Webinar

Machine Learning and Data Analytics in the Pandemic Era

Jeffrey D. Camm, Thomas H. Davenport, and Elizabeth Heichler Runtime 0:59:03

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The COVID-19 pandemic has serious implications for the future of data, analytics, and machine learning.

The pandemic has disrupted machine learning, analytics, and data strategies at large companies around the world. Now’s a good time to look at what that has meant for leaders who rely on these tools, and what those leaders are doing to redeploy and regroup.

In this webinar, Jeffrey D. Camm and Thomas H. Davenport, authors of “Data Science Quarantined,” discuss the recent conversations they’ve had with data science and analytics experts. They’ll offer examples of disruption outcomes and strategies on how to capitalize on the new realities going forward.

In this webinar, you’ll learn:

  • Why there has been a pivot to fast-cycle descriptive analytics.
  • What the disruption has meant for machine learning models.
  • Four steps for rebooting, including weighing data relevancy and ramping up model auditing.

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About the Authors

Jeffrey D. Camm is the Inmar Presidential Chair in Business Analytics, associate dean of business analytics, and executive director of the Center for Analytics Impact at the Wake Forest University School of Business (@wakeforestbiz). Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, as well as a fellow at the MIT Initiative on the Digital Economy, a visiting professor at Oxford’s Saïd Business School, and a senior adviser to Deloitte’s Analytics & Cognitive practice. Elizabeth Heichler (@eheichler) is the executive editor at MIT Sloan Management Review. She moderated the session.

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