FEATURE : MACHINE LEARNING
Automotive vehicles are also able to collect complex data from their surroundings and interpret it to make precise and accurate decisions on their own , using Machine Learning . IDC forecasts that the number of vehicles capable of level one autonomy ( driver assistance ) will increase from 31.4 million units in 2019 to 54.2 million units in 2024 .
Fady Richmany , Senior Director and General Manager – UAE , Dell Technologies , said with the developments being made in the field of Machine Learning today , the practical uses in enterprises are endless . Richmany said Machine Learning systems can be used to help anticipate trends and identify problems , thereby playing an important role in supporting decision-making processes . “ Enterprises can also use Machine Learning for customer retention , since Machine Learning systems can study customer behaviour and identify potential steps for customer retention ,” he said . “ Additionally , they can make use of Machine Learning to help with market research and customer segmentation . This allows them to deliver the right products and services at the right time , while also gaining valuable insights into the purchasing patterns of specific groups of customers to better target their needs .”
He said furthermore , enterprises can also increase their operational efficiency by deploying Machine Learning to handle day-to-day routine business tasks , thereby speeding up operations , freeing up their employees for more innovation and creating new business opportunities .
Machine Learning secret sauce
With vendors often claiming to have some Machine Learning secret sauce in their wares that will revolutionise an enterprise ’ s business , CIOs are being urged to be careful when selecting the right Machine Learning systems and tools .
Alan Jacobson , Chief Data and Analytics Officer , Alteryx , said the three top considerations to selecting any Machine Learning system should always include usability , breadth of scope and an outcomes-based view .
Jacobson said much of the emphasis for Machine Learning has been on the technology , not the people and that ’ s where failed projects are rooted . “ As technologies continue to converge , so do the consumers or producers of those capabilities . The current skills , gap continues to be any issue for enterprises . There is a distinct lack of data scientists across the globe ,” he noted .
Second , according to Jacobson , enterprises have to ensure that the chosen solution can help clean and manipulate data from whichever sources are necessary and operate across the breadth of the tech stack in place . “ The end goal should be replacing any disconnected tools with hyper-specific functions in favour of one broad-use tool ,” he said . “ Finally , as with any technology purchase , an outcomes-based approach is also essential in keeping things on track . Is your organisation able to benchmark these outcomes in advance from existing use cases , for example ? A focus on the long-term business impact and a direct impact on productivity are two key metrics to assess here .”
Rick Rider , VP , Applied Innovation , Infor , said endless compute resources is the reason Machine Learning is seeing wider industry acceptance now . In addition ,
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