Next Steps for ITOA - Part 4
May 19, 2017
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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.

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