MIT Sloan Management Review Article on Managing Data Privacy Risk in Advanced Analytics

Cybersecurity techniques that keep personal data safe can limit its use for analytics — but data scientists, data owners, and IT can partner more closely to find middle ground.

“How can we protect the privacy of our customers’ personal data while leveraging that data via AI and analytics?” This question reflects a growing internal dilemma as companies pursue advanced analytics and artificial intelligence.

The troves of data that customers’ ever-more-digitalized lives produce can be a rich source of insight for organizations using advanced analytics tools. At the same time, this data is a deep source of concern to IT staffs committed to meeting both regulatory agencies’ and consumers’ expectations around data privacy. Both are important objectives — but meeting them simultaneously requires confronting an inherent conflict. Increasing data privacy in the context of analytics and AI involves using techniques that can reduce the utility of the data, depending on the task and the privacy preservation technique chosen.

About the Author

Gregory Vial is an associate professor in the Department of Information Technologies at HEC Montréal. Julien Crowe is senior director of artificial intelligence at the National Bank of Canada. Patrick Mesana is a doctoral candidate in the Department of Decision Sciences at HEC Montréal.

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