The authors offer some insightful thoughts on how machine learning can help extract value from big data. The authors begin their discussion by “illustrating the limitations of current methods and human intellect across the 4 Vs (volume, velocity, variety, and veracity)” and the barriers that can block the extraction of the 5th V (value). Their article further highlights some excellent ML use cases at cutting-edge companies.
Article
Why Machine Learning Is Crucial to Effective Utilization of Big Data
Posted December 11, 2017 | Leadership | Technology | Amplify
About The Author
Greg Smith is a Partner at Arthur D. Little (ADL), based in London. He founded and co-leads ADL Catalyst and was previously a member of ADL's Executive Committee, where he had responsibility for ADL's global innovation strategy. His work focuses on pragmatic business strategy in the context of digital transformation, as well as the application of disruptive information technologies in solving intractable business problems in major enterprises.… Read More
Michael Papadopoulos is a Cutter Expert, Partner in ADL Catalyst, and a member of ADL's AMP open consulting network. He is passionate about designing the right solutions using smart-stitching approaches, even when elegance and architectural purity are overshadowed by practicality. Mr. Papadopoulos leads the scaling of multidisciplinary organizations by focusing on continuous improvement, establishing quality standards, and following solid… Read More
Andreas Macek is a Digital Strategy Consultant at Arthur D. Little. He has extensive experience in digital strategy and IT transformation following an Agile approach through management consulting roles in FTSE 250 companies. He can be reached at Macek.Andreas@adlittle.com.
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