Network analytics tools deepen with machine learning and AI
Network analytics has been changing the overall businesses performance for quite some time. More companies are mastering the use of comprehensive analytics strategy to increase efficiency, gain a more significant competitive advantage, and boost their business bottom lines. The challenge of analyzing vast amounts of network data in real time is creating increased demand for implementation of machine learning, and artificial intelligence as advanced network analytics tools with the use of cloud services places a higher value on security needs in the wake of greater distributed denial-of-service attacks.
Network analytics play a significant role in customizing user experiences through IT although analytics for the enterprise access network is a complicated process. Besides the array of connectivity options, accommodation of the blend of client devices and the different application models with proper utilization of input data embarks the pathway for the use of machine learning and AI to surface insights and recommend useful actions automatically as in real-time metrics and traps from infrastructure, APIs from application servers, actual data packets generated by real clients, and synthetic data packets generated by simulated clients.
From wireless technologies where organizations have been able to improve their Wi-Fi and wireless LAN performance to the companies in the customer engagement space using predictive approach for analyzing conversations, use network analytics tools that increasingly harness both machine learning and AI for diving more deeply into enterprise network's performance. AI and Machine learning transform network analytics by enabling organizations to make predictive recommendations, adding intelligence to digital marketing, predicting the likelihood of a sales lead conversion, providing intelligent guidance to agents, automating and streamline efficiency while also optimizing a customer’s lifetime value.