most important metrics with metrics that capture the behavior of cloud based infrastructures will prove to be a massive challenge.
Bernd Harzog
CEO, OpsDataStore

Read 2017 Application Performance Management Predictions - Part 2

Related Links

Hot Topics

The Latest

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 ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Why VM and Container Monitoring Is Not Enough for Cloud-Hosted Autonomous Agents

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Beyond Monitoring: Why IT Operations Must Evolve into Decision Operations

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...