FEATURE : IOT AND ML
Increasingly IoT , edge computing , automation and business use cases are becoming increasingly converged explains executives from Dell Technologies , Palo Alto Networks , PROVEN Consult , Vertiv ,
Zebra Technologies , NetApp , and Heriot-Watt University Dubai . and the cloud . They are faced with increasing volumes of orders for their products and services at greater volume and speed , so need their workers to have modern , easy to use technology to help them do their best work .
AI is ideal for analysing data , spotting trends , and offering prescriptive and predictive recommendations to help with decision making and efficiency . Enterprises have lots of data and AI is turning data into an asset . Enterprises are moving from systems of record to systems of reality with enterprise asset intelligence .
As increased devices become connected to the Internet , there is a growing demand for AI and ML capabilities to be available at the edge of the network and therefore we see Digital enterprises are increasingly turning to edge computing to address the limitations of traditional cloud computing . The primary benefits that edge computing provides to these organisations include faster processing and analysis of data at the edge of the network , closer to where data is generated , leading to reduced latency , improved application performance , and reduced bandwidth usage .
This can result in faster decision-making , increased operational efficiency and improved customer experiences . Additionally , edge computing can enable better data security and privacy , as data can be processed and analysed locally rather than being transmitted to a central cloud . Edge computing is therefore becoming essential to digital enterprises as they seek to stay competitive in today ’ s fast-paced , data-driven business environment .
Digital enterprises share some themes in common , as well as having unique challenges and digital maturity levels . They constantly want to improve the service they provide to their customers . They want to digitise their workflows and gain better accuracy and visibility of their assets and inventory using software , RFID ,
The primary benefits that digital enterprises normally look for from edge computing are improved performance , lower latency and reduced bandwidth . Edge computing improves speed and efficiency of data processing since the data is processed locally on the edge devices instead of sending the data to a cloud server . This comes in handy for applications that require real-time data analysis or involve large amounts of data . In addition , edge computing helps reduce the bandwidth cost related to transmitting large amounts of data over the Internet .
“ Edge computing is enabling enterprises to process data locally , closer to its source , without having to send it to a centralised location for processing . Running AI , ML models at the edge is becoming common practice , powered by the innovations happening in edge infrastructure ,” says Hani Khalaf , Field CTO , IoT and Digital Cities META , Dell Technologies .
Business adoption
Edge computing addresses a myriad of business needs and application requirements , opening a world of possibilities . High-speed trading , high-definition content delivery , and autonomous cars all depend on near-real-time computing capabilities . Without them , these applications would experience detrimental performance issues , ranging from financial losses to a poor user experience , or even loss of life .
“ Consumer-facing companies such as healthcare organisations and retailers may want to push offers or services to consumers ’ mobile devices and wearables
IoT enabling enterprise automation
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