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The Future Of The Telco Industry And Impact Of 5G & IoT – Part II

Cloudera

The real opportunity for 5G however is going to be on the B2B side, IoT and mission-critical applications will benefit hugely. What that means is that this creates new revenue opportunities through IoT case uses and new services. 5G and IoT are going to drive an explosion in data.

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DS Smith sets a single-cloud agenda for sustainability

CIO Business Intelligence

Enabling consistency in the data sets from these varied sites is integral to DS Smith’s analytics strategy, as well as for anticipated changes in the company’s technology and business models, Dickson says. Here, Dickson sees data generated from its industrial machines being very productive.

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PepsiCo transforms for the digital era

CIO Business Intelligence

Tapped to guide the company’s digital journey, as she had for firms such as P&G and Adidas, Kanioura has roughly 1,000 data engineers, software engineers, and data scientists working on a “human-centered model” to transform PepsiCo into a next-generation company.

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Reference guide to build inventory management and forecasting solutions on AWS

AWS Big Data

Such a solution should use the latest technologies, including Internet of Things (IoT) sensors, cloud computing, and machine learning (ML), to provide accurate, timely, and actionable data. To take advantage of this data and build an effective inventory management and forecasting solution, retailers can use a range of AWS services.

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NJ Transit creates ‘data engine’ to fuel transformation

CIO Business Intelligence

Collectively, the agencies also have pilots up and running to test electric buses and IoT sensors scattered throughout the transportation system. IDC analyst Sandeep Mukunda says NJ Transit’s approach to data analytics has been very advanced. Lookman Fazal, chief information and digital officer, NJ Transit.

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When will AI usher in a new era of manufacturing?

CIO Business Intelligence

P&G engineers developed a high-speed data collection system to capture data to use for training AI models. One challenge they faced is that, while production errors are extremely costly and disruptive, they don’t happen often, which means that failure events are underrepresented in the training data. says Cretella.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

For instance, for a variety of reasons, in the short term, CDAOS are challenged with quantifying the benefits of analytics’ investments. Some of the work is very foundational, such as building an enterprise data lake and migrating it to the cloud, which enables other more direct value-added activities such as self-service.

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