Our current era places significant value on analytics when making business decisions. From baseball to economics to computing, it’s impossible not to hear how analytics can improve business. Advanced statistics provide a huge advantage and detailed knowledge to any team. Considering how intricate modern networks are becoming, having data-driven awareness is more important than ever.
The key purpose of network monitoring is to improve the experience for your employees, customers, and users. Knowing everything about your network allows IT teams to understand what users are dealing with, how to fix problems quickly, and it allows them to create detailed plans to improve network performance.
A major selling point for network performance monitoring solutions is the data collection. Without the right network analysis, fixing downtime and performance issues can be an arduous task. Automated network data collection allows your team to be proactive, fix problems quickly, and build the best possible network for your company’s needs.
Users, from a workplace and customer perspective, are more demanding than ever when it comes to network performance. A simple outage or bug can lead to tremendous outrage from an online customer base. Traditionally, IT teams heard about network problems directly from these frustrated users. Users often don’t report their issues, so some problems could go unresolved or be more widespread than the team might realize. With modern network performance monitoring solutions, IT teams will seldom have to deal with customer feedback.
Network monitoring allows teams to see everything happening at any given time on your network. You’ll be able to see what is affecting users as it happens, and you’ll be able to fix the problem as quickly as possible. Improving user experience is critical to any business. Having the tools to be ahead of your network issues is crucial to reputation and financial success.
Many solutions offer up to the minute metrics to keep your team informed. They can allow teams to build custom alerts. Alerts arrive during anomalous network activity, error messages, irregular payload size or any other network behaviors a team might want to look out for. Your team can also set thresholds of acceptable and unacceptable performance metrics. Teams will have the ability to be proactive about fixes, instead of being reactive and putting out fires.
Managing a modern network requires getting information to fix issues before they become a problem for users. In addition to providing the alerts and information, network monitoring solutions can utilize the information on their own and give teams suggestions on how to fix problems that might arise.
Tracking network trends allow your team to make decisions in an informed and data-driven way. Having a detailed history of the problems that occur on a network will make efficiency-related changes more valuable.
Understanding where consistent problems occur, as well as who these problems impact, leads to an improved network strategy. In addition, network related technological advances become easier than ever to implement. For example, automation in network monitoring starts with knowing exactly what to automate. Detailed data on the strengths and weaknesses of a network makes this process efficient and meaningful.
Almost all enterprise technology solutions rely on data in some way or another. Network monitoring tools use machine learning to enhance automation and make networks more secure. Machine learning provides the ability to recognize abnormal user behavior to prevent attacks and lock threats out. Earlier this year, Tesla’s cloud platform was being used by cryptojackers for several months without anyone noticing. A machine learning security tool would have allowed the company to recognize they were using more processing power than normal.
It’s easy to recognize the benefits machine learning could have in the cloud. The cloud has almost unlimited possibilities in today’s enterprise computing space. One area that will see an obvious benefit is cloud application workloads. Developers won’t be as worried about using unsecure code in the application, as the machine learning tool will recognize and eliminate this. From there, application performance monitoring will be more efficient, as automated data collection and reporting will alert developers of any problems they may not have noticed.