Bringing Alert Management into the Present with Advanced Analytics
March 25, 2015

Kevin Conklin
Ipswitch

Share this

We have smart cars on the horizon that will navigate themselves. Mobile apps that make communication, navigation and entertainment an integral part of our daily lives. Your insurance pricing may soon be affected by whether or not you wear a personal health monitoring device. Everywhere you turn, the very latest IT technologies are being leveraged to provide advanced services that were unimaginable even ten years ago. So why is it that the IT environments that provide these services are managed using an analytics technology designed for the 1970s?

The IT landscape has evolved significantly over the past few decades. IT management simply has not kept pace. IT operations teams are anxious that too many problems are reported first by end users. Support teams worry that too many people spend too much time troubleshooting. Over 70 percent of troubleshooting time is actually wasted following false hunches because alerts provide no value to the diagnostic process. Enterprises that are still reliant on yesterday’s management strategies will find it increasingly difficult to solve today’s operations and performance management challenges.

This is not just an issue of falling behind a technology curve. There is a real business impact in increasing incident rates, failing to detect potentially disastrous outages and human resources wasting valuable time. An increasing number of IT shops are anxiously searching for alternatives.

This is where advanced machine learning analytics can help.

Too often operations teams can become engulfed by alerts – getting tens of thousands a day and not knowing which to deal with and when, making it quite possible that something important was ignored while time was wasted on something trivial. Through a powerful combination of machine learning and anomaly detection, advanced analytics can reduce the alarms to a prioritized set that have the largest impact on the environment. By learning which alerts are “normal”, these systems define an operable status quo. In essence, machine learning filters out the “background noise” of alerts that, based on their persistence, have no effect on normal operations. From there, statistical algorithms identify and rank “abnormal” outliers on a scale measuring severity (value of a spike or drop occurrence), rarity (number of previous instances) or impact (quantity of related anomalies). The result is a reduction from hundreds of thousands of noisy alerts a week to a few dozen notifications of real problems.

Despite producing huge volumes of alerts, rules and thresholds implementations often miss problems or report them long after the customer has experienced the impact. The fear of generating even more alerts forces monitoring teams to select fewer KPIs, thus decreasing the likelihood of detection. Problems that slowly approach thresholds go unnoticed until user experience is already impacted. Adopting this advanced analytics approach empowers enterprises to not only identify problems that rules and thresholds miss or simply execute against too late, but also provide their troubleshooting teams with pre-correlated causal data.

By replacing legacy rules and thresholds with machine learning anomaly detection, IT teams can monitor larger sets of performance data in real-time. Monitoring more KPIs enable a higher percentage of issues to be detected before the users report them. Through real-time cross correlation, related anomalies are detected and alerts become more actionable. Early adopters report that they are able to reduce troubleshooting time by 75 percent, with commensurate reductions in the number of people involved by as much as 85 percent.

Advanced machine learning systems will fundamentally change the way data is converted into information over the next few years. If your business is leveraging information to provide competitive services, you can’t afford to be the laggard.

Kevin Conklin is VP of Product Marketing at Ipswitch
Share this

The Latest

September 21, 2017

The increased complexity of new computing architectures coupled with new application development methodologies – especially in the face of time-to-market and security threat pressures – should make secure UX the first strategic decision for CEOs and CFOs on the path to digital transformation ...

September 19, 2017

IT professionals tend to go above and beyond the scope of their core responsibilities as the changing business landscape demands more of their attention, both inside and outside of the office, according to the Little-Known Facts survey conducted by SolarWinds in honor of IT Professionals Day ...

September 18, 2017

Digital video consumption is viral and, according to a new study released by IBM and International Broadcasting Convention (IBC), more than half of the 21,000 consumers surveyed are using mobiles every day to watch streaming videos, and that number is expected to grow 45 percent in the next three years ...

September 15, 2017

No technology that touches more than one IT stakeholder, no matter how good and how transformative, can deliver its potential without attention to leadership, process considerations and dialog. In this blog, I'd like to share effective strategies for AIA adoption ...

September 14, 2017

Enterprise IT environments are becoming more heterogeneous and complex, with fragmentation permeating cloud infrastructure, tooling and culture, according to a survey recently conducted by IOD Cloud Technologies Research in partnership with Cloudify ...

September 12, 2017

One area that enables enterprises to reduce complexity and streamline operations is their virtual desktop infrastructure (VDI). Virtualization is a linchpin of digital transformation and effectively optimizing an enterprise's VDI is essential to moving forward with digital technologies. Delivering the best possible VDI performance means taking a fresh look at what "desktop" means today. The endpoint, or desktop, now can be a physical thin client, a software-defined thin client, a traditional laptop, a phone or tablet. To reduce operational waste and achieve better performance across the desktop environment, consider these five actions ...

September 11, 2017

In incident management, we often overlook the simple things in favor of trying to do too much, too soon. Why not make sure we've done the fundamentals properly? ...

September 08, 2017
For our Advanced IT Analytics (AIA) Buyer's Guide, we interviewed more than 20 deployments to help us better assess vendor strengths and limitations. So given the abundance of riches to work with, I've decided to illustrate several of the more prominent AIA benefit categories with actual real-world comments ...
September 07, 2017

The Input/Output Operations per Second (I/O) capabilities of modern computer systems are truly a modern wonder. Yet no matter how powerful the processors, no matter how many cores, how perfectly formed the bus architecture, or how many flash modules are added, somehow it never seems to be enough ...

September 06, 2017

By taking advantage of performance monitoring, IT and business decision makers can gain better visibility into their cloud and application performance. Dedicated performance monitoring has become essential for providing visibility into all areas of application performance and keeping the business running optimally ...