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Real-Time Service Dashboards and Insights using AIOps
In today’s digital era, businesses need to enhance the observability of their IT infrastructure. With high observability, business organizations have real-time insights about the performance of their software systems. However, lack of efficient service dashboards and real-time insights are hurting business organizations. Legacy monitoring tools are not efficient enough to provide real-time insights. This is why business organizations are preferring Artificial Intelligence for IT Operations (AIOps) for effective service dashboards.
What are service dashboards?
Service dashboards appear automatically when someone initiates a check type. Service dashboards will continuously run in the background. They will accumulate the performance data from different software systems and convert them into meaningful insights. You cannot extract anything meaningful from a large chunk of unstructured data. To understand the data, it should be visualized appropriately. Service dashboards visualize the data in a way that can be understood by the IT teams. Since service dashboards appear automatically, you may get to know about an incident within the IT infrastructure easily.
Many dashboard builders in the market let you customize your service dashboard. Many traditional monitoring tools come with built-in dashboards for displaying analytics. However, the issue with the traditional analytics dashboard is that it cannot show real-time insights. With AIOps based analytics platforms, effective service dashboards can be integrated that show real-time insights. Service dashboards can help with generating instant insights for several performance metrics. For generating real-time performance insights, service dashboards will need a real-time monitoring tool. It is where AIOps comes in handy for generating real-time performance data.
Real-time insights with AIOps
Artificial intelligence forms the core of an AIOps-based monitoring system. The idea behind using AI algorithms is to eliminate the need for manual efforts. AI algorithms can automatically make decisions and perform monitoring. Even if you hire many system administrators, you still cannot perform non-stop monitoring manually. AIOps based analytics platforms keep running even if no one is at the office. It is very helpful in the current scenario of remote work culture due to the recent global pandemic.
AIOps not only generates real-time insights regarding the system performance but also regarding the security of IT infrastructure. For example, if a hacker is launching port scans against your IT network, an AIOps-based solution could block it in real-time. The big question is – How does AIOps deduce real-time insights?
Rich observability leads to real-time insights
Rich observability of organizational systems means you can understand the internal states easily. How well are you able to understand the internal states of your software systems based on external outputs? A software system that is not observable will be of no use to the IT teams. IT teams cannot uncover the dependencies of the software system. You cannot predict the exhaustive capacity of a software system if you don’t know about its internal states.
AIOps-based enterprise solutions provide rich observability into software systems. With rich observability, IT teams can get to know their software systems better.
- Data is generated from different sources in the IT infrastructure of an organization. AIOps based analytics platforms can acquire data from different sources continuously. Temporal data, log data, and many other types of data are collected continuously by AIOps. Acquiring data on such a large scale is not possible with manual efforts. With ample performance data, AIOps can extract meaningful insights.
- Data collected from different sources need to be aggregated for better data analysis. Best AIOps products and tools in the market can correlate data from different sources without any manual interruption.
- Besides collecting performance data continuously, AIOps based analytics platforms also perform rigorous data analysis. AI/ML algorithms continuously filter noise and derive patterns between the data. Real-time insights will keep appearing on your service dashboards with a reliable AIOps-based monitoring solution.
Finding the underlying cause with AIOps
One of the biggest challenges for IT teams is that they cannot find the source of an incident. Incidents are unwanted IT issues that need to be fixed immediately for business continuity. Much time is wasted in identifying the epicenter of an IT issue due to a lack of insights. The time taken to acknowledge an IT incident increases drastically and, so does Mean Time to Resolve (MTTR). This is where real-time insights provided by AIOps come into play. Intelligent AI/ML algorithms can quickly identify the underlying cause of an IT issue. AIOps uncovers the interdependencies within the software systems which, helps it in identifying the source of an IT disaster quickly.
The biggest advantage of AIOps is that it generates real-time system alerts without any noise. When many system alerts are generated by the monitoring system, it is difficult to distinguish which incidents are more important than others. AIOps based analytics platforms are beneficial to IT teams for prioritizing IT incidents based on their impacts on the business operations.
Proactive incident management with real-time insights
Besides providing real-time insights, the best AIOps products and tools also provide actionable insights. When your service dashboard displays a system alert, it will also show certain actions that can be taken to resolve the IT issue. You don’t need to waste time deciding which department has the best resources to solve a critical IT incident. An AI-led monitoring platform can guide teams to acknowledge an IT incident faster. Your support teams will quickly resolve an IT incident, thus, boosting service resilience.
Conclusion
The global market size of the AIOps industry will be more than USD 600 billion by 2030. More and more businesses are preferring service dashboards that are backed by AIOps based analytics platforms. You can also manage your IT incidents and system failures proactively with real-time insights provided by AIOps.
We have been hearing and using the term ‘Big Data’ for a while. Though there could be multiple interpretations of it, one common explanation is, Big Data represents the acquisition, storage, and processing of massive quantities of data beyond what traditional enterprises used to. Also, the data need not be in conformity to any schema, business rules, or structure.
In the recently announced Technology Trends in Data Management, Gartner has introduced the concept of “Data Fabric”. Here is the link to the document, Top Trends in Data and Analytics for 2021: Data Fabric Is the Foundation (gartner.com).
As per Gartner, the data fabric approach can enhance traditional data management patterns and replace them with a more responsive approach. As it is key for the enterprise data management strategy, let us understand more about the details of data fabric in this article.
In an IT Organization, what is measured as success.? Predominantly it inclines towards the Key Performance Indicators, internally focused metrics, SLAs and other numbers. Why don’t we shift our performance reporting towards ‘value’ delivered to our customers along with the contractually agreed service levels? Because the success of any IT operation comes from defining what it can do to deliver value and publishing what value has been delivered, is the best way to celebrate that success.