Today’s incident pipelines are noisy. The average enterprise deals with at least 15 different monitoring and observability tools that create thousands of alerts a day, often overwhelming and drowning their IT operations. But it’s not just their number that’s an issue.
The world is moving fast, led by an ever-accelerating IT landscape. In recent years, two distinct types of teams have emerged that assist in driving this business transformation: DevOps/SRE teams that are in charge of driving rapid innovation of products and services, and IT Ops/NOC teams that focus on preventing outages and maintaining the high level of quality, reliability and serviceability that modern, discerning customers expect.
James Beard, the pioneer of television cooking shows, once asked, “Where would we be without salt?”. Salt is often underrated, even though it is the ingredient that has the greatest impact on food and flavor in the modern world. It has its own taste, but also balances and enhances the flavor of other ingredients. Salt boosts sweetness and blocks bitterness, it has scientifically proven capabilities to intensify flavor compounds that are too subtle to detect (i.e.
Our human capacity for ingesting information and acting on it, is constant. As the systems we operate grow more complex, we need to make sure we use technology that presents us with only the relevant information we need, exactly when we need it. In aviation, this lesson was learned long ago, and now IT Ops is catching up.
With so many IT vendors claiming they provide AIOps platforms, how do you understand the differences between them, and decide what flavor of AIOPs to choose for your organization? Join us in a CTO Perspective discussion with Elik Eizenberg, CTO and co-founder at BigPanda, to find the answer. Read the skinny for a brief summary, then either lean back and watch the interview, or if you prefer to continue reading, take a few minutes to read the transcript. Enjoy!
Coined by Gartner in 2016, the term ‘AIOps’ refers to the combining of big data AI and machine learning to automate and improve IT operations processes. Back then, this very broad definition led to some confusion, with different IT vendors characterizing AIOps differently, depending on what they were actually offering.
Today’s IT landscape is complex, hybrid, and fast-moving, and the adoption of multi-cloud infrastructure, applications, and new digital transformation initiatives is accelerating. IT operations teams, playing a vital role in enabling the delivery of uninterrupted services and creating business value for enterprises, are finding they need to constantly grow their resources to manage all the moving pieces in their IT stack. This can get expensive … but how much are they spending?
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.
It has been eight years since The Phoenix Project was published and a lot has changed since then! I started to think about what we’ve learned in that time. It starts with the theory of constraints. I still see it all the time. Organizations take actions which are merely temporary, putting out fires but not solving for the underlying causes of those fires.
Low MTTR is the much-desired nirvana-state in IT Operations. One of the most painful parts of the incident management lifecycle, which prevents the achievement of this nirvana, is triage: the time it takes first incident responders to determine the next action when facing a barrage of IT incidents. Why?
