In today’s data-driven business world information is omnipresent and companies are required to make smarter and faster decisions derived from data. At the same time, having the right talent, at the right time, in the right place is a challenge that companies continue to struggle with. Combining the data-driven decision-making is only starting to spread to the HR functions of global companies. Technological advancements are shaking HR functions and reshaping the way companies approach people management.

This master’s thesis focuses on the intersection of talent identification and big data analytics tools where HR analytics and evidence-based talent identification are found. The purpose is to examine how big data tools can be used to facilitate talent identification in MNCs. Data and tools applicable for identifying talent are examined as well as the concept of big data and the tools to process it, such as artificial intelligence and its applications. Data mining techniques show great potential for more efficient and accurate personnel selection.

The empirical evidence in this qualitative, exploratory study was collected from eight MNCs and four consulting firms, through interviews with fourteen participants from Europe and North America. The findings of the study are discussed in connection to existing literature.

The findings indicate that HR analytics has not yet reached its full potential in the case organisations, and that further implementation is still underway. The HR function struggles to cope with the big data challenge. Predictive analytics are seen as a future trend that hold an immense amount of potential for increasing organisational performance from the people management perspective.