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Introduction 17 (see Chapters 2, 3, and 6; Angrave et al., 2016; McAbee et al., 2017; Shmueli, 2010; Yarkoni & Westfall, 2017). 1.2.4.2 Processing power Second, it has become easier to analyze data and uncover (behavioral) patterns. Due to advances in computing power and developments in open-source programming languages (e.g., R, Python, Pig, Julia, Ruby) and libraries (e.g., caret, scikit-learn, Tensorflow, Theano), anyone with some statistical training can run complex analyses and large-scale simulations on their personal laptop. These days, “given adequate data and access to a personal computer, a six-year-old could use a basic statistics program to generate regression results” , Charles Wheelan jokingly states in his book Naked Statistics (2013, p. 187). On a larger scale, distributed databases and computing systems (e.g., Hadoop, Spark) allow organizations to scale their capabilities in order to handle, process, and analyze staggering amounts of data. Simultaneously, we see an improved dissemination of new methodology and a rise in interdisciplinary collaborations (see Chapter 2; James, Witten, Hastie & Tibshirani, 2013; Strohmeier & Piazza, 2013). As a result, models and techniques that are common in fields other than HRM (e.g., physical, life, computer, and medical sciences) are nowadays increasingly applied to solve personnel problems (see Chapters 3 and 6; Strohmeier & Piazza, 2013). These developments allow the HRM function to better leverage the value of its data. 1.2.4.3 Push towards evidence-based HRM Third, the HRM function is experiencing a strong push to become more data-driven and evidence-based. The popular and the scientific press have shared success stories of progressive HRM departments (e.g., Bock, 2015; Rasmussen & Ulrich, 2015; Siegel, 2016) and of other functional disciplines (e.g., McAbee et al., 2017; McAfee, Brynjolfsson, Davenport, Patil, & Barton, 2012; Lewis, 2004), highlighting the enormous value that data analytics may bring. At the same time, the scientific community established that HRM affects operational and financial outcomes (e.g., Guest et al., 2003; Jiang et al., 2012), but that organizations may want to test the effectiveness of HRM policies and practices in their own, local context (e.g., Boselie et al., 2005; Johns, 2006; Lepak & Snell, 2002; Paauwe & Farndale, 2017). It seems that becoming more evidence-based and data-driven through analytics would provide the HRM function with huge benefits (Barends & Rousseau, 2011, p. 233). Data and analytics can allow organizations and their HRM departments to manage their personnel more effectively and/or efficiently, thus providing a competitive advantage. In practice, organizational stakeholders increasingly demand evidence of the impact of HRM decisions (Minbaeva, 2017a; Van der Togt & Rasmussen, 2017) and this causes HRM professionals to turn to people analytics to complement their intuition, experience, and beliefs with facts and evidence (Minbaeva, 2017a, p. 111). 1.3 Dissertation outline This dissertation aims to answer two main research questions: 1. What is the current state of people analytics?