Edge computing critical for businesses to harness data for dominance


Intel’s new report, ‘The Edge Outlook’ identifies edge computing as a critical factor that companies must harness to navigate and understand data for current as well as future business needs. The edge computing is claimed to make it possible for every single object to store information and for that information to be extracted and used in real time.
Technology use has grown exponentially during the pandemic, generating new, unprecedented volumes of critical business data. This data will be central to the digital transformation of many businesses, but many organizations are facing very real data processing challenges. For example, it’s impractical to send the sheer volume of data now being created back to the cloud for processing due to latency issues. This is where edge computing can play a critical role in driving efficiencies and underpinning the future growth of business, as per Intel.
The paper provides insight into the now, new and next of edge computing across key industries. In short, the edge is where businesses can turn ambitious plans into reality. Businesses are realizing that the edge is integral to unlocking future innovations — 76% say that identifying “the ideal location” for data processing is a challenge.
This report aims to help IT leaders on how to use edge computing to drive operational efficiencies, create new products and open new revenue streams using real-world success stories. Here are the key insights:
Retail: Data analyzed at the edge corrects massive amounts of inventory distortion, while making supply chains and product development incredibly efficient. The edge provides retailers with real-time consumer behavior analysis, empowering them to deliver more personalized experiences. Intel customer WonderStore’s shop window conversion rate improved by nearly 17% since deploying edge technologies. This was achieved by using visual sensors and real-time analysis powered by edge technologies to customize store experiences based on customers’ fashion choices, sentiment and dwell time.
Industrial: AI-based robotics are used to perform repetitive and potentially hazardous tasks with greater speed and accuracy than humans. Machine vision is also used to validate features and check for defects, helping to deliver the highest-quality product possible. These edge deployments helped Intel customer Audi boost weld inspection speed by 100 times with just 18 milliseconds of latency. As a result, labor costs have are down by 30 to 50% at its Neckarsulm, Germany, site, one of the company’s two principal assembly plants.
Healthcare: Edge computing helps deliver a higher quality of care and clinical efficiency by enabling frequent patient monitoring and data collection, integration with electronic health records and AI-powered patient data analysis. Deep-learning inference is used in image-based diagnostics to speed the detection of health issues and save lives. Thanks to edge technology, Intel customer Philips managed to speed CT scan imaging by 188 times without the need to add hardware acceleration.
Telecommunications: Driving network and operational efficiency, machine learning can help telecom operators increase network and operational efficiency to meet rising service level expectations while simultaneously reducing costs. With AI and analytics-based engines, operators gain the ability to intelligently manage 5G networks to achieve key network KPIs, network automation, energy savings and operational flexibility to serve a wide variety of 5G and edge use cases. Intel recently helped Japan’s Rakuten Mobile develop the world’s first container-based, fully cloud-native network. They are using edge data centers to provide rapid response times for applications and rich media content – enabling their mobile network to support immersive, multisensory experiences for customers.

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