With the advent of IoT and 5G, mobile technologies are becoming increasingly complex. This growing complexity suggests that automation is the future of networks.
Network administrators are increasingly applying AI in networking infrastructure to make these complex environments more manageable.
A self-aware system has the potential to improve the health of technological systems by enabling the computer to practically diagnose itself and determine how to improve. With many companies looking for new efficiencies, automation opportunities and value from their IT operations, it’s no surprise that AI is playing an increasing role.
According to a recent report from telecommunications company Ericsson, approximately 19% of telecommunications organizations around the globe are planning to adopt AI within the next three years. When it comes to applied machine learning for aiding IT organizations, AI for predictive maintenance and network management is proving particularly valuable. Companies are using AI to predict and detect network outages which can help prevent unplanned network downtime. The ITU journal found that AI is being applied to situations where there is a shortage of human resources in various fields.
Are network operators becoming data scientists?
In the IT field, where there’s an abundance of data, the role of data science is crucial, as it provides insights from this data. Using machine learning, network operators can automatically cluster and categorize data, identifying patterns and spotting anomalies and outliers. Automating the data analysis enables network operators to more effectively identify and study patterns related to how the networks are being used, failures, outages and congestion-related issues, much as data scientists currently do with big data. While there is a widespread shortage of data scientists, adding data science capabilities to network and IT tools provides needed capabilities without requiring more data science talent.
IT professionals are using machine learning to craft AI systems that acquire the most relevant information and speed up the process of studying data. IT operations analytics, or ITOA, is an emerging approach to using big data and machine learning to optimize IT operations. AI systems are helping IT operations to be more proactive and discover trends and patterns in data. AI can gather information from a variety of sources, including livestreaming data or data that has been logged over time from infrastructure and the network operating system. When ITOA has incorporated AI in the background, the process for identifying problems, addressing those problems and mitigating potential future issues can be done faster and more consistently.
Another growing area is the use of AI to enable more sophisticated automation. Network providers are relying more and more on automation to manage their widely distributed and highly heterogeneous infrastructure. Some of this includes automating tasks they are currently doing manually, such as updating settings, maintaining patches, updating vulnerability checks, adjusting system configuration settings, and other things related to keeping systems up to date and responding to change such as adding or deleting users or devices.
This need for AI-assisted management will only continue to increase as 5G networks get rolled out. Machine learning is being incorporated by network administration for the monitoring and detection of problems across the network.
AI is also proving valuable for automatic maintenance. To provide optimal service for the consumer and to retain the same quality for a mobile network, automated systems using AI are keeping a watchful eye over systems and can help spot and predict failures at a much faster rate than humans.
AI is changing the network operations workforce
Some wonder if AI technology is pushing network administrators to adopt the role of data scientists. While there is little quantifiable evidence that network operations and IT operations roles are shifting into data science roles, there is anecdotal evidence that indicates the transition is underway. Using AI and data science skills, a more sophisticated form of network operations will help businesses turn unorganized information into insights that create opportunities for action and change. This turns the role of network and IT operations from a tactical one, focused on addressing today’s and yesterday’s problems, to a strategic role that can help organizations plan for future business possibilities and address their IT infrastructure needs as part of that strategic vision.
Companies are realizing that by incorporating AI into their existing workflows, systems and processes they can gain efficiency. As IT networks continue to become more complex and the amount of data generated continues to increase, the adoption of AI will also continue to grow.