How AIOps helps in Application Monitoring

There’s no one-size-fits-all approach regarding application monitoring, especially for companies using applications in various cloud environments. Companies are rapidly investing in microservices, mobile apps, data science programs, data ops, etc. Subsequently, they’re also integrating monitoring tools to improve domain-centric monitoring abilities.

AIOps tools help streamline the use of monitoring applications. It allows companies that need high application services to efficiently manage the complexities of IT workflows and monitoring tools. AIOps extends machine learning and automation abilities to IT operations. These robust technologies aim to detect vulnerabilities and issues to resolve them, determine operational trends, and simplify the remediation of the problems that affect their applications’ performance and availability.

What Exactly Is AIOps?

AIOps is short for Artificial Intelligence for IT Operations. AIOps combines machine learning, data analytics, and many other AI technologies to automate the identification and remediation of common and recurring IT operations issues. AIOps leverages data from logs and event recordings to monitor assets and obtain visibility into dependencies without interfering with IT systems.

Capabilities of AIOps Platforms

AIOps platforms provide the following capabilities:

  • Machine learning capabilities to help in identifying patterns in the collected data.
  • A dedicated data platform for aggregating raw data and logs from various monitoring tools and data sources across your applications and infrastructure. 
  • Dashboards, analytics, and console integration help IT operations gain a single-pane view over their applications and infrastructure.
  • Out-of-the-box integrations with tools used for IT service management, monitoring, agile development, collaboration, and log data collection, parsing, and ingestion tools. 

How Does AIOps Work?

AIOps platforms are powered by algorithms that automate and simplify prominent aspects of IT operations and application monitoring:

  • Data Selection: It collects all the data generated by applications and infrastructure in the form of logs and events and analyzes it. Post analysis, AIOps platforms highlight data that has an issue.
  • Pattern Discovery: AIOps platforms correlate and find relationships between different data elements in the form of patterns.
  • Interference: AIOps determines the root causes of new and recurring issues allowing companies to take proactive actions to mitigate the implications of these issues. 
  • Collaboration: AIOps platforms simplify and promote collaboration across IT teams through unified dashboards and intelligent notification systems.
  • Automation: AIOps works towards automating responses to issues and threats as much as possible, thereby making issue and threat remediation quick and straightforward. 

Improved Application Monitoring with AIOps

The adoption of AIOps has numerous benefits – right from processing data from multiple sources faster and using that data to make data-driven decisions, to making IT operations more proactive by predicting and remediating performance issues across applications and deployments. Let’s take a closer look at how AIOps is helpful in improving your application monitoring efforts.

Detect Hidden Relationships

IT operations and monitoring are an extensive web of interdependencies; no system works independently. However, with so much data present, it is challenging to understand the relationships between systems. AIOps allows you to evaluate performance metrics across different types of systems quickly. This can help identify the impact of IT applications on the overall company’s performance and customer satisfaction. 

This is accomplished by initially working with the business to determine mission-critical activities for such applications. The next step is to gather data produced during the day-to-day tasks like orders, cancellations, transactions, etc. AIOps algorithms can be leveraged to identify patterns or clusters in the collected data, allowing businesses to understand the relationships better.

Optimizing The Use of Customer And Transaction Data

Capabilities of AIOps can help in the identification of patterns, anomaly recognition, categorization, and extrapolation. These are essential aspects of big data analytics operations that the organization applies to the transaction and customer data. Leveraging AIOps can help in understanding user behavior in broad IT systems. 

This will make it easier to monitor how any modifications on the applications will affect the business operations. By harnessing internal application monitoring data, AIOps can bring together customer and transaction data effectively. When the information is readily available, a business can efficiently choose the right path for the application.

Forecasting The Issues

An essential role of AIOps is ameliorating the predictive analytics activities. It closely studies the current and past behavior of the apps. This allows the technology to predict future scenarios, enabling the business to adjust its strategies. This proactive approach helps in improving application performance and also in gaining competitive advantages. 

For instance, companies can identify changing trends in how users are interacting with apps. So they will have a clear idea of the areas that they need to focus. Moreover, AIOps allows businesses to perform a deep analysis of the cause of the problem. Not just that, it will also take the necessary steps to eliminate the issue before it impacts the performance.

Decrease The Response Time

By leveraging AIOps, companies can reduce the response time of dealing with errors and outages. Experts believe that AIOps can reduce the cost of events like errors, outages by 30% to 40%. This signifies a massive saving considering that the average cost that a company bears in service disruption is approximately $300,000 per hour. 

This is due to the ability of this powerful technology to detect where the data originates. Every system that a business uses produced a lot of data, making it harder to track the source of information. But AIOps manages the massive amount of data from a central location, allowing better process and application security.

Bringing Together Silos

One of the hurdles in improving application performance is how siloed organizations can be. More than 90% of IT professionals say that most monitoring tools only provide them with information related to their areas of responsibility.

But AIOps can deal with this issue by leveraging data analytics and machine learning. These technologies allow the tools to monitor tons of information streams. Such extensive monitoring makes it easier to spot problems that would otherwise be difficult to spot with a siloed approach.

Final Thoughts

IT leverages a lot of application monitoring tools to maintain operational efficiency. However, each of these tools collects a massive amount of data that needs to be maintained. The team fails to detect vulnerability and issues in the complex web of data, leading to security threats. By accessing the potentials of AIOps, IT teams can automate and improve their application monitoring processes by leaps and bounds.

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