2018 Application Performance Management Predictions - Part 5

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 2018. Part 5 covers NoOps, Analytics, Machine Learning and AI.

Start with 2018 Application Performance Management Predictions - Part 1

Start with 2018 Application Performance Management Predictions - Part 2

Start with 2018 Application Performance Management Predictions - Part 3

Start with 2018 Application Performance Management Predictions - Part 4

AUTONOMOUS OPERATIONS: NO OPS

2018 will mark the year of blending APM intelligence, as just another data source, into the ultimate IT business goal: Autonomous Operations, also called NoOps by Forrester. These AI-powered, automated and autonomous systems will automate deployment, monitoring, management, securing and remediation of IT environment. If your current APM solution is not already integrated/capable of integrating into these larger systems, you'll want to use 2018 to get yourself acquainted and start your projects. The future starts now.
Daniel Schrijver
Senior Principal Product Marketing Director, Oracle

NO OPS NO LONGER

"NoOps" will no longer be a thing as infrastructure and operations/run teams become more involved in the development aspects of the software engineering and take back the Ops.
Alex Popov
Cloud Enablement and Continuous Delivery, Barclaycard

MACHINE LEARNING

The New Focus: Proactive, Not Reactive. In today's fast-changing, dynamic virtual environments, IT managers can no longer afford to be reactive or to use trial-and-error to address issues. As 2018 progresses, IT management will be able to take full advantage of the holistic, predictive analytics that new machine-learning based tools enable. These tools can predict and even recommend steps to avoid a variety of issues that can take IT application owners by surprise with costly results. For example, IT can use these tools to eliminate application performance issues, threats to failovers and unexpected capacity usage.
Jerry Melnick
President & CEO, SIOS Technology

New global research from Quocirca, Damage Control: The Impact of Critical IT Incidents, shows that improved operational intelligence, driven by machine data, will continue reduce the impact of critical IT incidents in 2018. The average organization records 1,208 IT incidents per month, 5 of which turn out to be critical. In particular, operational intelligence reduces the number of duplicate incidents through machine learning and repeat incidents through improved root cause analysis.
Bob Tarzey
Independent Analyst and Freelance Writer, Quocirca

2018 will see the adoption of AI, in the form of machine learning, by major software vendors who will be embedding it within their core applications. This machine learning will also become a standard platform for data analytics for new development initiatives. The IoT market will take greatest advantage from this adoption, as the volume of data needing analysis grows exponentially.
Sven Hammar
Founder and CSO, Apica

Read Sven Hammar's Blog: What's Ahead for the Software Testing Industry in 2018?

AI

Nearly all IT management product companies are now claiming to be AI driven. Analysts are declaring AI to be a strategic requirement. CIOs are demanding AI products. The market will start to go beyond buzzwords and hype, and focus on how intelligent automation can be used.
Tom Joyce
CEO, Pensa

2017 saw virtual assistants and chatbots popping up a bit more regularly, though mostly confined to the advanced enterprise ITSM and help desk platforms. In 2018 AI-based tools like these will trickle down to more midmarket ITSM products. They'll also be the basis for one or two new ITSM best-of-breed and application startups.
Craig Borowski
Content Analyst, Software Advice (a Gartner Company)

As more enterprises move toward deploying IoT for business applications, AI and machine learning will become imperative, rather than optional. AI will gain more prominence as an enabler of improved ITSM, self-service offerings, and as a necessary element in digital transformation initiatives.
Marcel Shaw
Engineer, Federal Markets, Ivanti

Companies are having trouble keeping up with consumers' desire for innovation. Better, sleeker, faster seems to be in constant demand — and all with a flawless experience. But, old legacy apps weren't built for this modern wave of digital users. They just don't work at speed or scale — at least without performance issues that cause more abandon rates than signups. So, companies are rebuilding their legacy apps on the cloud. But, these rapid changes have given rise to complex IT ecosystems, which make it difficult to monitor digital performance and manage the user-experience effectively — at least by using traditional tools. That's why, in 2018, AI will become critical in IT's ability to master increasing IT complexity in order to deliver on consumer demands. Organizations will look to AI to automate all the heavy lifting and proactively identify problems so that they can pinpoint the underlying root cause of any issues before their customers are impacted.
Alois Reitbauer
Chief Technology Strategist, Dynatrace

Despite the hype, AI has demonstrated value in industries across the board — from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making. AI's success will continue in the new year, specifically in a new area: troubleshooting. Expect to see an impact on troubleshooting for operators, data centers, etc. as AI helps individuals tackle the day-to-day issues, enabling them to focus on critical problems that AI itself can't help. In 2018, AI will guide and augment humans in solving hard problems as it further cements its value-add as a human cognitive partner, guiding us through the trees to make more impactful decisions.
Ash Munshi
CEO, Pepperdata

AIOPS

Continued adoption of machine learning, data science principles, and big data techniques that will improve pattern discovery, anomaly detection and root cause analysis. Because of this, AIOps/ITOA will play a larger role.
David Ishmael
Director of IT Operations Analytics, Trace3

