A growing number of top companies are using professional personality assessment and big data as a tool for personnel decisions. Based on 30 years of research in the field of HR assessment, with the usage of digital tools it has evolved into a predictable and accurate solution.
The last 30 years of assessment has had a very long way to go: in the 80’s accuracy and reliability was established, but methods were inefficient and behaviour was not measured. In the 90’s usability was achieved on all levels and basic ERP systems were used. But all of these were focusing on performance enhancement and employee satisfaction was not a factor to be considered. At the beginning of the 2000’s new platforms were born every month and big data was starting to gain attention. But most of the solutions have been inadequate, and that was not supporting trust towards implementation of technologies in HR. In the last decade, data-analytics has gained ground with innovative platforms.
Today at hiring, a well-structured personality test evidently beats any interview three times: a study by Hogan Assessments shows a 0.2 correlation coefficient between an unstructured interview and the actual capability of a potential employee – this grows 0.6 with a personality test.
Personality assessment, big data and talent analytics are decidedly the future of HR and every business, as incredibly powerful predictive and descriptive tools for personnel decisions. According to Dr. Robert Hogan, founder of Hogan Assessments “The most determinative trend in HR is talent analytics. On the one hand, it makes it possible to make personnel decisions based on facts and not impressions. On the other hand, most HR professionals don’t make a difference between data derived from good and bad assessments – the problem is, that from wrong data, only wrong decisions can be made.”
Usage of data is now crucial for people management, because well-established tests are backed with thousands of experiences. Biased personal impressions are now insufficient and cost businesses too much.