Today has been the last day of the 7th Workshop on Talent Management that has been held in Helsinki. It’s has been an excellent experience where researchers and practitioners have shared results and discussions from their projects, researches, and professional experiences. We have been able to listen to experts on the topic such as Wayne Cascio, Vlad Vaiman, Hugh Scullion, David Collings, and Anthony McDonnell. Next year, the workshop will be held in the Toulouse Business School (France). So, we will be able to go by car.

In the workshop, Eva Gallardo and I have presented our paper: Unscrambling Talent Analytics. The paper has been very well welcomed and has attracted the attention of a significant number of attendees. So we continue with renewed enthusiasm in the research of talent analytics.

I attach the abstract of our paper:

Nowadays the use of huge amount of data suggests new ways to solve problems and create value in organizations, which make Business Analytics a valuable and interesting tool. The HR management domain is not immune to this trend and Talent Analytics has emerged as a hot topic for practitioners and academics. However, few is known about what is in this domain. Thus, the purpose of this paper is to provide an overview of the existing literature on Talent Analytics (TA). In short, we want to answer to these questions: What is meant by TA? How much do we know about that? In order to answer these questions, we have performed a scoping review based on the several types of publications collected in Scopus (Elsevier). The results show that the field of TA is underdeveloped, both in the academic and in the practitioner’s literature. There aren’t significant differences among them. Publications can be divided into two main groups: (1) development of frameworks for the design, implementation and use of TA, listing its main pitfalls; and (2) case descriptions of the design, implementation and use of TA, which are characterized primarily by systems that focus solely on descriptive rather than predictive or prescriptive analysis. Finally, we suggest five future research lines about TA.