BMC announced the newest release of its TrueSight AIOps platform, TrueSight 11, empowering customers to apply artificial intelligence to IT Operations.
TrueSight 11 is an AIOps platform that identifies and contextualizes patterns from virtually any data source, revealing recurring issues and repetitive tasks ideal for automation. TrueSight dynamically learns the behavior of infrastructure and manages capacity, including multi-cloud utilization in the context of applications and services. These predictive capabilities empower IT teams to reduce mean time to resolution (MTTR), identify false alarms, and align cloud spend with budget targets.
“Businesses cannot realize the true potential of a dynamic infrastructure if operations teams aren’t managing those resources in the context of business priorities,” said Shayne Higdon, President, Performance & Analytics at BMC. “BMC’s research indicates that 78 percent of IT leaders are looking to apply artificial intelligence as part of their multi-cloud management strategies. Our TrueSight platform for AIOps manages the health, performance, and cost of multi-cloud environments to improve infrastructure and operations agility, optimize service performance, and eliminate blind spots created by the explosion of digital data.”
TrueSight easily ingests, analyzes, and contextualizes data to provide actionable correlations and insights faster and more accurately than manual human analysis could produce. By dynamically learning the behavior of the infrastructure, TrueSight prioritizes issues by level of importance to the business and eliminates false alarms. With unified capacity and performance analysis augmented by AI, organizations can now understand resource utilization in the context of both performance and spend, enabling them to intelligently forecast and optimize both using TrueSight.
TrueSight 11 also introduces new solutions that address key use cases for machine learning to improve IT Operations, Service Desk, and Application Development disciplines:
- Cloud Cost Control forecasts infrastructure capacity and cost for cloud services and on-premises data centers to regain control of budgets and optimizing resources.
- Service Ticket Analytics uses machine learning to analyze tickets descriptions in real-time and intelligently categorizes them for faster resolution.
- Change and Defect Analytics uses Jira data for visibility into the performance and financial impact of defects and bugs assigned to application development teams.
- Event Stream Analytics reduces event overload to quickly identify hotspots and proactively identify service or application deterioration before users are impacted.
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