The latest News and Information on AIOps, alerting in complex systems and related technologies.
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The world of software is growing more complex, and simultaneously changing faster than ever before. The simple monolithic applications of recent memory are being replaced by horizontal cloud-native applications. It is no surprise that such applications are more complex and can break into infinitely more ways (and ever new ways). They also generate a lot more data to keep track of. The pressure to move fast means software release cycles have shrunk drastically from months to hours, with constant change being the new normal.
There’s a familiar saying: garbage in, garbage out. For ITOps, this directly applies to data engineering. BigPanda’s Area Vice President of Value and Adoption, Craig Ferrara, says the importance of data hygiene—putting good data in to get good data out—is the core of data engineering, and it requires ITOps to take a look at their data before integrating with an AIOps solution.
Complex environments are notorious for generating a high volume of alerts. For IT teams, this deluge presents a critical, time-consuming challenge. Managing alerts and incident response keeps these busy professionals under constant pressure and risks alert fatigue. Nonstop “noise” can desensitize people and actually lead to missed or ignored alerts—risking delayed responses and downtime. These high stakes make handling alerts a key security and productivity issue.
Most organizations suffer from some form of alert noise, shares Adam Blau, senior director of product marketing at BigPanda. “Alert noise is only going to increase as organizations support cloud-native applications spanning multiple public and private clouds, including ephemeral deployments and more. It’s not going to get easier for organizations to understand the signal from all those alerts being sent,” Blau said.
