- Making Application Performance Monitoring More Proactive
- Current Challenges with Application Performance Monitoring
- How to make application performance monitoring more proactive?
- Detects patterns between software systems
- Analyzes customer data
- Forecasting performance issues
- Decrease MTTD
- In a nutshell
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.