IT Operations (ITOps) has played an important role in enterprise development. Especially, the pandemic situation and the paradigm shift to innovative technology have put immense pressure on ITOps to perform better and provide smooth experiences. With enough options in the market, the consumer is quick to quit an application or service if they encounter issues. This is where the adoption of Artificial Intelligence for IT Operations or AIOps, reduces the tremendous load on ITOps and helps improve efficiency, and allows quick remediation and automation.
Performance issues with essential software systems of an organization can seriously hamper service availability and ROI. Due to increased workload and complexity, application performance monitoring is a key challenge for organizations. Not all users will submit a complaint regarding low-performing applications offered by your organization. Customers may simply divert to other service providers if they repeatedly find flaws in your service applications. Find out why there is a need to make application performance monitoring more proactive.
Current Challenges with Application Performance Monitoring
Since the software systems are getting complex, application performance monitoring tools are also getting more multifaceted. Organizations have to invest in training the system administrators for using performance monitoring tools effectively.
There are real-time user monitoring tools that alert IT teams, only when a performance issue has occurred. Firms will solve the issue only after it has occurred and will experience downtime.
- The traditional application performance management solutions are unable to provide high observability into the internal states of the software systems. Poor observability makes it tough to detect performance issues within the IT infrastructure.
- Timeliness of performance alerts can have a significant impact on the costs required to fix the issue. What’s the point in knowing about a performance issue when it has crossed the critical stage? If the anomalies are not addressed on time, they could result in system failure or complete shutdown.
- When a performance issue is detected, it is hard for organizations to find out which IT team is responsible for fixing it. Collaboration between production and pre-productions teams is one of the biggest pain points for IT firms.
- Organizations are lacking predictive analytics models for predicting capacity exhaustion or future performance issues. There is a need to analyze the large amount of data produced by application performance monitoring tools.
How to make application performance monitoring more proactive?
With the challenges stated above, one can understand why there is a growing need for effective real-time user monitoring tools. AIOps (Artificial Intelligence for IT Operations) has proved to be a vital solution for eradicating the monitoring challenges faced by organizations. A few ways in which AIOps makes application performance monitoring more proactive are:
Detects patterns between software systems
No software system in an organization works individually and is related to other software systems. Similarly, performance issues are also correlated and can affect the entire IT infrastructure. Using AI for application monitoring can help you uncover interdependencies between software systems. AIOps platforms will help you in understanding the mission-critical activities for software systems.
The day-to-day data produced by essential applications are recorded and analyzed by an AIOps based analytics platform. The performance data from various sources is matched by an AIOps platform to uncover patterns/clusters. If you can identify patterns between performance issues, you can detect various anomalies even before they occur.
Analyzes customer data
Why depend on customers to report an issue with your service applications? AIOps-based real-time user monitoring tools can collect customer and transactions data. AIOps-based platforms will monitor the user experience of customers and will report a performance issue even before the customer finds about it. When an organization knows about the performance issues with service applications in advance, it can resolve them without hampering service availability.
Forecasting performance issues
AIOps uses predictive analytics models to forecast performance issues within the IT infrastructure. AIOps platforms analyze the historical and current performance data of applications to understand behavioral changes over time. Predictive analytics using AI applications can also help you gain a competitive advantage as you will ensure the high uptime of your software systems.
For example, AIOps can identify if there is a change in how the customers are interacting with your service applications. Predictive analytics business forecasting will help you in solving an IT incident before it impacts the performance.
Studies have shown that AIOps can help in reducing the cost of resolving performance issues by 30% to 40%. Besides cost optimizations, AI data analytics monitoring tools can significantly reduce the MTTD (Mean Time to Detect). When you can find the prevailing performance issue in less time, you can work proactively to resolve them.
AIOps platforms are an AI automated root cause analysis solution that quickly finds the source of a performance issue. Traditional application performance monitoring tools follow a siloed approach and provide limited information to the IT teams. Contrary to that, AIOps analyze diverse information streams to produce actionable insights. It will also help in managing your service availability and reliability.
In a nutshell
The worldwide AIOps industry is predicted to progress with a compound annual growth rate of 34% by 2025. Many organizations are already using AIOps based analytics platforms for monitoring the performance of business applications proactively. AIOps platforms will provide you with high-end analytics that can help in managing applications performances proactively.
