2019 Application Performance Management Predictions - Part 5
December 19, 2018
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APMdigest invited industry experts — from analysts and consultants to users and the top vendors — to predict how APM and related technologies will evolve and impact business in 2019. Part 5 covers the evolution of IT Operations Analytics and its impact on the IT team.

Start with 2019 Application Performance Management Predictions - Part 1

Start with 2019 Application Performance Management Predictions - Part 2

Start with 2019 Application Performance Management Predictions - Part 3

Start with 2019 Application Performance Management Predictions - Part 4


For 2019, we can expect to see continuous innovation for root-cause automation — Keeping mean time to resolution (MTTR) low will always be a priority, and as such, we can expect the drive to improve automated anomaly detection, machine learning, smart algorithms, and intelligent baselining will increase in 2019.
Denis Goodwin
Director of Product Management, AlertSite by SmartBear


With the inception and deployment of advanced algorithms and machine learning into the monitoring market, we are seeing a greater need for specific information and ultimately better information to make smarter decisions on the performance and delivery of services and applications. Prediction: Over the next 2 years customers will demand and vendors will provide systems that are more "opinionated" that move away from simple signal and alerts and move to compound alerts that explain what happened and what someone can do about it. Through machine learning and SaaS-driven tools customers will deliver a strong network effect that leverages newly learned patterns from a large number of customers, basically learning problems from other customers, and providing value directly to the user.
Gadi Oren
VP of Products, LogicMonitor


Today's applications (and by extension the APM tools) are sitting on a proverbial iceberg, on a substrate built of complex layers of abstraction — orchestrators like Kubernetes, service meshes, containers, functions, VMs and clouds. Any of these layers could be the reason behind a problem — causing an outage, lost revenue and immediate damage to a brand. To head off this danger, APM must go deep and provide correlation up and down the infra and app stack. The winning solutions in 2019 will go one step further, guiding the user towards quicker root cause analysis and lower MTTR by data mining the telemetry. No company can afford to find out there's an outage from customers complaining on Twitter.
Arijit Mukherji
CTO, SignalFx


AIaaS (AI as a Service) offerings, especially at targeted verticals, will flood the market in 2019 giving businesses more options for finding answers. Identifying the right data will remain a significant challenge. Successful offerings will help customers validate and prioritize the insights provided.
Andi Mann
Chief Technology Advocate, Splunk


In 2019, we will see the emergence and increased implementation of industry and use-case specific specialized ML models. These will be able to provide simple solutions for complex problems: Instead of repeating the intricate process of training new models, users will merely point to existing data sources to begin receiving predictions via pre-trained models. This increasingly specialized system has the ability to enable business decisionmakers to gain insights more efficiently and more intelligently.
Saurabh Dutta
Product Manager, Big Data Analytics Practice, Impetus Technologies


In 2019, IT Service Management (ITSM) will place more emphasis on self-service by integrating artificial intelligence (AI) into self-service functionality. AI powered chatbots integrated with advanced searching capabilities will eliminate most front-line calls typically handled by an analyst in today's traditional help desk solutions. ITSM powered with AI will lead to the formation of Artificially Intelligent Service Management (AISM) as a commonly implemented enhancement to traditional ITSM. Eventually, virtual assistants powered by AI will serve as the only interface between end users and service delivery.
Marcel Shaw
Engineer, Federal Markets, Ivanti


The speed automation that is needed outruns most enterprises' abilities to generate usable training data for AI and ML. Data from a week ago, and the patterns it shows, is worthless in today's fast-paced IT world. Yet so often, week-old data seems to be the standard for training AI and ML devices. Enterprises are waking up to the fact that historical data is useless as the pace of business accelerates and requires timely inputs. If enterprises are going to get serious about AI, ML and automation in a rapidly-changing IT landscape, they are going to need training data that is contextualized and real-time.
Erik Rudin
VP of Business Development and Allian, ScienceLogic


