In modern times, the network is becoming an integral part of operations for business subscribers. The majority of organizations operating worldwide rely on the network to process business transactions, interact with clients, access enterprise applications, etc. Any disruption in the network can cause business operations to stop and induce a huge loss to the organization.
In most cases, a dedicated team is responsible for fixing network issues and ensuring that the network is functional and performing optimally. They operate in conjunction with the network operations center, which notifies support teams when they receive a ticket or an alarm from a network monitoring tool. From there, the team starts troubleshooting the network to find the root cause of the issue.
This approach, however, is reactive as it waits for issues to occur before taking action instead of preventing problems in the first place. With predictive analytics, network engineers will have the ability to detect potential network disruption and performance issues before they affect operations.
Importance of Predictive Analytics in Network Performance
Predictive analytics is a subsection of data analytics that makes use of historical data to predict future events. It uses different algorithms and techniques to identify patterns and project trends that can be used to deduce what can happen next. The concept has been used in multiple applications such as in sales forecasting, risk assessments, and many more.
Predictive analytics is already being used in the network space as a tool to help identify potential network disruption. To further understand its importance, we have listed some benefits of using predictive analytics in monitoring network performance.
Prevents Network Disruption
A network disruption can have a huge impact on the operation of a customer’s business. Such disruptions can be caused by an issue in the network like a faulty network device, a fiber cut, or an overloaded link or hardware. With network predictive analytics, these issues can be prevented or can be resolved faster.
For example, the network analytics shows that a link has been hovering around 90% utilization and packet drops are being seen due to this in the past 7 days. If there is a redundant link, the network engineer can proactively failover to the more stable link to avoid the link being overutilized should a spike in traffic occur in the future. If there is no redundant link, bandwidth on the primary link can be upgraded to cater to the increase in traffic.
Faster Resolution of Issues
Network issues can be more complex than simply having a highly utilized link as described in the previous example. In such cases, the time it will take the network engineer to troubleshoot and fix the issue will be longer. This is common for issues where you start troubleshooting from scratch and gather data from the physical to the application layer. With predictive analytics, you can have a quick overview of the network performance in a specific time frame.
For example, a user has been experiencing network issues in the past 3 days. The engineer looked at the data before the issue happened and he noticed that there have been incrementing errors in the port where the customer’s internet is connected. With this data, he was able to easily determine that it was a physical cabling issue, which saved time in troubleshooting instead of communicating with the customer back and forth. (More information on this strategy to rapidly and reliably diagnose with low mean time to repair is available in this whitepaper.).
Network Performance Optimization
Apart from having a network connection, it is also important for a business customer to have fast and stable network performance. Imagine if a user needs to download a file from the internet which is 10GB in size and the network speed is only 5 Mbps and is shared by multiple users. It will take the user a long time to download this compared to the convenience of having a 50-Mbps (or greater) network speed.
Predictive analytics uses network trends to identify potential issues that may occur in the future. If the trend shows that the bandwidth has been over-utilized in the past few months consistently, the network engineer can proactively suggest a bandwidth upgrade.
Another good example would be a situation where data shows an incrementing latency trend in an internet circuit over the past few days. In this case, the engineer can either manually failover the traffic to another circuit or configure a network device to automatically failover traffic to another link if a specific latency threshold has been reached.
Protection from Security Threats
Cybersecurity is significant to any business, but especially national infrastructure. Protecting data assets and sensitive information from attackers is one of the highest priorities. Hackers are continuously targeting national infrastructure and looking for weak points in security. With predictive analytics, the network operator can analyze and identify any abnormal behavior which in turn can prevent the attacker from causing further damage.
In summary, predictive analytics will be one of the most helpful tools in the next decade and eventually one that every company deploys at scale. This tool will help telecoms decrease resolution times, improve network performance with increase protection against incoming threats.
Tom Ayling is CEO of Gisual, a software that automates the workflow of diagnosing and correlating off-network outages. Gisual proactively notifies telecoms of the outage source, root cause and restoration time. This reduces mean-time-to-repair, eliminates trucks rolls, and decreases cost-per-call.