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Next Steps for ITOA - Part 4

APMdigest asked experts across the industry — including analysts, consultants and vendors — for their opinions on the next steps for ITOA. These next steps include where the experts believe ITOA is headed, as well as where they think it should be headed. Part 4 covers automation and dynamic IT environment.

Start with Next Steps for ITOA - Part 1

Start with Next Steps for ITOA - Part 2

Start with Next Steps for ITOA - Part 3

AUTOMATED PROBLEM DETECTION AND RESOLUTION

ITOA is following a general progression: from providing better visibility into the operational environment, to identifying root causes of issues, to predicting issues before they occur, to automatically preventing issues. The current state of the art today centers on predictive analytics, as machine learning and other AI approaches are particularly useful for this task. Automatic prevention of issues, especially in complex enterprise environments, is still largely in the future – but given the pace of innovation today, it's right around the corner.
Jason Bloomberg
President, Intellyx

Automating many of the back end processes developed around IT Infrastructure Library (ITIL) using IT Operations Analytics to speed up time to value will be the next step for ITOA. It is no longer about analyzing the data – it is how you automate out obstacles for the Software Defined Datacenter of tomorrow.
Jeanne Morain
Author and Strategist, iSpeak Cloud

Fueled by the learning and interpretation of operational data, intelligent automation and self-healing systems will become predominant questions for companies faced to an explosion of the number of physical and virtual devices devices (ITaaS, IoT). Those topics will prevail, not only because of major improvements in the accuracy of algorithms, but simply because of the impossibility for humans to manage booming volume of IT Ops information.
Yann Guernion
Product Marketing Director, Workload Automation, Automic Software

With the development of big data and AI techniques specific to IT operations, we can automate detection and repair of IT problems *before* they occur – a huge step towards the "holy grail" of 100% uptime.
Kimberley Parsons Trommler
Product Evangelist, Paessler AG

Performance management is important, but can be a struggle when keeping up with new technologies and constantly growing system complexities. The only viable solution over time is to extend the use of automation. Using operation analytics to create baselines and leveraging big data analytics to help detect anomalies and prevent incidents ensures that when incidents occur you have automatically gathered all relevant information to determine the root cause.
Sven Hammar
Founder and CEO, Apica

DIGITAL PROCESS AUTOMATION

Digital process automation (DPA) is fast emerging as the next step in the evolution of IT Operation Analytics. DPA allows for far closer collaboration between business and IT to map core operations and business transactions, providing greater data analytics and insight. As a result, DPA empowers the enterprise to be more responsive to customers, deliver products to market faster and provide an enhanced customer experience. Arming workers with the right data at the right time, and in the specific context of that business moment, helps them do their jobs more effectively and to respond to the changing needs of digitally-savvy consumers in near to real time.
Rich Fitchen
GM of North America, Bizagi

DEEP LEARNING

The volume, variety, and velocity of changes in telemetry data, technology, and processes will drive ITOA evolution towards deep learning. For context, artificial intelligence is exemplified by knowledge bases, while machine learning focuses the knowledge via logical regression, and deep learning moves into the realm of artificial neural networks, also known as multilayer perceptrons (MLP). The next two years will be a race to discover deep learning models that will enable ITOA automation and orchestration that optimize any organization's application stack to meet their customers' on-demand needs regardless of the consumption platform, delivery model, or rate and size of consumption. The end goal remains the same: delivering frictionless consumption for an organization's revenue-generating and revenue-supporting application services. In this journey, "monitoring with discipline" will play a key role in determining the efficiency and the effectiveness of the deep learning ITOA models towards that goal.
Kong Yang
Head Geek, SolarWinds

FLEXIBILITY AND RAPID CONFIGURATION

Real time data from advances like IoT and machine learning to artificial intelligence offer a lot of potential to make ITOA a force for improving the customer experience. However, collecting, organizing and making sense of the deluge of data from such disparate sources and executing against the insights they provide, is going to require a level of flexibility that will tax traditional IT systems. Platforms that can be rapidly configured to changing business needs will emerge.
Colin Earl
CEO, Agiloft

CONTAINERS AND MICROSERVICES

With more and more applications running in containers or microservices, IT Operations Analytics (ITOA) becomes more important to be able to process the constantly changing nature of these services. ITOA also enables us to truly understand the service dependencies, detect anomalies, and generate appropriate alerts or even self-healing fixes that can maximize uptime for the services and minimize the time requirements for IT staff.
Steve Lack
VP of Cloud Solutions, Astadia

Read Next Steps for ITOA - Part 5, offering some interesting final thoughts.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

Next Steps for ITOA - Part 4

APMdigest asked experts across the industry — including analysts, consultants and vendors — for their opinions on the next steps for ITOA. These next steps include where the experts believe ITOA is headed, as well as where they think it should be headed. Part 4 covers automation and dynamic IT environment.

