5 Ways Data Science Is Improving Network Process
Due to the growing data flood, data science tools and methodologies have increased significantly over the past few decades. Any industry that provides services nowadays uses big data analytics, or data science as you may want to call it, to its fullest extent.
Organizations from various industries, including e-commerce, streaming services, healthcare, education, government organizations, and nonprofits, are attempting to make sense of their data. Because of this, data science has transformed from a mere buzzword to an absolute lifesaver. In order to remain ahead of the curve, businesses are working hard to hire the best data scientists. The demand for and quantity of qualified data professionals is significantly out of balance. If you’re a budding data scientist, you should arm yourself with the necessary knowledge and obtain certification from the best Data Science course in Delhi to remain ahead of your competitors.
Communications service companies have evolved over time to appreciate the beauty of data science. SDN/NFV technologies, which stand for software-defined networking and network function virtualization, respectively, are being quickly adopted by many service providers to run their services. Thanks to these technologies, organizations can use a self-service portal to obtain network capacity. The CDN forgoes proprietary hardware for an open, programmable global network infrastructure that can be managed from a central location. The NFV also allows for the delivery of features like acceleration and firewall/proxy from the network or customer premises equipment, enabling zero-touch provisioning when extra functionalities are required.
Let’s examine how SDN/NFV tools supported by data science can revolutionize how Customer Service Companies operate:
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Boost network efficiency, visibility, and management oversight.
SDN’s introduction has many advantages, including network-wide visibility, analytics, and management via a straightforward dashboard. A central controller chooses the most efficient path for each application’s traffic movement. It evaluates the required level of service quality, link health, workload priority for the company, and real-time congestion levels. This capability to swiftly evaluate traffic flow through various paths within a network improves redundancy.
While carrying out activities susceptible to latency faster, such as traffic acceleration, data science and AI can be useful at both the center and the edge of this complex network.
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Lower alertness fatigue
Since SPs migrated to SDN/NFV, the number of components that need to be tracked and controlled has multiplied. The overwhelming quantity of data and information these service-providing organizations have access to from the distributed components in the shape of logs and alerts is one of the most concerning problems. It is difficult for organizations to concentrate on crucial information when there is so much information, no prioritization, and a high false-positive rate. Data science makes understanding the context of these errors and ignoring the unimportant ones possible, which can result in a prioritized list of alerts for the SP operations team to evaluate and respond to.
This guarantees that cloud apps are responsive, simple to use, and contribute to improved customer experience and employee efficiency while minimizing network costs.
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Reduce expenses
SDN combines various computing, storing, and processing tasks into less expensive commodity computers, drastically lowering the capital expense. Simultaneously, data science and virtualization assist in automating various management duties and manual network configuration, lowering overall operations costs. As a result, there is much less need to attend branch office locations physically.
For some fundamental operational duties, most industry giants, including Facebook, LinkedIn, Netflix, etc., have already shifted to self-healing. Over time, a growing number of service providers will adopt “management by exception,” where most errors and performance declines are fixed by automatic self-healing based on data science.
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Boost protection
Security is one of the main draws of SDN for 45% of SPs, according to a study by the eWeek publishers. End-to-end traffic flows and emerging threats are under the supervision of the central SDN controller in the core network. These centralized SDN controllers can be taught to adapt to the threat environment, determine when something is malicious, and produce reports for the experts using data science and algorithms. While a virtual switch can be set up to filter packets at the edge of the networks and divert malicious traffic to higher layers of security, SDNs can be taught to send security updates out to main sites routinely.
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Proactively improving the network.
These service providers’ operations teams frequently battle to balance excellent performance and high availability. These teams must promptly locate and address any problems in their network.
These network devices generate enormous amounts of monitoring data, which can be rapidly processed using data science to identify recurring patterns and create precise models of their performance. Techniques for detecting anomalies can also be used to identify deviations from typical system behavior that may ultimately result in network breakdowns.
So these were the main 5 ways data science is boosting the network services. It’s indeed surprising that data science is found in a variety of fields. Hence, if you are planning to become a data scientist, sign up for an online Data Science course in Bangalore, which covers multiple data science methodologies and kickstart a career today!
