Who are the brightest 10% of people at your Company? Which of your current staff are best equipped with both good judgement and good leadership. One of my diligent HR officer’s want to move into IT, does she have a good fit with that role? Which of my current financial officers have the greatest risk of being dishonest? I want to help staff become more assertive, but what percentage of staff would most benefit from such a course?

“Who are the brightest 10% of people at your Company? Which of your current staff are best equipped with both good judgement and good leadership.”

The above are a bare slither of the questions which exist at the interface between psychometrics and data science. Psychometrics collects fascinating data on individual human functioning. An average personality test mines for 20 or 30 dimensions, and coupled with tests for cognition, leadership, integrity and skills, it is not uncommon to source around 100 individual metrics on each psychometric participant. Multiply this by the number of people in any organisation and the richness of this data begins to emerge. Of course, psychometrics are but one lens of human behaviour and all the nuances of individual functioning that make us unique are not all measurable. Yet when psychometric data is coupled with performance management and other historical data from the organisation, a multi-dimensional set of talent management trends becomes available. Human behaviour becomes a tangible and valuable metric that can be used to understand organisational dynamics and help link the potential of human energy to the realisation of organisational strategy.

Personality measures, all flawed in capturing the full complexity of individual functioning, are useful enough in predicting who is resilient, a team-player, emotionally fragile; confident; assertive and many other dimensions. The power of such testing was demonstrated in the 2016 USA elections which saw Donald Trump become the president of the USA. At the time many had argued that such an outcome was highly improbable. This has largely been attributed to Cambridge Analytica’s use of psychometrics instead of traditional demographical data. Cambridge Analytica utilised Facebook personality questionnaires coupled with machine-learning to target specific content to individuals who would be most receptive to it. In some ways the most likely Trump supporters were “played”, feeding them information that built their allegiance to Trump. Amazingly, all on personality data. Working with the Trump campaign team, Cambridge Analytica’s approach became focused on how to get the disenfranchised voter to want to vote, and the data collected became honed into very specific, targeted mini-campaigns.

If psychometrics can be used to swing an election, shouldn’t companies be using it to channel human energy more purposefully? In short to convergence of psychology and information technology is likely to be one of the biggest and most powerful shifts in how we conceptualise and engage with these two fields from discrete fields of study. We are now either seeing a complimentary overlap or merger between the two, in the form of psycho-informatics. Machine Learning techniques are revolutionising the use of psychometrics in a pioneering and decisive manner, changing the way in which we engage with these fields of inquiry for the better. Every company can for reasonable costs tap in the power of psychometrics and data analytics. This kind of information has been available for quite some time, but it has never been easier to use that it is now.

Have more questions on psychometric assessments contact Hilton: Hilton@omnicor.co.za

Co-Authors: Hayley Dady and Dr Hilton Rudnick