While implementing digital transformation, one of the many challenges a company must go through is to implement a strong disaster recovery program. IT downtime and failure can lead to loss of business and customer data. Companies try their best to reduce the count of power outages to avoid any loss of information. However, power outages can still occur and, safeguarding data is of utmost importance.
Challenges with legacy tools in disaster recovery and protection
Why do you need to shift to AI-based tools for reducing IT outages? Businesses shifted to AI-based tools because legacy tools cannot handle the complexity of modern-day IT infrastructure. Application performance monitoring is an important process for predicting IT outages. Traditional monitoring tools cannot monitor the performance of applications and software systems rigorously which leads to unpredicted IT failures. To cope with the complexity of modern-day IT infrastructures, AI for application monitoring is used. Let us look at how AI helps in overcoming these challenges and reducing IT failures.
AI can help in predicting potential risks
The biggest challenge for businesses in 2021 is that they cannot predict potential IT risks. They are blind when it comes to knowing the risks associated with their IT infrastructure. You could perform manual analysis and still miss the IT vulnerabilities present in your digital infrastructure.
AI for application monitoring helps in predicting IT risks is as follows:
- AI-based monitoring tools can perform data analysis with higher accuracy. They can compare large sets of business data that cannot be analyzed manually.
- AI for application monitoring can predict IT risks that haven’t even occurred yet. It helps IT professionals in predicting unknown risks associated with the IT infrastructure.
- AI can predict the IT risks that are more likely to occur based on the current situation of your IT infrastructure. It helps businesses in implementing a better strategy for disaster recovery and protection.
- Besides IT risks, power outages also occur within the IT infrastructure. An important software system could abruptly stop working as it has reached its exhaustive capacity. Power outages and sudden shutdowns can result in data loss. AI-based tools use predictive analysis models to know when a system will fail or stop working.
Among all the AI-based tools, AIOps (Artificial Intelligence for IT Operations) is preferred for application performance monitoring nowadays. AIOps can perform rigorous application performance monitoring. It will collect telemetry data from each software system or endpoint connected to the IT infrastructure. The data collected by the AIOps-based platform is then analyzed to find any vulnerabilities associated with the IT infrastructure. All of this is done by an AIOps based analytics platform without any manual interruption.
AI offers better insights for disaster recovery and protection
The role of AI can also be seen in protecting sensitive business data. Organizations have access to customer data and information that cannot be leaked. AI helps an organization in making better policies to safeguard their business data. For example, an AIOps based analytics platform can provide valuable insights that can be used to make a better disaster prevention strategy. AI-based tools perform business impact analysis to find which components of the IT infrastructure need better protection. They can also let you know about the impact of any IT issue in terms of data loss. An AIOps based analytics platform is much better than someone performing manual analysis. When you feed a data set to an AIOps based analytics platform, it can return valuable insights in less time. All the previous IT outages are recorded by an AIOps based analytics platform that helps it to provide better insights.
Role of AIOps in preventing data loss
All the log data, telemetry data, and customer data are continuously recorded by an AIOps based analytics platform. If some information is lost due to an IT outage, an AIOps-based platform will help IT teams in recovering it. AIOps is worthy even for complicated cloud-based IT infrastructures. Cloud architecture possesses temporary data and processes that are wiped off the next second. With AIOps based analytics platform, you can recall such processes and data in the cloud infrastructure.
Disaster recovery made easy with AIOps
When a power outage occurs within the IT infrastructure, you need to resolve it quickly to maintain service availability. AIOps enhances the disaster recovery process as follows:
- An AIOps bases analytics platform has data about the performance of each endpoint and software system. When an IT failure occurs, it quickly analyses the performance data to find out the root cause of an IT incident.
- AIOps can map the relationships between data entities and quickly identify the root cause of an underlying problem. It decreases the MTTD (Mean Time to Detect) for an IT issue and helps in getting the systems back online quickly.
- AIOps also helps in reducing the alert noise produced by the monitoring systems. All the redundant alerts for the same IT incident can be resolved with an AIOps based analytics platform.
- AIOps based analytics platform can help in prioritizing IT incidents based on their impacts on the service availability. For example, AIOps can identify incidents that could result in huge data loss. IT teams can quickly solve those incidents first to prevent the loss of information/data.
- AIOps also provides actionable insights once the root cause of an IT incident is identified. IT teams can acknowledge an IT incident better with AI-based tools.
The global AI market will be worth more than USD 185 billion by the end of 2025. Recent technologies like AI and big data are helping businesses to safeguard their sensitive business data. Start using AI for disaster recovery and boost service availability!