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.
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.
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.
Cloud Enablement and Continuous Delivery, Barclaycard
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.
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.
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.
Founder and CSO, Apica
Read Sven Hammar's Blog: What's Ahead for the Software Testing Industry in 2018?
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.
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.
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.
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.
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.
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.
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.
SVP and GM of IT Markets, Splunk
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.
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.
Read 2018 Application Performance Management Predictions - Part 6, covering more about ITOA and data.
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