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.
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.
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.
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.
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.
GM of North America, Bizagi
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.
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.
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.
VP of Cloud Solutions, Astadia
Read Next Steps for ITOA - Part 5, offering some interesting final thoughts.
The Internet played a greater role than ever in supporting enterprise productivity over the past year-plus, as newly remote workers logged onto the job via residential links that, it turns out, left much to be desired in terms of enabling work ...
The world's appetite for cloud services has increased but now, more than 18 months since the beginning of the pandemic, organizations are assessing their cloud spend and trying to better understand the IT investments that were made under pressure. This is a huge challenge in and of itself, with the added complexity of embracing hybrid work ...
After a year of unprecedented challenges and change, tech pros responding to this year’s survey, IT Pro Day 2021 survey: Bring IT On from SolarWinds, report a positive perception of their roles and say they look forward to what lies ahead ...
One of the key performance indicators for IT Ops is MTTR (Mean-Time-To-Resolution). MTTR essentially measures the length of your incident management lifecycle: from detection; through assignment, triage and investigation; to remediation and resolution. IT Ops teams strive to shorten their incident management lifecycle and lower their MTTR, to meet their SLAs and maintain healthy infrastructures and services. But that's often easier said than done, with incident triage being a key factor in that challenge ...
Achieve more with less. How many of you feel that pressure — or, even worse, hear those words — trickle down from leadership? The reality is that overworked and under-resourced IT departments will only lead to chronic errors, missed deadlines and service assurance failures. After all, we're only human. So what are overburdened IT departments to do? Reduce the human factor. In a word: automate ...
On average, data innovators release twice as many products and increase employee productivity at double the rate of organizations with less mature data strategies, according to the State of Data Innovation report from Splunk ...
While 90% of respondents believe observability is important and strategic to their business — and 94% believe it to be strategic to their role — just 26% noted mature observability practices within their business, according to the 2021 Observability Forecast ...
Let's explore a few of the most prominent app success indicators and how app engineers can shift their development strategy to better meet the needs of today's app users ...
Business enterprises aiming at digital transformation or IT companies developing new software applications face challenges in developing eye-catching, robust, fast-loading, mobile-friendly, content-rich, and user-friendly software. However, with increased pressure to reduce costs and save time, business enterprises often give a short shrift to performance testing services ...
DevOps, SRE and other operations teams use observability solutions with AIOps to ingest and normalize data to get visibility into tech stacks from a centralized system, reduce noise and understand the data's context for quicker mean time to recovery (MTTR). With AI using these processes to produce actionable insights, teams are free to spend more time innovating and providing superior service assurance. Let's explore AI's role in ingestion and normalization, and then dive into correlation and deduplication too ...