[ADVERTORIAL] In today’s business, digital transformation requires a customer-centric, digital first Approaching all aspects of an organization, from business models and customer experiences to processes and operations.

AI, automation, hybrid cloud and other digital technologies are being deployed to harness data to drive smarter workflows, faster and smarter decisions, and real-time responses to market disruptions. And ultimately, it changes customer expectations and creates new business opportunities. Artificial intelligence and machine learning algorithms have played a critical role in the digital transformation of the very essence of businesses around the world.

These ever-changing business trends have revolutionized how digital markets work. Network workers everywhere must now face the ever-changing challenges that are inevitable.

Challenges for IT administrators in the ITOM area

The larger the network, the smaller the overview and, ultimately, the challenges posed by complex networks. Businesses face a variety of IT challenges, including:

scalability: As companies grow, they must scale their IT operations to meet increasing demands. This can be challenging as IT infrastructure and applications become more complex and difficult to manage.

complexity: Modern IT infrastructures are complex and effective management requires a great deal of specialist knowledge. Operational IT teams need to have in-depth knowledge of the infrastructure and applications in the organization to ensure everything is running smoothly.

integration: To ensure business continuity, the activities of IT teams must be aligned with those of other entities in the organization, such as e.g. development teams and stakeholders. This requires effective communication and collaboration between departments, processes and technologies.

maneuverability: IT teams must be able to respond quickly to changing business needs and requirements. This requires a flexible and agile infrastructure that can adapt to changing requirements.

Costs: The cost of running IT can be significant, and organizations must therefore weigh the cost of maintaining their IT infrastructure against the value it brings to the business.

AIOps platforms vs. domain-centric AIOps

AIOps platforms perform multiple functions including data collection and analysis, pattern recognition, and automatic recovery. They usually work according to the Observe, Engage, Act model. An AIOps platform combined examines logs from various tools and platforms in silos with the intention of identifying recurring patterns or trends that may be cause for concern or anomalies. This method may be a breakthrough in digital transformation, but it still looks like a group of blind people trying to describe an elephant by touching different parts of the animal’s body. What a futile attempt to generate alerts from a tool that relies on data from other tools that don’t communicate with each other.

There are many solutions and tools on the market that leverage AI-based technologies specific to their domain and use case. An example of this is ManageEngine OpManager, which includes features such as customizable thresholds, capacity planning, and forecasting. These functions apply AI and machine learning algorithms to perform a specific task or solve a specific problem.

This is where an AIOps tool that can already ping devices and includes inventory information in its network performance monitoring model can solve the “contextual blindness” problem. The data is processed in a highly contextual manner by the ML algorithm and correlated with more context for pattern analysis and quick detection of anomalies rather than trying to find a needle in a haystack. This is how intelligent and efficient systems for network monitoring and management are created.

What to expect from a domain-centric AIOps solution

Here are the three main aspects of an AIOps tool to monitor network infrastructure performance:

• anomaly detection
• Forward-Looking Forecast
• Automatic error correction

Fast detection of anomalies

Let’s take a look at a use case of a typical IT organization. The sales team has a deadline to meet. You need to upload all the data to the CRM tool. However, the tool works slowly and the data is not uploaded. The sales manager contacts the IT support department who can help him with this problem.

The IT technician performs some basic troubleshooting and diagnostic tests to examine network and server availability, network connectivity, bandwidth bottlenecks, and application outages. Finally, he takes a look at the logs, which he hates, by the way. Another hour and his shift will be over. He just wants to get this over with.

The ticket is now forwarded to the operations manager. They will contact the application framework development team in the hope that they can resolve the issue as soon as possible. The sales team has now CCed their bosses in the mail. This delay is absolutely unacceptable! Finally, the developer identifies the faulty code.

The error was at the code level and was impossible for the IT support department or the operations manager to find. The problem is solved. With a delay of six hours and many remote sessions, which caused a lot of trouble for everyone involved, the CRM database is filling up. Well, the job is done.

Now let’s look at what would happen if an AIOps tool had been deployed to detect this anomaly.

The AIOps tool that monitors the CRM application monitors the number of incoming requests over time. The graph is compared to previous data so any deviation is quickly identified. But how? Just by looking at seasonal trend data.

AI quickly identifies the fault domain through event correlation and cause-and-effect analysis. In this case, the problem is caused by failed transactions that are the result of buggy code in the continuous deployment pipeline.

The demand-time graph is further processed by AI and ML’s algorithms to determine the exact time the anomaly occurred. This is very different from how a human would solve the problem. And a lot less time consuming too.

Why is the AIOps model performing better? Proactive problem isolation and root cause analysis (RCA)

Immediate, scheduled or periodic notifications within the escalation matrix

Instant solution with rollback/script execution through human-in-the-loop (HITL) automation

OpManager is an efficient, intelligent network monitoring tool that can perform cause and effect analysis, among other things. Long downtimes are easily avoided when the network team installs a holistic network performance monitoring solution like OpManager in the network environment.

Predictive forecasting with AIOps

OpManager uses existing data using ML algorithms to track your memory consumption and predict when your memory will reach its maximum capacity and when you will need an upgrade. OpManager uses an ML algorithm to predict thresholds that can be set by an administrator for each device. OpManager warns when set capacity thresholds are exceeded. This way you stay informed about your memory, CPU and memory usage.

Solve network problems automatically

Proactive monitoring: AIOps performance management tools enable real-time monitoring of IT infrastructure and applications, providing IT operations teams with visibility into the performance and health of their systems. OpManager comes with 10,000 ready-to-use device templates with initial configurations to categorize the devices based on vendor type, device type, etc. into the predefined device types. This significantly shortens the implementation time. You can also define custom templates or modify existing templates to meet the needs of your environment.

Alert: AIOps tools can send alerts and notifications to operational IT teams when issues are detected, allowing them to quickly identify and respond to issues. OpManager has notification profiles, alarm suppression mechanisms and threshold violation warning systems. That’s all an IT admin needs to stay up to date and keep a close eye on the entire network.

Analytics: AIOps tools can analyze data from IT infrastructure and applications to identify trends and patterns, helping IT operations teams understand performance and make data-driven decisions.

Recovery: AIOps tools can automatically fix problems by performing tasks such as restarting an application or reconfiguring a server. This is exactly what the workflow feature in OpManager does.

Predictive Maintenance: AIOps tools can use machine learning algorithms to predict when devices or systems are likely to fail, allowing IT operations teams to perform preventative maintenance before the failure actually occurs. OpManager’s forecasting feature was designed to make this process a breeze for IT teams.

AIOps functionality in OpManager

OpManager predicts future memory, CPU, and memory usage based on existing data.

The tool tracks CPU and memory usage, analyzes your memory usage and provides a report with accurate data when your devices reach 80%, 90% and 100% of the allocated storage space. OpManager warns when set capacity thresholds are exceeded. This way you stay informed about your memory, CPU and memory usage. Network administrators can use the internal workflow automation tool to design their own workflows to automatically resolve network issues.

With OpManager as your trusted AIOps tool, the human-made “post-mortem” reports typically created with slow and accurate manual analysis are a thing of the past, and you achieve better IT infrastructure management, lower MTTR, and lower total cost of ownership. while keeping people informed when needed.

Fill out this form and see OpManager in action

Download OpManager here. You can request a personalized demo here.

You can find offers on this website and general information here.

This is a commercial submission by ManageEngine. The publishers are not responsible for the content.