KEYNOTE: The Predictive Power of Massive Data about our Fine-Grained Behavior

April 4, 2017

Prerequisite: None

Foster Provost

Professor of Information Systems

NYU Leonard N. Stern School of Business

What really is it about “big data” that makes it different from traditional data? In this talk I illustrate one important aspect: massive ultra-fine-grained data on individuals' behaviors holds remarkable predictive power. I examine several applications to marketing-related tasks, showing how machine learning methods can extract the predictive power and how the value of the data "asset" seems different from the value of traditional data used for predictive modeling. I then dig deeper into explaining the predictions made from massive numbers of fine-grained behaviors. Fine-grained behavior data incorporate various sorts of information that we traditionally have sought to capture by other means. For example, for marketing modeling the behavior data effectively incorporate demographics, psychographics, category interest, and purchase intent.

Finally, I discuss the flip side of the coin: the remarkable predictive power based on fine-grained information on individuals raises new privacy concerns. In particular, I discuss privacy concerns based on inferences drawn about us (in contrast to privacy concerns stemming from violations to data confidentiality). I show however that it is possible to provide online consumers with transparency into the reasons why inferences are drawn about them and control over the inferences being drawn about them.

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TDWI Accelerate Boston

The Boston Marriott® Copley Place
Boston, MA
April 3–5


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