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Chapter 1 16 1.2.4 Public Interest in People Analytics Regardless of the precise definition and demarcation, the rise in public interest in people analytics is quite remarkable. Figure 1.1 demonstrates the monthly Google search interest for several labels between 2004 and 2018. A locally weighted regression line (Cleveland & Devlin, 1988) was fitted to these monthly data to visualize the tremendous increase in interest in the domain since 2007. This interest in people analytics is due to at least three concurrent developments: (1) the rise of digital technology, (2) an increase in processing power, and (3) a push towards evidence-based HRM. Figure 1.1: Monthly Google search interest on “people analytics” and related terms over time. Values are proportional to the maximum value and fit by locally weighted regression lines. 1.2.4.1 Digital Technology First, digital technology – including personal computers, the Internet, and mobile devices – has changed and continues to change how we manage and organize work and the information we collect in the process. With the rise of digital HR information systems (HRIS), we have witnessed great increases in both the volume and the complexity of the data we gather on our personnel. Organizations used to keep physical records containing basic employee information locally whereas, nowadays, terabytes of workforce data can be gathered, processed, and monitored on a continuous basis in the cloud (Angrave, Charlwood, Kirkpatrick, Lawrence, & Stuart, 2016; Ball, 2010; Bersin, 2015; Deloitte, 2017; Günther, Mehrizi, Huysman, & Feldberg, 2017; Hendrickson, 2003; McAbee, Landis, & Burke, 2017). Concrete examples involve the gamification of work – where game features are added to a work context in order to provide real-time information on, for instance, employees’ performance (Cardador, Northcraft, & Whicker, 2017) – or the collection and analysis of video data for HRM processes such as employee selection or safety management (Guo, Ding, Luo, & Jiang, 2016; Roth, Bobko, Van Iddekinge, & Thatcher, 2016). While the complex, novel data gathered via digital technology has the potential to improve our HRM decision-making, processing and analyzing such data often requires a different approach than the one we are used to in HRM research and practice