Continue to drive automation Implement AIOps and relieve IT teams
Those who have already anchored agile technologies, cloud computing and DevOps in the company are on the best path to digital transformation. AIOps continue to drive strategic innovation and enterprise automation.
Companies on the topic
AIOps helps to keep track of the enormous amount of data and to detect errors in the configuration earlier.
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Artificial Intelligence for IT Operations or AIOps for short combines Artificial Intelligence (AI), Machine Learning (ML) and Big Data. Demand for it is currently growing steadily because unexpected scenarios can be addressed and anticipated using AI, ML and predictive analytics.
The increasing complexity of digital enterprise applications, which include hybrid on-premise and cloud infrastructures, as well as the introduction of modern application architectures such as containerization, will lead to a sharp increase in the volume and complexity of data. Data overload from modern digital environments can then delay repairs and overwhelm IT ops teams.
Smarter strategies and centralized AIOps systems can help organizations improve the customer experience, deliver advanced application security and optimization, leverage intelligent automation, and grow as an autonomous digital enterprise. The adoption of AIOps is essential to scale resources and effectively manage modern environments.
Key benefits of AIOps
IT OPS teams face growing and diverse challenges, including managing the huge increase in operational data volume, the increasing complexity of IT environments, and the high demands for speed and flexibility due to digital transformation.
By applying artificial intelligence and machine learning, as well as big data analytics to improve and automate IT operations, AIOps helps improve the speed, flexibility and efficiency of businesses. Problems and anomalies can be identified in real time and in some cases resolved directly.
Implementing AIOps is more important than ever for businesses. IT ops teams are facing a huge increase in operational data volume, the increasing complexity of IT environments through multi-cloud and remote work environments, and digital transformation initiatives with newer application architectures such as containers and short-lived workloads.
In addition, AIOps can also help Dev-Ops teams achieve greater system reliability. If customers already have knowledge of correlations of events and recognizing patterns, more modern approaches with no-code capabilities may be helpful to be able to capture this custom knowledge.
AIOps doesn’t just offer a more effective approach to automatically identifying clusters and patterns and speeding up root cause isolation. Rather, even in environments with a lot of data and complex dependencies, it is the only possible approach to manage and reduce “noise”.
Overall, AIOps can help relieve IT teams and support innovation and improvement for the enterprise. By automating traditional processes using AIOps, organizations can achieve greater employee satisfaction, improved customer retention, and effective time and resource savings.
The implementation of AIOps
To successfully implement AIOps, it’s important to define beforehand what your organization expects from an AIOps initiative. This can involve either current requirements such as more effective reduction of “event noise” and faster analysis of the possible causes to reduce the mean repair time (MTTR) for problems, or areas of interference that need to be addressed. Prior consideration can be used to determine what a successful AIOps implementation might look like.
In the second step, the people, processes and tools should be considered. IT organizations operate in highly complex, hybrid environments. Therefore, not only is it more expensive to pursue traditional approaches, but it can quickly become unmanageable as you scale your teams. When choosing the right tools, it’s important to find a vendor that specializes in your specific use cases and can help you achieve your goals.
It’s also critical that AIOps tools have an open approach that integrates with your existing IT tools and data sources, as a wide range of data needs to be observed and analyzed. They should then identify the right processes that support agility and collaboration to integrate across development, operations, and security. It is equally important for companies to think about the most valuable resource, the employees, and ensure that the right tools and processes are in place to build on insights.
Another decisive factor is the right data types, because the amount and volume of data alone are not sufficient. While events and metrics are useful for delivering AIOps insights, topology data can be important for effectively performing root cause analysis-and the ability to unify data from multiple sources is hugely important.
As the volume and complexity of data and the proliferation of IoT and 5G continue to grow, intelligent solutions such as AIOps are becoming an indispensable part of data-driven businesses. AIOps enables organizations to transform their solutions from reactive to predictive and finally to proactive. When employees identify potential problems early, organizations can prevent disruption, effectively save time, and focus on innovations that drive operational performance and help the organization evolve into an Autonomous digital enterprise.
Possible challenges during the changeover
The mindset and willingness to modernize the previous monitoring and event management processes is crucial in the transition. Companies should be open to new types of technology solutions. For example, some IT organizations have concerns about investing in data scientists ‘ skills or hiring people with a degree in data science.
However, the IT solution should include all the intelligence, so organizations only need the operational capabilities to manage it and strategically leverage the rich insights AIOps can deliver. It’s also important to invest in the right platform that can scale and absorb data from multiple tools.
The key to using AIOps is a strong data management foundation. Understanding what a company’s” normal ” operations look like across the enterprise in a single end-to-end view helps IT teams identify needs, opportunities, and threats as they arise. This is critical for smart operations and a data-driven business.
The Future of AIOps
AIOps are expected to become mainstream in the next two to three years as companies continue their path to “autonomous digital enterprises”. Data volumes will continue to grow and become less and less manageable, especially with the introduction of IoT and 5G.Demand for AIOps will increase as companies continue to try to meet performance requirements and SLAs in the face of the enormous amount of data.
In addition, the connection between DevOps and IT Ops is strengthened by leveraging AIOps ‘ comprehensive insights in the app development process for application performance management. In addition, AIOps will play a central role in managing cloud-based apps and services, given the enormous cloud adoption.
The future depends on technology improving, scaling effects decreasing, and algorithms becoming mainstream – for example, they should simply work “out of the box” for different domains. AIOps is not expected to be a niche tool anymore, it will prevail in the broad masses if it is easy to use.
* Ali Siddiqui is Chief Product Officer at BMC Software.
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