As a global company with more than 6,000 employees, BMC faces many of the same data challenges that other large enterprises face. The organization has 500 applications for business services, 80,000 VMs, 3,000 hosts, and more than 100,000 containers. BMC needed a solution to transform this large volume of data and enable observability to understand thousands of events as a single scenario.
Observability allows IT and other teams that manage data to better understand applications and all their interdependencies. It’s a challenging goal that legacy tools are ill-equipped to achieve, in no small part because they only provide data for one part of the IT infrastructure. To achieve observability, the organization must have a platform and/or a data lake that can ingest data from all tools, and then aggregate and correlate that data. Given the sheer volume of enterprise data, it’s impossible to do this manually.
Artificial intelligence (AI) and machine learning (ML) are critical tools for enabling observability, because they can automate data ingestion and analysis to reveal key insights about the environment. AI for IT Operations (AIOps) incorporates these insights into service management and automates monitoring, discovery, and even remediation.
BMC Helix is an AI-powered service and operations management solution developed by BMC to enable faster, more accurate, and more efficient ways of delivering service innovation. This is turn ensures availability, reliability, and performance. The platform collects data from all common IT tools, and uses AI to correlate the data, provide observability, and enable AIOps.
BMC is an organization that “drinks its own champagne” by leveraging the BMC Helix AIOps platform to address internal enterprise IT challenges. The solution helps BMC gain a critical customer perspective to further hone the solution and ultimately provide a stellar user experience and maximum value. It’s BMC on BMC.
For example, BMC IT uses BMC Helix with AIOps for:
Automation: Up to 30% of incident remediations are automated through self-service, operator-initiated automation, or closed loop remediation.
Service blueprints: For BMC, AI reduced the time it took to create topology models for an application from 24 hours to just 10 minutes.
Incident management: Comprehensive end-to-end monitoring enables a faster time to resolution.
For example, BMC experienced a critical event in its data center that affected 2,000 configuration items (CIs). BMC Helix AI-driven event correlation reduced those 2,000 events to a single situation, saving hours of work to isolate the root cause.
Likewise, when a critical application faces a potential service outage, the BMC Helix platform’s AI capabilities can automatically perform troubleshooting and restorative actions to reduce the impact to a business service and, in many cases, take care of the issue before it becomes a problem for business users. This has allowed BMC to reduce mean time to resolution (MTTR) for critical events to less than five minutes.
Interested in learning how BMC Helix can help you automate up to 30% of incident resolution? Click here to see it in action.
>>> Read full article>>>
Copyright for syndicated content belongs to the linked Source : CIO – https://www.cio.com/article/1251461/bmc-on-bmc-how-the-company-enables-it-observability-with-bmc-helix-and-aiops.html