Artificial intelligence will evolve IT by seeing predictive analytics replace manually intensive activities with intelligent automation. This evolution has been coined AIOps. This will allow organizations to leverage data and AI to quickly identify problems, provide recommendations on how to resolve existing issues, streamline automation with self-service and self-recovery capabilities, and predict future outcomes to forecast costs. AIOps will take IT operations analytics (ITOA) to the next level by automatically applying insights to ensure high performing IT environments are proactively making decisions that ultimately improve the health of the business.
Rick Fitz
SVP and GM of IT Markets, Splunk

AI LIMITATIONS

In 2018, we expect to see a growing realization of the limitations of today's AI for IT issue identification and resolution. As the number of performance-impacting elements (and IT complexity) increases, AI can be helpful in identifying some problem spots, but human intervention will always be needed to discern what (if any) issues are truly customer-impacting and thus warrant a call to IT teams in the middle of the night. For example, let's say a front-end server is slowing down. Are customers growing angered? Are revenues in danger? Or can the issue wait until the morning? These are things that a machine can't necessarily learn. AI without guided human intervention can actually have the adverse impact of desensitizing IT staffs and making them less effective.
Mehdi Daoudi
CEO and Founder, Catchpoint

CONVERGENCE OF ITOA AND BI

We expect a convergence of IT Operations Analytics (ITOA) and Business Intelligence (BI), with AI as the bridge. AI allows for the analysis of every metric at the most granular level while still correlating them across disparate data sources. With excessively large amounts of data, traditional dashboards become slow and overwhelming containing many false and missed alerts. The only way to track, learn and derive insights from all of the available data is to use AI. Once you have an AI system evaluating the IT and business metrics, unified alerts can identify true insights, and companies will have access to a "single pane of glass" so that both business and technology executives can have a clear understanding of every aspect of the business.
David Drai
CEO, Anodot

Read 2018 Application Performance Management Predictions - Part 6, covering more about ITOA and data.

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Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

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As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

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Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs Double Down on AI While Navigating Enterprise Hurdles

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2018 Application Performance Management Predictions - Part 5

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 2018. Part 5 covers NoOps, Analytics, Machine Learning and AI.

Start with 2018 Application Performance Management Predictions - Part 1

Start with 2018 Application Performance Management Predictions - Part 2

Start with 2018 Application Performance Management Predictions - Part 3

Start with 2018 Application Performance Management Predictions - Part 4

AUTONOMOUS OPERATIONS: NO OPS

2018 will mark the year of blending APM intelligence, as just another data source, into the ultimate IT business goal: Autonomous Operations, also called NoOps by Forrester. These AI-powered, automated and autonomous systems will automate deployment, monitoring, management, securing and remediation of IT environment. If your current APM solution is not already integrated/capable of integrating into these larger systems, you'll want to use 2018 to get yourself acquainted and start your projects. The future starts now.
Daniel Schrijver
Senior Principal Product Marketing Director, Oracle

NO OPS NO LONGER

"NoOps" will no longer be a thing as infrastructure and operations/run teams become more involved in the development aspects of the software engineering and take back the Ops.
Alex Popov
Cloud Enablement and Continuous Delivery, Barclaycard

MACHINE LEARNING

The New Focus: Proactive, Not Reactive. In today's fast-changing, dynamic virtual environments, IT managers can no longer afford to be reactive or to use trial-and-error to address issues. As 2018 progresses, IT management will be able to take full advantage of the holistic, predictive analytics that new machine-learning based tools enable. These tools can predict and even recommend steps to avoid a variety of issues that can take IT application owners by surprise with costly results. For example, IT can use these tools to eliminate application performance issues, threats to failovers and unexpected capacity usage.
Jerry Melnick
President & CEO, SIOS Technology

New global research from Quocirca, Damage Control: The Impact of Critical IT Incidents, shows that improved operational intelligence, driven by machine data, will continue reduce the impact of critical IT incidents in 2018. The average organization records 1,208 IT incidents per month, 5 of which turn out to be critical. In particular, operational intelligence reduces the number of duplicate incidents through machine learning and repeat incidents through improved root cause analysis.
Bob Tarzey
Independent Analyst and Freelance Writer, Quocirca

2018 will see the adoption of AI, in the form of machine learning, by major software vendors who will be embedding it within their core applications. This machine learning will also become a standard platform for data analytics for new development initiatives. The IoT market will take greatest advantage from this adoption, as the volume of data needing analysis grows exponentially.
Sven Hammar
Founder and CSO, Apica

Read Sven Hammar's Blog: What's Ahead for the Software Testing Industry in 2018?

