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According to the 2021 Market Guide released by Gartner, communications service providers or CSP IT operations need to make use of AIOps platforms now more than ever. AIOps (Artificial Intelligence for IT Operations) in CSP IT operations are capable of improving decision-making based on operational data insights and IT automation.
AIOps covers a range of solutions when it comes to CSPs including IT operations deviating from traditional software, networking, media and information services, and application services. The use of AIOps by CSPs allows the leveraging of the improved AI infrastructure to create a more accessible and optimized platform. CSPs have relied on service assurance to maintain a standard of user experience. With the help of AIOps, these services can be enhanced. Read on to know more.
How can AIOps provide service assurance to CSPs?
Service assurance requires a set of processes that can optimize not only the performance of communication networks but also the solutions available for the management of those networks. For service assurance, CSPs can use AIOps to improve the following aspects:
1. Dynamic and Updated Client Profiles
Clients or subscribers often change preferences and patterns on communication platforms and channels. CSPs need to adapt to these changes in order to provide relevant services. AIOps service assurance solutions help to create client profiles that are constantly updated according to the changes. Such dynamic profiles allow CSPs to offer personalized service to targeted clients and expand their customer base.
2. Standardizing and Contextualizing of Data
Decision-making or improvement of services depends on the insights available from current and historical data. By using AIOps for the standardization of that data and then contextualizing what is operational, the analytics can be more accurate. This also helps in the augmentation of service management using IT operations management software and in the promotion of automation. AIOps enables the usage of such operational data to optimize various channels that provide communication services including social media, web and mobile applications, and cloud service.
3. Quick Identification of Problems
In communication services, AIOps can be used to establish dynamic baselines. These baselines can then be used to detect discrepancies or anomalies that can affect the quality of service being provided. AIOps is able to identify such issues very quickly and come up with solutions to resolve them. Machine learning not only detects the problems but identifies the root cause and clusters of issues to eliminate them effectively. Troubleshooting becomes much easier and there is no need for any manual processes, thus reducing the risk of human error.
4. Better Prediction of Outcomes
AIOps tools can predict the outcomes of services even before they have been released into the market. These predictions are based on past events and customer responses and are therefore quite accurate. CSPs can opt for automation, instead of depending on manual analysis and can get predictions that will help them implement the necessary changes to their services.
5. Solutions for Improved Customer Engagement
While customer engagement is a two-way street, CSPs need to figure out various ways to reach out to the customers. Subscribers will usually only call if there is an issue that needs to be fixed, however, there can be diverse methods of customer engagement that are beyond just technical support. The best AIOps platforms software solutions can be used to gain real-time insights from customer activity. These insights can then be used to predict and determine how customers are responding to the service provided and which areas need to be improved or optimized.
6. Approaching Zero-Touch Networking
Zero-touch networking operations have not been fully realized but service assurance from AIOps can help to approach such a state of network automation. Zero-touch networking means that the network can operate completely on its own and this includes assessment and analysis of events, decision making, as well as prioritization of resolutions. Since AIOps effectively reduces the need for human intervention, the goal of zero-touch networking operations is much more achievable today, than it was a few years before.
Does AIOps provide network automation for service assurance to IT operations?
Network automation is an integral part of all approaches towards service assurance. Service assurance includes the use of the best AI auto-discovery tools and real-time insights that are policy-driven and accurate. Network automation can be employed for the enhancement of network performance and capacity. This is usually done through end-to-end automation. Autonomous networking helps to eliminate the need for human involvement and improves management, scheduling, assessment, and analysis of services.
Increase in Observability in AIOps Service Assurance
Observability in communication services goes beyond just the operations of IT systems and monitoring and management of applications. It involves the complete setup or matrix of communication systems and networks. When the entire system becomes observable, CSPs can make use of enhanced and optimized AIOps digital transformation solutions that are not dependent on the variability of any data. Since it involves the implementation of machine learning and algorithms, there is no need for any specific or targeted programming, other than what AIOps already provides.
Introducing automation to service assurance includes observability and this assists in data analysis and prediction of anomalies. The observability of a platform can affect its ability to prioritize and resolve issues. Completely observable communications systems can also leverage AIOps better to reduce error as well as downtime, especially when applied accurately to service assurance.
The telecommunications market depends on service assurance and AIOps can completely change the face of communications with digital transformation services and solutions. CSP systems usually include predictable operations that are deterministic. However, the use of artificial intelligence helps to introduce intent-based, observable systems with end-to-end automation. This can easily identify and fix errors before they affect the service provided or compromise user experience. AIOps frameworks help to overcome the shortcomings of traditional software and allows CSPs to optimize available operations and improve newer networks.