Analytics
Monitoring and observability requirements are continuing to adapt to the rapid advances in public cloud, containers, serverless, microservices, and DevOps and CI/CD practices. As new technology and development processes become mainstream, enterprise adoption begins to increase, bringing its own set of security, scalability, and manageability needs. I sat down with Stephen Elliot, VP of Management Software and DevOps at IDC, to discuss where the market is headed, how legacy vendors will need to adapt, and how customers can get ahead of these trends to gain a competitive advantage ...
APMdigest invited industry experts to predict how APM and related technologies will evolve and impact business in 2019. Part 5 covers the evolution of ITOA and its impact on the IT team ...
APMdigest invited industry experts to predict how APM and related technologies will evolve and impact business in 2019. Part 4 covers IT Operations Analytics including Machine Learning and AI ...
We all know artificial intelligence (AI) is a hot topic — but beyond the buzzword, have you ever wondered how IT departments are actually adopting AI technologies to improve on their operations? ...
How can IT teams focus on the critical events that can impact their business instead of wading through false positives? The emerging discipline of AIOps is a much-needed panacea for detecting patterns, identifying anomalies, and making sense of alerts across hybrid infrastructure ...
In a recent webinar AIOps and IT Analytics at the Crossroads, I was asked several times about the borderline between AIOps and monitoring tools — most particularly application performance monitoring (APM) capabilities. The general direction of the questions was — how are they different? Do you need AIOps if you have APM already? Why should I invest in both? ...
We're seeing artificial intelligence for IT operations or "AIOps" take center stage in the IT industry. If AIOps hasn't been on your horizon yet, look closely and expect it soon. So what can we expect from automation and AIOps as it becomes more commonplace? ...
Use of artificial intelligence (AI) in digital commerce is generally considered a success, according to a survey by Gartner, Inc. About 70 percent of digital commerce organizations surveyed report that their AI projects are very or extremely successful ...
Most organizations are adopting or considering adopting machine learning due to its benefits, rather than with the intention to cut people’s jobs, according to the Voice of the Enterprise (VoTE): AI & Machine Learning – Adoption, Drivers and Stakeholders 2018 survey conducted by 451 Research ...
AI (Artificial Intelligence) and ML (Machine Learning) are the number one strategic enterprise IT investment priority in 2018 (named by 33% of enterprises), taking the top spot from container management (28%), and clearly leaving behind DevOps pipeline automation (13%), according to new EMA research ...
Retailers that have readily adopted digital technologies have experienced a 6% CAGR revenue growth over a 3-year period, while other retailers that have explored digital without a full commitment to broad implementation experienced flat growth over the same period ...
The goal of EMA's latest research was to look at how advanced IT analytics (AIA) — EMA's term for primarily what today is best known as "AIOps" — is being deployed. Here are the remaining four of my seven personal takeaways ...
OK, the data is in! The goal of EMA's latest research was to look at how advanced IT analytics (AIA) — or EMA's term for primarily what today is best known as "AIOps" — is being deployed. Here are seven of my own personal takeaways ...
This question is really two questions. The first would be: What's really going on in terms of a confusion of terms? — as we wrestle with AIOps, IT Operational Analytics, big data, AI bots, machine learning, and more generically stated "AI platforms" (… and the list is far from complete). The second might be phrased as: What's really going on in terms of real-world advanced IT analytics deployments — where are they succeeding, and where are they not? This blog will look at both questions as a way of introducing EMA's newest research with data ...
Here's the problem: IT teams are in the dark. The only information they have available to them is based on what users decide to tell them about through calls to the help desk ...
Modern enterprise growth is heavily reliant on an organization's ability to assess past IT events to then look forward, anticipate and prevent service failures from happening. This is the crux of predictive analytics. Today's fast-moving enterprises have data and expertise locked up in siloed organizations, making it difficult to extract actionable insights, which inevitably impacts the scale, size and speed of a company's growth. The following are steps to build the best predictive analytics team ...
The essential value resulting from data-driven processes has become progressively linked with analytics. Once considered a desired complement to intuitive decision-making, analytics has developed into a main focus of mission-critical applications across industries for any number of use cases ...
Global business value derived from artificial intelligence (AI) is projected to total $1.2 trillion in 2018, an increase of 70 percent from 2017, according to Gartner, Inc. AI-derived business value is forecast to reach $3.9 trillion in 2022.
Imagine this: after a fantastic night's sleep, you walk into the office ready to attack the day. You sit down at your desk ready to go, and your computer starts acting up. You call the help desk, but all IT can do is create a ticket for you and transfer it to another team to help you as soon as possible ...
APMdigest asked experts from across the IT industry for their opinions on the essential tools to support digital transformation. Part 3 covers analytics, AI and machine learning ...
If IT professionals want to be instrumental to their organization's successful digital transformation journey, they should continue prioritizing a hybrid IT environment while simultaneously developing new skillsets and leveraging emerging technologies. To help IT professionals arm themselves with a new set of skills, technologies, and resources to bridge the leadership gap and manage the intersection of hype and performance, consider the following recommendations ...
In the time it takes you to read this sentence, a website will lose 53% of its visitors if the page hasn't loaded. This confirms what most of us have known for a while: IT performance is more important than ever in today's digital economy. It's crucial to an organization's bottom line, as high bounce rates can result in lost money, while improved performance can save time and money ...
When will we be at a point where virtually all enterprise workloads are run in the cloud and how will that change things for IT? To find out, we commissioned a survey, Cloud Vision 2020: The Future of the Cloud. The results were fascinating. I'll share three fundamental lessons we learned in the survey ...
The deluge of data created by IT infrastructures that generate data every second often takes great investments of time to make sense of. Leveraging AI or machine learning technologies in Application Performance Management (APM) simplifies the complex IT systems, automates application-environment discovery and makes for smarter decisions faster with proactive problem resolution. The benefits of AI-driven APM solutions seem obvious but raise important questions around AI technologies and its greater impact ...
Businesses everywhere continually strive for greater efficiency. By way of illustration, more than a third of IT professionals cite "moving faster" as their top goal for 2018, and improving the efficiency of operations was one of the top three stated business objectives for organizations considering digital transformation initiatives ...
