Tackle complexity with AIOps software
AIOps tools apply machine learning and advanced analytics to identify patterns in monitoring, capacity, service desk, and automation data across hybrid on-premises and multi-cloud environments. Adopting AIOps empowers IT operations and observability teams to :
- Utilize AIOps, machine learning, and anomaly detection to improve performance and availability, on-prem and in the cloud
- Reduce event noise and prioritize business-critical issues
- Support the speed of application releases and DevOps processes
- Proactively identify problems and quickly drill into root cause to reduce MTTR
- Model and predict workload capacity requirements to optimize resource usage and cost
Key requirements of AIOps software
Implementing an AIOps strategy goes beyond getting better analytics for existing data. Building the basis for a machine learning system that will yield continuous insights requires:
- Open data access including multiple, consumable sources of historical and streaming IT data
- Machine learning and algorithms that learn behavioral patterns of data and yield automated insights
- Automation to act on analytical insights and engage with the ITSM Service Desk
BMC is a trusted leader in AIOps
BMC solutions deploy machine learning and advanced analytics as part of a holistic monitoring, event management, capacity and automation solution to deliver AIOps use cases that help IT Ops run at the speed that digital business demands.
- Reduce event noise by 90%
- Predictively alert to reduce incidents by 40%
- Reduce time to identify root cause by 60%
- Automate event remediation to reduce MTTR by 75%
Open data access
Observability teams must be able to consume huge volumes of data and events across multiple technologies and systems of record as the basis for a successful AIOps strategy. Key requirements include:
- Monitoring distributed applications across on-premises, cloud and container environments
- Achieving a unified data view across different layers of the app stack
- Data agnostic monitoring, including taking in data from other monitoring tools
Machine learning
IT analytics is ultimately about pattern matching. Machine learning applies the computational power and speed of machines to the discovery and correlation of patterns in IT data. It does this more and faster than human agents and dynamically changes the algorithms used by analytics based on changes in the data.
- Behavorial learning of normal conditions
- Dynamic baselines extend beyond static thresholds
- Anomaly detection based on learned patterns
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