People Analytics: Using Digital Exhaust from the Web to Leverage Network Insights in the Algorithmically Infused Workplace Dept. of Computer Science (April 25, 2023)
Organizations need to do more than analyse data on demographic attributes to bring the performance of people analytics in the algorithmically infused workplace up — and in line with the hype. We need to focus not only on who people are but also on who they know. The potential for social network analysis to identify “high potentials,” who has good ideas, who is influential, and what teams will get work done efficiently and effectively is well established based on decades of research. The challenge has been collecting network data via time-consuming surveys, which elicit low response rates, and have high obsolescence. This talk presents empirical examples ranging from corporate enterprises to simulated long-duration space exploration to demonstrate how we can leverage people analytics – and in particular relational analytics - to mine “digital exhaust”— data created by individuals every day in their digital transactions, such as e-mails, chats, “likes,” “follows,” @mentions, and file collaboration— to address challenges they face with issues such as team assembly and team conflict. [Go to the full record in the library's catalogue]
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