Big data reporting

Mobile network performance

Devices and connected objects are fantastic information sensors. Smooth operation of mobile networks is a crucial matter for a great many enterprises including mobile operators, as well as for any business that uses critical applications in a mobile environment.

Did you know that every mobile device, every communications module can contain or generate precious data? Did you also know that these data can be a source of income for your business?

Through its solutions for massive data collection and real-time analysis, ip-label processes the data that your systems yield, correlates them, and highlights their value for enterprises which have a crucial need for them.

Big data

A single data repository!

Data take on their full meaning when they are presented back-to-back with other data. For example, measurements from crowd-sourcing applications (production of participative data) must be analyzed in relation to data from controlled measurement systems (probes, for instance) to establish their validity.

Furthermore these data take on greater significance when they are correlated with business data or application usage data.


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ip-label guides you through the data revolution

ip-label helps you to define the data that is pertinent for you, assists with the collection of this information (anonymization, realtime streams, etc.) and works with you on a business model. Your data become a new source of revenues!

Big data reporting analytics

ip-label expert

The R&D teams at ip-label develop measurement technologies that match every environment (hardware probe, mobile, Windows, Linux, Android, iOS): active testing (latency, speed, capacity, video, audio, etc.), passive testing (mobile operator, type of network technology, cell, MNC, MCC, signal level, position, etc.) in a wide variety of contexts.

As a player in the cloud, ip-label has designed and deployed powerful real-time data collection platforms, now backed by big data structures. These structures allow real-time access to data. Sophisticated analysis algorithms have also been developed to detect errors or deviations and notify when anomalous situations occur, make predictions, and automatically establish typologies.

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