Four Ways Data Science Can Boost Network Performance
Four Ways Data Science Can Boost Network Performance
Because of the ever-increasing data deluge, data science tools and methodologies have increased dramatically over the last few decades. Today, it isn't easy to find a service area that isn't exploiting the sheer power and might of big data and analytics, or Data Science, as you call it.


Organizations across domains attempt to make sense of their data, from e-commerce to streaming platforms, healthcare to education, and government agencies to non-profits. As a result, Data Science has progressed from simple jargon to a true savior. Organizations that want to stay ahead of the curve are working hard to hire the best data scientists. There is a substantial supply-demand imbalance for competent data professionals. If you're an aspiring data scientist, you should arm yourself with the necessary knowledge and obtain a Data science certification course in Delhi to stay ahead of your peers.

Consider how SDN/NFV solutions backed by data science may transform Customer Service Provider operations:


  • Enhance network visibility, performance, and management controls.

SDN has provided several benefits, including network-wide visibility, analytics, and control via a single dashboard. A centralized controller calculates the appropriate path for each application's traffic flow. It assesses real-time congestion levels, connection health, workload priority to the company, and necessary service quality. The capacity to rapidly review routing traffic over several pathways in a network promotes redundancy.


Using data science and intelligence at the core and the periphery of this complex network can help with jobs that are prone to delay, such as traffic acceleration. This guarantees that cloud apps are responsive and simple to use and contribute to increased staff productivity and a better customer experience while lowering network costs.


  • Improve Security

According to the publishers of eWeek, security is one of the main draws of SDN for 45 percent of the SPs polled. The core network's centralized SDN controller manages end-to-end traffic flows and emerging threats. These centralized SDN controllers may be trained to adapt to the threat landscape, make choices when something is harmful, and deliver reports to specialists using data science and algorithms. SDNs can be trained to frequently deliver security upgrades to central sites, while a virtual switch can be configured to filter packets at the network's edge and divert hostile traffic to higher levels of protection.


Only the application of Data Science has enabled this multi-layered approach to security. Traditional hard-wired networks with strong security standards cannot match such granular insights into traffic and the capacity to react in real time.


  • Reduce expenses

SDN consolidates multiple computing, storage, and processing operations onto lower-cost commodity servers, resulting in significant capital savings. Simultaneously, data science and virtualization aid in manual network configuration and the automation of many administration chores, lowering total operations expenses. As a result, the necessity to physically visit branch office locations is greatly decreased.


Most of the tech giants, such as Facebook, LinkedIn, and Netflix, have already adopted self-healing for certain fundamental operating functions. Over time, more service providers will shift to "management by exception," where most frequent failures and performance degradations will be addressed by automated self-healing utilizing Data Science.

  • Network Optimization That Is Proactive

These service providers' operations teams frequently struggle to strike the correct mix between good performance and high availability. These teams must be proactive in identifying and resolving network emergencies.


Using data science, it is feasible to swiftly evaluate the massive amounts of monitoring data generated by these network devices, identify repeated patterns in the data, and create accurate performance models. Anomaly detection techniques can also be used to detect deviations from typical system behaviour that could lead to network breakdowns. 


To become a certified data scientist in any domain, join the IBM-accredited Data science course in Delhi.