IHS Markit has found that every year, businesses lose more than $700 billion to IT failures, and network downtime is the top offender. According to Allianz Global Corporate and Specialty, IT failures and outages plus data breaches were the top two worldwide business risks in 2019. The Ponemon Institute reports that the average per minute cost of network downtime is $9,000.
Achieving network performance excellence
Maximizing network uptime and minimizing network costs is now essential to meeting financial objectives. Achieving end user quality of experience (QoE) goals by ensuring network excellence is necessary to be competitive and to reduce churn. As carrier and enterprise networks become larger, more complex, carry higher traffic loads and encounter more security concerns, the network operations team needs next generation tools for traffic planning, network monitoring, and troubleshooting.
A single next generation network solution for monitoring, fault discovery, automatically finding and resolving network anomalies using AI and insight into traffic growth guarantees the best possible user experience. The goal is to maximize network uptime and overall network performance. A software-based solution is needed to grow with your network and is easily updated eliminating any chance of future hardware obsolescence.
How to discover and fix network anomalies using AI
With a dynamic network infrastructure that spans legacy, virtual and cloud, it is difficult to know network trends, who is using what application, the application response time, or which area of the network is experiencing unacceptable performance.
Network teams need to understand the geography of their network and application flows, where usage is high, when response time is long and when voice and video sessions are experiencing poor quality.
But with networks getting more and more complex, identifying the source of the issue takes time, effort, and is hampered by way too much unfiltered data, all of which can increase MTTR and churn. Your network performance solution must therefore be able to filter , de-duplicate and correlate the network data to a manageable size.
Using AI to map your normal network data and continuously comparing that to ongoing traffic discovers any anomalies immediately. The system should then be able to automatically investigate the source and suggest a solution. The days of network engineers spending hours to identify the problem source no longer suffice in today’s networks so the whole discover-identify-resolve process must be automated.
Many causes of network failures can be anticipated. Events like hardware failures, traffic spikes that challenge network capacity and even cyberattacks. By establishing a base or “network DNA” allows network operators to pro-actively discover potential failures and internal or external attacks. rather than scrambling to find and fix the problem after the fact.
What is data analysis in real time?
Today’s networks are too complex and carry far too much data for IT teams to be able to effectively handle network monitoring and management functions with outdated manual approaches and technologies. In an environment in which every minute of network downtime can translate into thousands of dollars of financial loss, a strategic investment in tools and technologies to help ease this burden on IT teams can yield enormous cost savings that far outweigh the expense of the resources provided.
Enter the concept of data analysis in real time: DART. It enables network managers to be notified the instant the problem arise. It tracks in real-time, all network traffic and all application transactions and through automated alerts and resolution workflows, it vastly streamlines the process of addressing network issues and improve network excellence.
DART produces unlimited user-defined reports on voice, video, and application performance. It detects and reports on network TCP performance, RTP metrics, Network Usage, Latency, User and Application performance, Video QoE and User Identity. It then stores them in a centralized time-series database that establishes a baseline of network and application performance. Network operators then benefit from a coherent summary of data that homes in on the metrics that is relevant to the problem at hand.
DART yields tangible benefits
Organizations cannot sidestep every network problem, such as weather-caused outages or inadvertent cuts in fiber cable. But anticipating and heading off the ones that can be avoided brings concrete benefits that are worth the investment in the right tools.
These benefits will cut your organization’s costs and boost revenues and can easily be justified to management by crafting a business case that shows the financial benefits and reduced costs for adopting a network performance tool like DART.