AI

Nearly all IT management product companies are now claiming to be AI driven. Analysts are declaring AI to be a strategic requirement. CIOs are demanding AI products. The market will start to go beyond buzzwords and hype, and focus on how intelligent automation can be used.
Tom Joyce
CEO, Pensa

2017 saw virtual assistants and chatbots popping up a bit more regularly, though mostly confined to the advanced enterprise ITSM and help desk platforms. In 2018 AI-based tools like these will trickle down to more midmarket ITSM products. They'll also be the basis for one or two new ITSM best-of-breed and application startups.
Craig Borowski
Content Analyst, Software Advice (a Gartner Company)

As more enterprises move toward deploying IoT for business applications, AI and machine learning will become imperative, rather than optional. AI will gain more prominence as an enabler of improved ITSM, self-service offerings, and as a necessary element in digital transformation initiatives.
Marcel Shaw
Engineer, Federal Markets, Ivanti

Companies are having trouble keeping up with consumers' desire for innovation. Better, sleeker, faster seems to be in constant demand — and all with a flawless experience. But, old legacy apps weren't built for this modern wave of digital users. They just don't work at speed or scale — at least without performance issues that cause more abandon rates than signups. So, companies are rebuilding their legacy apps on the cloud. But, these rapid changes have given rise to complex IT ecosystems, which make it difficult to monitor digital performance and manage the user-experience effectively — at least by using traditional tools. That's why, in 2018, AI will become critical in IT's ability to master increasing IT complexity in order to deliver on consumer demands. Organizations will look to AI to automate all the heavy lifting and proactively identify problems so that they can pinpoint the underlying root cause of any issues before their customers are impacted.
Alois Reitbauer
Chief Technology Strategist, Dynatrace

Despite the hype, AI has demonstrated value in industries across the board — from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making. AI's success will continue in the new year, specifically in a new area: troubleshooting. Expect to see an impact on troubleshooting for operators, data centers, etc. as AI helps individuals tackle the day-to-day issues, enabling them to focus on critical problems that AI itself can't help. In 2018, AI will guide and augment humans in solving hard problems as it further cements its value-add as a human cognitive partner, guiding us through the trees to make more impactful decisions.
Ash Munshi
CEO, Pepperdata

AIOPS

Continued adoption of machine learning, data science principles, and big data techniques that will improve pattern discovery, anomaly detection and root cause analysis. Because of this, AIOps/ITOA will play a larger role.
David Ishmael
Director of IT Operations Analytics, Trace3

Artificial intelligence will evolve IT by seeing predictive analytics replace manually intensive activities with intelligent automation. This evolution has been coined AIOps. This will allow organizations to leverage data and AI to quickly identify problems, provide recommendations on how to resolve existing issues, streamline automation with self-service and self-recovery capabilities, and predict future outcomes to forecast costs. AIOps will take IT operations analytics (ITOA) to the next level by automatically applying insights to ensure high performing IT environments are proactively making decisions that ultimately improve the health of the business.
Rick Fitz
SVP and GM of IT Markets, Splunk

AI LIMITATIONS

In 2018, we expect to see a growing realization of the limitations of today's AI for IT issue identification and resolution. As the number of performance-impacting elements (and IT complexity) increases, AI can be helpful in identifying some problem spots, but human intervention will always be needed to discern what (if any) issues are truly customer-impacting and thus warrant a call to IT teams in the middle of the night. For example, let's say a front-end server is slowing down. Are customers growing angered? Are revenues in danger? Or can the issue wait until the morning? These are things that a machine can't necessarily learn. AI without guided human intervention can actually have the adverse impact of desensitizing IT staffs and making them less effective.
Mehdi Daoudi
CEO and Founder, Catchpoint

CONVERGENCE OF ITOA AND BI

We expect a convergence of IT Operations Analytics (ITOA) and Business Intelligence (BI), with AI as the bridge. AI allows for the analysis of every metric at the most granular level while still correlating them across disparate data sources. With excessively large amounts of data, traditional dashboards become slow and overwhelming containing many false and missed alerts. The only way to track, learn and derive insights from all of the available data is to use AI. Once you have an AI system evaluating the IT and business metrics, unified alerts can identify true insights, and companies will have access to a "single pane of glass" so that both business and technology executives can have a clear understanding of every aspect of the business.
David Drai
CEO, Anodot

Read 2018 Application Performance Management Predictions - Part 6, covering more about ITOA and data.

Related Links

Hot Topics

The Latest

Cloud Backup as a Business Asset: Why Your Backup Strategy Needs to Evolve

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private Cloud Outlook 2025: A Definitive Cloud Reset

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

Two-Thirds of Organizations Significantly at Risk of Software Outage within Next Year

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

MongoDB's Market Woes Shed Light on Hard Truths About Vendor Lock-in

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

MEAN TIME TO INSIGHT Podcast - Episode 15: DIY Network Automation

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Checkmate VPN Intruders with Personal SASE Service

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Observability 2.0: The Convergence of AI, OpenTelemetry, and Unified Data

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

Challenges of DIY Network Automation

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Best Practices for Enterprises to Reduce Cloud Costs

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs Double Down on AI While Navigating Enterprise Hurdles

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

IBM