Voor Privacyweb schreef ik onlangs over people analytics en het mogelijk resulterende nudgen van medewerkers: kleine aanpassingen of duwtjes die mensen in de goede richting zouden moeten sturen. Medewerkers verleiden tot goed gedrag, als het ware. Maar wie bepaalt dan wat goed is, en wanneer zouden werkgevers wel of niet mogen of zelfs moeten nudgen?
With great pleasure I’ve studied and worked in the field of people analytics, where we seek to leverage employee, management-, and business information to better organize and manage our personnel. Here, data has proven valuable itself indispensible for the organization of the future.
Data and analytics have not traditionally been high on the list of HR professionals. Fortunately, there is an increased awareness that the 21st century (HR) manager has to be data-savvy. But where to start learning? The plentiful available resources can be daunting…
Have a look at these 100+ amazing books for (starting) people analytics specialists. My personal recommendations are included as pictures, but feel free to ask for more detailed suggestions!
Behavioural Psychology: focus on behavioural psychology and economics, including decision-making and the biases therein.
Technology: focus on the implications of new technology….
Ethics: … on society and humanity, and what can go wrong.
Digital & Data-driven HR: … for the future of work, workforce, and organization. Includes people analytics case studies.
Management: focus on industrial and organizational psychology, HR, leadership, and business strategy.
Statistics: focus on the technical books explaining statistical concepts and applied data analysis.
People analytics: …. more technical books on how to conduct people analytics studies step-by-step in (statistical) software.
Programming: … technical books specifically aimed at (statistical) programming and data analysis.
Communication: focus on information exchange, presentation, and data visualization.
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Past week, Analytics in HR published a guest blog about one of my People Analytics projects which you can read here. In the blog, I explain why and how I examined the turnover of management trainees in light of the international work assignments they go on.
For the analyses, I used a statistical model called a survival analysis – also referred to as event history analysis, reliability analysis, duration analysis, time-to-event analysis, or proporational hazard models. It estimates the likelihood of an event occuring at time t, potentially as a function of certain data.
The sec version of surival analysis is a relatively easy model, requiring very little data. You can come a long way if you only have the time of observation (in this case tenure), and whether or not an event (turnover in this case) occured. For my own project, I had two organizations, so I added a source column as well (see below).
The 2018 annual Society for Industrial and Organizational Psychology (SIOP) conference featured its first-ever machine learning competition. Teams competed for several months in predicting the enployee turnover (or churn) in a large US company. A more complete introduction as presented at the conference can be found here. All submissions had to be open source and the winning submissions have been posted in this GitHub repository. The winning teams consist of analysts working at WalMart, DDI, and HumRRO. They mostly built ensemble models, in Python and/or R, combining algorithms such as (light) gradient boosted trees, neural networks, and random forest analysis.