Ready for 5G ? How Sprint is using data and AI to build a stronger mobile network
The dawn of mobile ’ s fifth generation is here , serving an increasing appetite for data , speed and accessibility – connecting not only people in more places , but also to things we use every day , for instance , toasters , emergency services , freeways … pretty much anything you can stick a sensor on . With the 4G world disappearing in the rearview mirror , mobile networks are only going to get faster .
Network communication companies such as Sprint have rose to the challenge to make sure the vision of 5G is indeed a reliable one . Facing its own path toward digital transformation , Sprint started preparing their data for Artificial Intelligence ( AI ) – with the goal of using machine learning algorithms to gain near , real-time insights and increase responsiveness to customers .
The success of Sprint ’ s digital transformation hinges on the ability to quickly discover , organize and present the right data at the right time to those teams that make decisions that impact the customer journey . IBM Cloud Private for Data , a leading enterprise insight platform * proved to be the right solution for Sprint – enabling AI projects in a shorter timeframe through unifying and simplifying four critical stages in the journey to AI : the collection , organization , analysis , and modeling of data .
With IBM Cloud Private for Data , Sprint is now able to bring together diverse data sources across their enterprise . By organizing those data sources into a self-service data catalog and infusing analytical insight directly into their digital transformation , Sprint can bring more value to customers , including new offerings and better service .
Sprint ’ s business analysts and data scientists are expecting a measurable productivity increase by leveraging their new self-service access to enterprise data and the integrated artificial intelligence tools that IBM Cloud Private for Data is a part of .
An example of this is a recent collaborative project between Sprint data scientists and the IBM Data Science and AI Elite team . Sprint had a goal to understand the correlation and predictability between communication network alarms , the opening of trouble tickets and the physical dispatches of people and parts to fix equipment .
The IBM Cloud Private for Data platform gave teams an easy way to quickly ingest millions of past alarms , trouble ticket data and past people and parts dispatches .
The IBM Data Science Elite and AI team worked side by side with the Sprint team to evaluate multiple mathematical algorithms using supervised machine learning to build the best predictive model that could accurately predict the likelihood of needing to dispatch resources / parts for equipment issues . Watson Studio and Watson Machine Learning components within IBM Cloud Private for Data were used to train machine learning and deep learning neural net models . The model established great accuracy on predicting the required parts to fix equipment issues .
Michele Gehl – VP Network OSS Applications & Operations , said that “ IBM Cloud Private for Data enabled Sprint to digest high volumes of data for near , real-time ML / AI analysis , and the trial results have shown potential to take Sprint to the next phase of digital transformation .” Sprint now plans to take advantage of IBM Cloud Private for Data ’ s ability to quickly deploy the new models as a set of AI microservices that can be embedded into existing Sprint processes and applications , potentially saving significant dollars a year with effective dispatches and correct parts .
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