IT operation teams use many tools to monitor, diagnose, and resolve system and application performance issues. In a recent survey of 1,300 IT professionals on the future of monitoring and AIops, 42 percent report using more than 10 monitoring tools; 19 percent use more than 25 tools.
That’s a lot of technology just to keep the lights on and provide the data required to monitor, alert, research, and resolve application incidents.
Monitoring tools are not one size fits all, especially for organizations running mission-critical applications in multicloud environments. As organizations invest in mobile apps, microservices, dataops, and data science programs, new monitoring tools are being added to provide domain-specific monitoring capabilities.
AIops platforms aim to simplify this landscape of monitoring tools. AIops helps organizations that require high application service levels better manage the complexity of their monitoring tools and IT operational workflows. As the name suggests, AIops brings machine learning and automation capabilities to the IT operations domain. These technologies aim to resolve incidents faster, identify operational trends that impact performance, and simplify the procedures required to resolve issues.
AIops is an emerging platform. In the survey, 42 percent of respondents either had never heard of AIops or had thought that applying machine learning to operations was “not a thing.” Only 4 percent are using an AIops tool in production today. Although AIops is an emerging platform, there’s a solid business case for many organizations to consider it.