12 10 2020 16 38 42 120411

Businesses are realizing that edge computing is integral to driving operational efficiencies and unlocking future innovations, according to an Intel report that provides insight into the now, new and next of edge computing across key industries.

Edge computing is a distributed IT architecture where computing (collecting, storing, processing and analysing data) takes place at or near the data source. This improves bandwidth availability, insights, response time and ultimately customer experience.

In the COVID-19 era, unprecedented volumes of critical business data is being generated but many organizations are facing very real data processing challenges. As per the Intel report, 76% stated that identifying “the ideal location” for data processing is a challenge. Edge computing can play a major role in addressing these challenges, driving efficiencies and underpinning the future growth of businesses.

“The edge makes possible a world where all of a sudden, every single object has the potentiality for information – information that can be extracted and used in real-time,” says digital pioneer and AI scientist Inma Martinez.

According to the Intel report titled “The Edge Outlook”, businesses can no longer afford to ignore the edge and they must harness it to successfully navigate and understand data – both now and into the future. The report also guides 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:

  • In the retail sector, data analyzed at the edge corrects massive amounts of inventory distortion, while making supply chains and product development incredibly efficient. With edge, retailers can analyse real-time consumer behaviour and deliver more personalized experiences
  • In the healthcare industry, edge computing can help unlock even more benefits of telemedicine. It 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.
  • In the industrial sector, AI-based robotic process automation systems are used to perform repetitive and potentially hazardous tasks with greater speed and accuracy as compared to humans while machine vision helps validate features and check for defects in order to deliver the highest-quality product possible
  • As for telecommunications, machine learning can help telecom operators increase network and operational efficiency. With AI and analytics-based engines, operators can intelligently manage 5G networks to achieve key network KPIs, network automation, energy savings and serve a wide variety of 5G and Edge use cases.

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