![\"\"](\"https:\/\/www.selector.ai\/wp-content\/uploads\/2022\/01\/aiops-for-networks-2.jpg\")
<\/p>\n
Analysis that leverages all the available data.<\/strong> Today\u2019s solutions do analysis in silos. Different vendors, different data types (telemetry, logs, configuration changes\u2026), and different layers (application, network overlays, and network infrastructure).<\/p>\n Siloed analysis does not generate the rich context needed to connect the dots, identify problem causes, and quickly allow operations teams to focus their efforts in the most impactful areas.<\/p>\n Leveraging collaboration environments.<\/strong> Today\u2019s workplace has been dramatically transformed by collaboration tools such as Slack and Microsoft Teams. However, many operations tools simply send alerts or dump raw metrics into these tools, overwhelming operations teams. Not only do alerts suffer from the previously explained problems, but the collaboration tools are also not leveraged. Whether working from home, in an office, or on the road, a variety of skills may be called on, across organizational boundaries and from many different screen types, to root cause an anomaly. Collaboration tools have become a critical hub for today\u2019s work style. Operations teams need tools that are well-integrated into these environments so the tools themselves become another member of the team.<\/p>\n Scalable insights.<\/strong> Microservices, network overlays, virtualization, and multi-cloud, are all driving the number of endpoints that have to be assured, the transient nature of endpoints, and where tools execute: public cloud, private cloud, hybrid cloud. Current generation tools are struggling to keep pace with this change. Operations teams need cloud-native solutions built specifically to address these changes.<\/p>\n Operations teams need to see the meaningful anomalies above the noise, they need rich context, including relationships between different anomalies, so they can quickly target probable causes, and they need the ability to rapidly collaborate across organizational boundaries and locations, to determine required actions. Teams also need cloud-native solutions that scale to meet today\u2019s data-rich operations environments. These are the key challenges that have to be met to significantly reduce, and ultimately eliminate, the 90% of the time it takes to resolve incidents. See, understand, collaborate, act.<\/p>\n In follow-on blogs we will elaborate on these challenges and describe how Selector AI uniquely meets these challenges.<\/p>\n","protected":false},"excerpt":{"rendered":" AIOps harnesses the power of machine-learning to analyze data and create insights. With many of today\u2019s disparate and siloed analytics approaches, as more data becomes available, complexity increases. With machine-learning approaches, the opposite is true \u2014 as more data becomes available, insights get better, creating clear pointers to probable cause. 90% of incident resolution is spent in analyzing data and identifying potential sources of anomalies. The leading edge of AIOps slashes this incident resolution time by addressing four critical operations challenges Critical Challenges Rapid identification of meaningful anomalies. Operations teams are overwhelmed by the velocity, variety, and volume of data. Teams cannot scale to observe all metrics, or effectively manage all thresholds. Meaningful anomalies are not separated from noise, operations teams have to switch from one statically programmed visualization to another, in an attempt to work out where diagnostic efforts should be fo<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"_uag_custom_page_level_css":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[29,37,31],"tags":[],"class_list":["post-1060","post","type-post","status-publish","format-standard","hentry","category-aiops","category-machine-learning","category-observability"],"acf":[],"yoast_head":"\nConclusion<\/h3>\n