Dealing with IT outages and downtime is one of the biggest technical challenges of the modern era, costing North American businesses an estimated $700 billion per year. Today's world of interconnected cloud services and microservice architectures has created infinitely more opportunities for something to go wrong and disrupt service. When that happens, there's an urgent need to alert the right people or teams to fix things.
During our deployments we come across all kinds of different organizational infrastructures. Importing users from the Active Directory is a key component to populating user information into Enterprise Alert. Enterprise Alert will only import users that are contained in security groups. However, we often see companies having users placed in distribution lists. Enterprise Alert will not import distribution lists.
It is 5 a.m. Tuesday. The ETL job that populates revenue data into your organization’s data warehouse fails midway through the process. When the CFO opens the mobile dashboard to review the last day’s results, he immediately notices that the data is wrong – again. For a few hours, the on-call ETL Architect determines what caused the data-load failure, fixes the issue, and restarts/monitors the job until it successfully completes.
Simple enough to be embedded in text as a sparkline, but able to speak volumes about your business, time series data is the basic input of Anodot’s automated anomaly detection system. This article begins our three-part series in which we take a closer look at the specific techniques Anodot uses to extract insights from your data.
There is a lot of industry buzz around how AIOps will affect change within IT Operations (ITOps). According to Gartner, Inc., the term “AIOps” describes platforms that combine big data and machine learning to support ITOps. This means that the problems being solved aren’t novel, the approach is. In ITOps or any other business unit, there are two primary constraints: time and money.