AI will augment — not replace — the workforce. AI applications will be transformational, improving efficiency and performance, generating huge cost savings, and giving rise to more innovative products and services. However, the future of work will involve humans and AI. The most innovative companies have already started planning how to best make this symbiotic future a reality.
Zachary Jarvinen
Head of Technology Strategy, AI and Analytics, OpenText


The data skills gap will increase — but so will data literacy: Data is both the problem and the answer for businesses. It's a problem because businesses manage to collect more data than they know how to use, yet it's the answer because it can predict forecasts and offer insight into how the business should run. The next year will see the data skills gap continue to increase — users need to be able to analyze properly where data comes from and how to use it, and it only gets more complicated as more data is made available and as algorithms enter the fray. But at the same time, business users will also grow more data literate as they seek to approach data as a team, and help one another get what they need from their data.
Laurent Bride
CTO, Talend

In the shorter term, the gap in skilled workers in data science will persist and demand will remain very high. Educational opportunities will expand to help address the need.
Zachary Jarvinen
Head of Technology Strategy, AI and Analytics, OpenText


Data scientists become more valuable than the ITOps generation. Good IT operations manager, systems admins and developers are all worth their weight in gold. But come 2019, organizations will find data scientists worth their weight in diamonds. They'll be the ones taking data from IT operations and developing the AI and ML use cases that will drive automation and, in turn, business value.
Antonio Piraino
CTO, ScienceLogic


As AI continues to disrupt industries that have been static for 50 years, we will see a critical need for data experts who can understand and apply data in a meaningful way — specifically to machine learning models. This will lead to a data scientist skill-set shortage. Overall demand for data scientists is growing, yet many organizations are having a difficult time finding enough qualified candidates — much like the Java Programmer of the early 2000s. As a result, today's organizations will look inside and start training their engineers on data science and machine learning. Call it the 2019 “Rise of the Machine Learning Engineer.” Data science is not a unicorn-like, mysterious field of study. Engineers can learn standard technology skills and apply them to machine learning. Specialized training and boot camps can deliver proficiency within a week, as it's less about formal training and more about applying skills to a specific domain or use case. The machine learning engineer is the master classman when it comes to specialized techniques, supervised learning and domain expertise. This expert will apply his/her training to analyze, understand and apply data in meaningful ways, fulfilling many of the actual duties of a data scientist.
Asim Razzaq
CEO, YotaScale


The explosion of artificial intelligence (AI) within IT is poised to provide many benefits and time-saving opportunities in 2019 but will require IT decision-makers (ITDMs) to evolve into strategic consultants rather than serving in reactive roles. AI will not replace the entire IT team overnight, nor will it get close any time soon due to the current applications of the technology. However, as AI starts to erode the need for humans in the IT helpdesk, we will see those ITDMs that wish to survive do what they should be doing anyway — grow, expand into higher value areas and maintain a close relationship with the business. Failing to evolve into this strategic leadership position will lead to ITDMs' extinction.
Ian Pitt
CIO, LogMeIn


The collection of data to support humans and algorithms continues and raises important ethical questions and is something we need to pay close attention to over the next few years. Data is human and therefore is just as messy as humans. Data does not create objectivity. It is well established that data and algorithms perpetuate existing biases and automated decisions are — at best — difficult to explain and justify. Appealing such decisions is even harder when we fall into the trap of thinking data and algorithms combine to create objective truth. With greater decision-making power comes much greater responsibility, and humans will increasingly be held accountable for the impact of decisions their business makes.
Christian Beedgen
CTO, Sumo Logic

Questions around data morality will slow innovation in AI/ML: The past year has seen the hype around AI/ML explode, and data ethics, trust, bias and fairness have all surfaced to combat inequalities in the process to make everything intelligent. There are many layers to data morality, and while ML advancements won't cease — they'll slow down in 2019 as researchers try to hash out a fair, balanced approach to machine-made decisions.
Laurent Bride
CTO, Talend

Read 2019 Application Performance Management Predictions - Part 6, covering IoT.

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