Using bibliometric analysis and text mining to improve the Thai talent database

In today's competitive world, “Talentism has become the new capitalism.” knowledge workers are viewed as valuable assets to their organizations and nations. In developing countries such as Thailand in particular, the number of experts in science and technology is quite limited. The mobility of talent between academia, government and industry is therefore essential for knowledge transfer and technology diffusion. In recognition of this fact, the Royal Thai Government developed a National Talent Data Base (“Talent Database”) in 2014, in order to facilitate mobilization of necessary talent and skills. However, the current Talent Database only supports keyword searches for potential candidates. Keyword searches alone provide no indication of the associations between researchers that may better allow talent managers to better pinpoint necessary and related skill sets, and to locate all potential candidates for positions. In this paper, we suggest improvements that may make the Talent Database more useful to managers seeking to plan talent mobility for R&D activities in Thailand. As an illustration, we apply “bibliometric analysis” and a “text mining” approach to identify topical emphases and experts' knowledge networks in the field of data science. The results could be used to assist decision makers to better match the demand with the right talents.