Start with Next Steps for ITOA - Part 1

Start with Next Steps for ITOA - Part 2

Start with Next Steps for ITOA - Part 3

AUTOMATED PROBLEM DETECTION AND RESOLUTION

ITOA is following a general progression: from providing better visibility into the operational environment, to identifying root causes of issues, to predicting issues before they occur, to automatically preventing issues. The current state of the art today centers on predictive analytics, as machine learning and other AI approaches are particularly useful for this task. Automatic prevention of issues, especially in complex enterprise environments, is still largely in the future – but given the pace of innovation today, it's right around the corner.
Jason Bloomberg
President, Intellyx

Automating many of the back end processes developed around IT Infrastructure Library (ITIL) using IT Operations Analytics to speed up time to value will be the next step for ITOA. It is no longer about analyzing the data – it is how you automate out obstacles for the Software Defined Datacenter of tomorrow.
Jeanne Morain
Author and Strategist, iSpeak Cloud

Fueled by the learning and interpretation of operational data, intelligent automation and self-healing systems will become predominant questions for companies faced to an explosion of the number of physical and virtual devices devices (ITaaS, IoT). Those topics will prevail, not only because of major improvements in the accuracy of algorithms, but simply because of the impossibility for humans to manage booming volume of IT Ops information.
Yann Guernion
Product Marketing Director, Workload Automation, Automic Software

With the development of big data and AI techniques specific to IT operations, we can automate detection and repair of IT problems *before* they occur – a huge step towards the "holy grail" of 100% uptime.
Kimberley Parsons Trommler
Product Evangelist, Paessler AG

Performance management is important, but can be a struggle when keeping up with new technologies and constantly growing system complexities. The only viable solution over time is to extend the use of automation. Using operation analytics to create baselines and leveraging big data analytics to help detect anomalies and prevent incidents ensures that when incidents occur you have automatically gathered all relevant information to determine the root cause.
Sven Hammar
Founder and CEO, Apica

DIGITAL PROCESS AUTOMATION

Digital process automation (DPA) is fast emerging as the next step in the evolution of IT Operation Analytics. DPA allows for far closer collaboration between business and IT to map core operations and business transactions, providing greater data analytics and insight. As a result, DPA empowers the enterprise to be more responsive to customers, deliver products to market faster and provide an enhanced customer experience. Arming workers with the right data at the right time, and in the specific context of that business moment, helps them do their jobs more effectively and to respond to the changing needs of digitally-savvy consumers in near to real time.
Rich Fitchen
GM of North America, Bizagi

DEEP LEARNING

The volume, variety, and velocity of changes in telemetry data, technology, and processes will drive ITOA evolution towards deep learning. For context, artificial intelligence is exemplified by knowledge bases, while machine learning focuses the knowledge via logical regression, and deep learning moves into the realm of artificial neural networks, also known as multilayer perceptrons (MLP). The next two years will be a race to discover deep learning models that will enable ITOA automation and orchestration that optimize any organization's application stack to meet their customers' on-demand needs regardless of the consumption platform, delivery model, or rate and size of consumption. The end goal remains the same: delivering frictionless consumption for an organization's revenue-generating and revenue-supporting application services. In this journey, "monitoring with discipline" will play a key role in determining the efficiency and the effectiveness of the deep learning ITOA models towards that goal.
Kong Yang
Head Geek, SolarWinds

FLEXIBILITY AND RAPID CONFIGURATION

Real time data from advances like IoT and machine learning to artificial intelligence offer a lot of potential to make ITOA a force for improving the customer experience. However, collecting, organizing and making sense of the deluge of data from such disparate sources and executing against the insights they provide, is going to require a level of flexibility that will tax traditional IT systems. Platforms that can be rapidly configured to changing business needs will emerge.
Colin Earl
CEO, Agiloft

CONTAINERS AND MICROSERVICES

With more and more applications running in containers or microservices, IT Operations Analytics (ITOA) becomes more important to be able to process the constantly changing nature of these services. ITOA also enables us to truly understand the service dependencies, detect anomalies, and generate appropriate alerts or even self-healing fixes that can maximize uptime for the services and minimize the time requirements for IT staff.
Steve Lack
VP of Cloud Solutions, Astadia

Read Next Steps for ITOA - Part 5, offering some interesting final thoughts.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...