AIOps Blog

Accelerate Service Assurance with the Latest Release of BMC Helix AIOps Solutions

4 minute read
Gyanendra Rana, Michi Schniebel

We are delighted to announce our latest 23.2 Spring release for BMC Helix Operations Management with AIOps, packed full of new innovations that enable customers to gain even more value from their investment and stay ahead of potential IT issues. We’ve enhanced service modeling, which improves service assurance for customers, added more vendors for out-of-the-box integrations and a larger data lake for customers to abstract data from, and much more. Read on to learn more about these exciting new enhancements.

BMC Helix Operations Management

We’ve rolled out new artificial intelligence and machine learning (AI/ML)-powered Situations capability to help customers manage major incidents, which includes:

New Service Blueprints: Service models are critical for real-time identification of service impacts; however good service models can be hard to create and maintain. With new Service Blueprints, you can define simple templates like a microservice on Kubernetes and then apply that template to all your other microservices, using AI/ML to monitor applications and continually analyze them for signs of trouble, identify patterns and trends, and take appropriate action.

Primary Situations: When you have thousands of events pouring in from many different sources and stakeholders across the organization demanding answers, you need a solution that gets you to the source of an issue fast. With the new Situations functionality, we can reduce event noise through automatic AI correlation. Multiple Situations are grouped into Primary Situations, so a common root cause be identified and fixed by the right team, leading to less distraction and faster mean time to repair (MTTR).

Situation Explainability: To quickly verify logic and get peace of mind about why a decision has been made, Situation Explainability can provide visual representations that help you validate how events were correlated and how the root cause was identified.

Situation Fingerprinting: The new Situation Fingerprinting feature leverages AI to automatically identify whether a similar situation has previously occurred. If you have seen and resolved the problem once, there is no need to go through the entire process again. Recurring situations are fingerprinted for easier future identification to help speed MTTR, reduce noise and staff toil, and improve service performance.

BMC Helix Operations Management - Situation Explainabilty.

Figure 1. BMC Helix Operations Management – Situation Explainabilty.

BMC Helix Log Analytics:

In the 23.2 Spring release for BMC Helix Log Analytics, we built an additional capability that use ML and unsupervised deep-learning model to assess data, detect anomalies from logs, and generate events that quickly alert you to impending problems in your application or system.

BMC Helix Log Analytics - Discover

Figure 2. BMC Helix Log Analytics – Discover.


BMC Helix Log Analytics – Self Monitoring

Figure 3. BMC Helix Log Analytics – Self Monitoring.

BMC Helix Intelligent Integrations

BMC Helix Intelligent Integrations use REST APIs and Webhook mechanisms to communicate with a data source, providing an easy-to-use, click-and-connect capability to configure an integration and bring in resource information, topology, and services from third-party data sources for an end-to-end view of your environment. In this release, users will find new connector support for Kafka, SAP HANA, and CA UIM and a connector enhancement for CA APM (topology).

The new and enhanced connectors make it faster and easier for customers to add their third-party monitoring data to BMC Helix Operations Management with AIOps, delivering more data sources to strengthen the AIOps algorithms’ ability to isolate root cause and speed MTTR.

BMC Helix Intelligent Integrations

Figure 4. BMC Helix Intelligent Integrations.

BMC Helix Continuous Optimization

BMC Helix Continuous Optimization uses intelligence and predictive analytics to manage IT resources and applications, including those based on Kubernetes and pods, microservices, containers, and multi-cloud services.

The latest release continues to build on its strong foundation by enhancing capabilities in the migration assessment tool to allow for quicker migrations and adding new out-of-the-box AIX views that allow customers to analyze the capacity of the AIX infrastructure and integrate the views into BMC Helix Dashboards.

New out-of-the-box service risk dashboards allow users to visualize a business service’s performance and gain insights into its overall health and status based on the correlation between business drivers and resources of a pool.

BMC Helix Continuous Optimization – AIX views

Figure 5. BMC Helix Continuous Optimization – AIX views.

New Synthetic Monitoring

BMC is also introducing a new partnership with Catchpoint, a web-based-software-as-a-service (SaaS) monitoring solution that enables end-to-end transaction monitoring.

Combining Catchpoint’s synthetic data solution with the BMC Helix platform, BMC delivers a complete solution where synthetic information can be used in tandem with infrastructure, application performance monitoring (APM), AIOps, or network data.

To learn more about this latest release and for an overview of the new features in BMC Helix Operations Management with AIOps, please refer to the release notes below:

Our development is highly dependent on the feedback we receive from our customers, partners, and the wider analyst communities. Thank you to all of you who contributed feedback to us. To continue the discussion, tell us how you’re using the new features and workflows and share suggestions to improve the product experience on the BMC Helix Operations Management community forum.

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These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.

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About the author

Gyanendra Rana

Gyanendra Rana is a Director of Product Management at BMC. He is responsible for AIOps and cross-product machine learning (ML) and automated intelligence (AI) management and combines a deeply technical background with a passion for bringing new tech to market. He started his career in engineering and, after working for many years, transitioned into product management. He has strong domain expertise in infrastructure tools, log analytics, security, observability, and AIOps for Enterprise management products.

Prior to joining BMC, he worked at Splunk where he led the team responsible for Advanced Analytics and Machine Learning Portfolio across enterprise, cloud and solutions products including Splunk Enterprise, Cloud, and Machine Learning Toolkit (MLTK). In his spare time, Gyanendra likes to read and enjoy all things outdoors.

About the author

Michi Schniebel

Michi Schniebel is Director of Product Management at BMC Software for the Helix ITOM products. With over 25 years of enterprise software, ITOM, hosting, MSPs, and cloud expertise, he is keenly attuned to define the product vision. His experience as a software engineer, Monitoring User, Data Center manager, and Product Manager helps him to understand customer pain points and needs to drive engineering more efficiently to the desired outcome.