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McKinsey’s 2016 Analytics Study Defines The Future Of Machine Learning

Unlocking the potential of #MachineLearning  via @McKinsey_MGI #BigData #Analytics

  • Filed under: enosix , enosix integration , enosiX Salesforce integration , enosix SAP integration , Louis Columbus’ blog , Machine learning , McKinsey 2016 Study Tagged: Analytics , enosix , enosix integration , enosiX Salesforce integration , enosix SAP integration , Machine learning , McKinsey Analytics , McKinsey Global Institute’ , McKinsey’s 2016 Analytics Study
  • McKinsey identified 120 potential use cases of machine learning in 12 industries and surveyed more than 600 industry experts on their potential impact.
  • The study underscores how critical integration is for gaining greater value from data and analytics.
  • Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing.
  • Location-based services and U.S. retail are showing the greatest progress capturing value from data and analytics .

These and many other insights are from the McKinsey Global Institute’s study The Age of Analytics: Competing In A Data-Driven World published in collaboration with McKinsey Analytics this month. You can get a copy of the Executive Summary here (28 pp., free, no opt-in, PDF) and the full report (136 pp., free, no opt-in, PDF) here. Five years ago the McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity (156 pp., free no opt-in, PDF), and in the years since McKinsey sees data science adoption and value accelerate, specifically in the areas of machine learning and deep learning. The study underscores how critical integration is for gaining greater value from data and analytics.

@telefonicab2b: Unlocking the potential of #MachineLearning via @McKinsey_MGI #BigData #Analytics

Enabling autonomous vehicles and personalizing advertising are two of the highest opportunity use cases for machine learning today. Additional use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics; predicting personalized health outcomes, and optimizing merchandising strategy in retail. McKinsey identified 120 potential use cases of machine learning in 12 industries and surveyed more than 600 industry experts on their potential impact. They found an extraordinary breadth of potential applications for machine learning.  Each of the use cases was identified as being one of the top three in an industry by at least one expert in that industry. McKinsey plotted the top 120 use cases below, with the y-axis shows the volume of available data (encompassing its breadth and frequency), while the x-axis shows the potential impact, based on surveys of more than 600 industry experts. The size of the bubble reflects the diversity of the available data sources.

McKinsey’s 2016 Analytics Study Defines The Future Of Machine Learning

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