IDC PeerScape: Practices to Implement Machine Learning Initiatives
This IDC peer study provides nine use cases while detailing the recommended practices in implementing machine learning. The use cases are collected from analyst briefings and vendor interviews (CCM Benchmark, Latvia Railway, Hangzhou Hikvision Digital Technology, Devon Energy, and Woodside Energy) and third-party publications (BakerHostetler LLP, MasterCard, Logitech, and The University of Tokyo), covering industries such as public transportation, marketing and media, legal service, financial service, healthcare service, oil and gas, and electronics. Despite the scattered scope of the machine learning use cases, IDC believes successful adoptions of machine learning stand on common ground. This study focuses on organizations established before the big data era. A lot of companies in this category are convinced of the business value propositions of machine learning but need practical guidance to optimize investments on such initiatives. "Cognitive/artificial intelligence or machine learning functionalities will drastically release manpower from operations where information searching and gleaning are heavily involved," says Jessie Danqing Cai, senior research manager with IDC Asia Pacific. "They will cause significant changes to the way the business world explores knowledge base and executes operational tasks. Active business adoptions shall, in return, accelerate the maturity curve of the enabling technologies in different industry contexts. The value creation potential of machine learning is immense," she adds.
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