Due to the increase in organizational data, IT processes are getting more complex. ITOps teams are finding methods that can increase their productivity. The recent COVID-19 pandemic has also added the pressure of managing remote work infrastructure. The rise of new-age technologies like AI and ML has helped ITOps teams to boost their productivity. AIOps (Artificial Intelligence for IT Operations) strategy is being implemented by many organizations to reap benefits. Read on to know about five use cases that break down AIOps capabilities.
Monitoring of software systems
There are various software systems in an organization used for providing essential system services. Ill-performing software systems will adversely affect the service availability and ROI (Return on Investment). Let’s find out how AI for application monitoring can help an organization:
- Due to a lack of performance monitoring tools, firms cannot predict the exhaustive capacity of their software systems. Application performance management solutions by AIOps can perform continuous monitoring of software systems without any manual efforts.
- AIOps monitors the entire IT infrastructure including virtual desktop infrastructure solutions, cloud resources, and other distributed IT architectures. The performance data collected is then analyzed via relationship mapping to derive insights.
- As new resources are added to the IT infrastructure, you must upgrade your application performance monitoring tools. Contrary to traditional monitoring tools, AIOps is scalable and can automatically adjust its capabilities according to the IT infrastructure.
Many organizations fix an anomaly in their service applications only when a user reports about it. However, this will have a serious impact on service reliability as you will have to fix anomalies in real-time. Predictive analytics models used by AIOps can help you in predicting future anomalies. AIOps based analytics platforms compare the real-time performance metrics with the historical data to predict anomalies within the IT infrastructure.
AIOps sets predefined performance levels and when the performance of a software system goes below that level, it immediately informs the concerned IT teams. Predictive analytics using AI applications can help you in setting automated alarms on the detection of an anomaly. It will help ITOps teams in fixing anomalies before they leave their impact on the service availability.
Root cause analysis with AIOps
AIOps is an AI automated root cause analysis solution that finds the underlying cause of an IT issue/anomaly. How AIOps helps with root cause analysis are as follows:
- Complex software systems make it hard to find the root cause of the issue/anomaly. CXOs have to analyze large data pools from different software systems to find the source of an IT issue. It is time-consuming and tedious for system administrators. AI data analytics monitoring tools can significantly decrease the MTTD (Mean Time to Detect).
- AIOps based platforms help in event correlation that identifies similar IT issues. If an event has occurred before, AIOps platforms will quickly inform that the same event has occurred again. One can follow the same procedure that was used to resolve the IT anomaly earlier and decrease MTTR (Mean Time to Resolve).
Reducing alert noise
The IT infrastructure of organizations is increasing due to a higher demand for their services. However, with more software systems, the number of alerts generated for anomalies can be significantly higher. IT teams often find themselves in the middle of numerous alerts for anomalies and can’t decide which one should be solved first.
With AIOps, you can define the range of normalcy and can proactively take steps when an event comes near that range. Real-time user monitoring tools offered by AIOps uses an inference model to select the most impactful alerts from a group of overloaded alerts. With AIOps, you can determine the critical alerts and can solve them first. Critical alerts are those which have the most adverse impact on essential software systems.
Security use cases
With the increasing complexity of IT infrastructure, ensuring security and compliance is a headache for ITOps teams. The top security use cases of AIOps platforms for organizations are as follows:
- IT operations analytics platform service powered by AI can help you with intelligent threat analysis. Besides finding behavioral anomalies, AIOps can help you with identifying the attacker sources.
- When a security issue is detected, AIOps-based platforms provide actionable insights to reduce the MTTR.
- Besides identifying external attacks on your IT infrastructure, you can also identify internal frauds with the help of real-time user monitoring tools. If a system software shows anomalous behavior, AIOps platforms can quickly identify it.
- AI data analytics monitoring tools can identify malware or ransomware in software systems without any manual efforts.
In a nutshell
The global AIOps industry worth will be more than USD 23 billion by the end of 2027. Application performance management solutions powered by AI/ML algorithms can reduce the manual load on your employees. You can handle security issues within your IT infrastructure in real-time via AIOps platforms.