What is IT Event Management?
Events can define as the piece of data which gives the information about the system resources. It can be triggered by any incidents, changes in the status or configuration of resource which affects the system resources. Generation of events can be considered as a reminder if any manual action is to be taken or it may be used as a notification if any activity has occurred. It can also be defined as the detectable incident that has some significance for the management of the IT Infrastructure to deliver the IT services. The primary objective of introducing Event management in IT infrastructure includes:
- Ability to detect, translate and initiate the required action for events.
- The basis for operational monitoring and control.
- It provides the gateway for many service operation activities.
- Gives operational information, warning and exception to support automation.
Challenges in IT Event management
Managing each event in the IT Infrastructure is the most significant challenge. The common problems facing the IT industries in maintaining the particular events are:
This is one of the biggest challenges that an IT Event management companies are going through as it is required to manage multiple data platforms. This is because the different database is used to interact with events and hence it creates difficulties in tracking the single event accurately.
Loss of data fidelity:
It is essential for any organization to keep the data secured as this data might have the confidential information and hence can cause the security problem.
Mutual events interpretation:
As the data flows with high velocity, there is a high probability of misinterpretation of the event. This interactive event interpretation will again the biggest challenge for the enterprise as it is tough to make the correct decision.
Benefits of Machine learning on IT Event management
Prediction and decision making
The important application of machine learning is prediction and decision making. Machine learning technology helps computers in such a way that computers can read and analyze more information than human. Hence one can trust on computers for decision making to stand apart in this competitive era.
For example, in the process of recruitment decision has to be made to recruit a right person for the job. The machine learning technology can do this decision making by implementing some algorithm in the recruitment process.
To make faster and more accurate decisions
The best example to quote on how machine learning is best for making the fast and accurate decision is price optimization in the hotel business. Machine learning algorithm is capable enough of identifying the optimal room price at any point in time by analyzing the real-time demand, review of the customers and also by examining competition from surrounding hotels. This algorithm is smart enough to determine the real-time data and adjust the price automatically after doing some predictive analysis to increase the profitability of the events.
Another popular application of machine learning is Sentiment prediction. It is also known as opinion mining. The opinion mining can help the organization to determine the employee’s opinion on a specific product, services or topic. This sentiment analysis is done by analyzing text such as online conversations, web comments, social media posts, etc.
Curate and categorize the content
Machine learning is the best solution to categorize and to curate this vast amount of content automatically. YouTube is the best real-time example of this type of machine learning application as it utilizes the machine learning algorithm for suggesting the content and help the viewer to find the relevant results for them.
At perfomatix, we are leveraging the machine learning techno?”?
“logy to develop a new architecture for the IT infrastructure event management. Talk to us today for a free consultation on your data-driven event management.