Big data in conjunction with artificial intelligence and machine learning are revolutionizing the potential and risk of predictive business analytics. Firms have extensive records on customer and employee behavior. While on the one hand this allows firms to deliver an increasingly sophisticated product offering that is customized to individual needs and preferences, it also has the potential to extract private information that a customer may prefer not to share. Similarly, firms now have the potential to use algorithms to replace workers in certain domains. It is thus more important than ever to engage with the opportunities presented by these new technologies while also managing and regulating the risks associated with them. We will explore three examples. First, we will see how fine grained data on household energy consumption can help us design more successful energy efficiency programs. Then, we will investigate whether algorithms can replace bank managers. And, lastly, we will see how consumer credit data can predict mortality.

The presentation will be followed by a discussion moderated by Martin Wagner who will share some of his experiences with big data in a central bank context. We want to discuss the implications of big data and business analytics for education, research, firms and organizations.

Thursday, 26th of January | 4pm | Hörsaal HS 1 (Zentraltrakt)

Matthew C. Harding

Matthew is an econometrician and data scientist who develops techniques at the intersection of machine learning and econometrics to answer big data questions related to individual consumption and investment decisions in areas such as health, energy, and consumer finance. He often focuses on the analysis of “deep data“, large and information-rich data sets derived from many seemingly unrelated sources but linked across individuals to provide novel behavioral insights. He is particularly interested in the role of technology and automation to induce behavior change and help individuals live happier and more sustainable lives. At the same time his research emphasizes solutions for achieving triple-win strategies. These are solutions that not only benefit individual consumers, but are profitable for firms, and have a large positive impact on society at large.

Matthew has a BA from University College London (2000), an MPhil from the University of Oxford (2002) and a PhD from MIT (2007). Prior to joining UC Irvine, where he leads the Deep Data Lab, he has been Associate Professor at Duke University (2014-2016) and Assistant Professor at Stanford University (2007-2014).