Proactive analytics uses AI, data analytics, and machine learning to make networks smarter, provide intelligent insights, and identify and troubleshoot issues.

Building a smarter network

With proactive analytics the network is smarter. Artificial Intelligence (AI), data analytics and machine learning, provide correlation among events on the network, user and device behavior, enabling the network to understand the Quality of Experience (QoE) of its users.

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Proactive analytics can simplify troubleshooting to identify problems when operations dip below a specified threshold. For example, based on a continuous analysis of connection times, the network can quickly identify if there is an issue. If it notices a change it will take action to solve the problem. The network will send a notification to the administrator to indicate if the problem can be automatically fixed. Predictive analytics can also help IT administrators anticipate and address potential issues before they become real problems in the network.

Proactive Analytics in Industries

Proactive analytics in healthcare: With proactive analytics, healthcare providers are better able to understand their business and can use the data to optimize departmental workflows and processes to deliver the best possible patient experience.

Proactive analytics in hospitality: Proactive analytics help hoteliers make informed decisions to optimize network usage, and grow their business. Predictions about future network needs are possible based on present resource usage and inventory information. Location analytics can be used to fine tune marketing strategies and offers aligned with the peak/valley hours associated with the hotel amenities.

Proactive analytics in education: Proactive analytics can provide specific and quantifiable information that can be compared benchmarks in other institutions in order to develop recommendations for improvements. It can also provide student data such as class attendance, application and device usage to help understand student success.

Proactive analytics in transportation: Proactive analytics can provide Quality of Experience (QoE) statistics to optimize passenger’s experiences, and improve efficiencies. Transport operators, in particular railway operators, have extensive predictive maintenance schedules for equipment such as rolling stock. Proactive analytics can bring significant value to the maintenance cycle, and to the business.

Proactive analytics in government: Proactive analytics can be leveraged to improve public sector services, and detect safety issues associated with large groups in specific areas. It can also provide the data to help detect anomalies and reduce the potential for cyber-attacks within a network, and it can minimize or eliminate down-time related to maintenance issues.

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