Brenna leads the Center for Integrated Research, where she oversees cross-industry thought leadership for Deloitte. In this capacity, Brenna leads a team of researchers focused on global shifts in digital transformation, trust, climate, and the future of work; in other words, how organizations can operate and strategize in an age of digital, cultural, environmental, and workplace transformation. Her own research focuses on connected digital and physical technologies and their transformational impact. She works with other thought leaders to deliver insights into the strategic, organizational, leadership, and human implications of these technological changes. Prior to joining Deloitte, Brenna was a senior director at Forbes Insights, the thought leadership division within Forbes Media, where she oversaw and conducted primary cross-industry research on topics such as innovation, technology, transformation, Big Data and privacy/security, philanthropy and talent management.

Identifying whole/part relationships between books in digital libraries can be a valuable tool for better understanding and cataloging the works found in bibliographic collections, irrespective of the form in which they were printed. However, this relationship is difficult to learn computationally because of limited ground truth availability. In this paper, we present an approach for data augmentation of whole/part training data through the use of artificially generated books. Artificial data is found to be a robust approach to training deep neural network classifiers on books with limited real ground truth, working to prevent over-fitting and improving classification by 91.0%. Modern cataloging standards support encoding complex work-level relationships, opening the possibility for bibliographic collections that better represent the complex ways that works are changed, iterated, and collated in library books.