In a recently published study conducted by Talent Data Labs, 144 companies of different sizes were examined to see how they handle their HR data collection – and interpretation. As clear as the outcome is, the future of human capital management looks far from bright. Luckily the tools to change the current trend are readily available: we just need to know how to properly utilize them.
The world of talent analytics is on the brink of radical change, especially in a time where a global pandemic plays an undeniable role in re-envisioning the way we work. It’s a development that calls for drastic changes on how we view technology and data, but the question remains whether the world of HR is ready for a transformation on such a large scale. A team of researchers and data analysts decided to find proof of the pudding by investigating to what extent today’s widely used HR tech and software solutions actually provide strong and meaningful talent analytics that’s truly beneficial to the workforce. The study examined if the participating companies have the right software to move forward, whether they use it in a way that allows them to make impactful predictions that can bring about measurable changes, and whether these changes can deliver demonstrable benefits to the workforce.
Method
It was crucial to the study to involve a diverse field of participants, so the companies were selected by size, industry and country of origin to ensure that the research would be secure yet widely applicable. The researchers held interviews of three different parts with representatives of the firm in question. The first goal was to get a better understanding of how these companies applied different technologies, whereas the second was aimed at getting a better understanding of employer’s responsibilities, the company’s reporting standards and the flow of information. The last part consisted of evaluating the effectiveness of the different processes and the results they produced on their talent analytics efforts. After all the interviews were held, the researchers used a cause-and-effect model to transform input such as attributes, traits and skills into outcomes ranging from happiness within the workforce to the success of a company’s cost-per-click marketing campaign. In the process of gathering data it became clear that independent variables such as demographics and personality traits are not hard to come by: to the companies involved it seems a lot more challenging to actually process them into reliable outcomes.
Results
One of the most remarkable findings of the study is the mismatch between what companies communicate and reality. It’s commonplace to talk about data and insights as a staple of the HR market but hard evidence is difficult to come by, if existing at all. The research shows that it’s not a software problem but a people and probably empowerment issue. On the one hand the software and tools are very mature and prevalent but on the other hand the types of data and information we use are limiting. We overemphasize behavioral data (post hoc analysis) and we underestimate the value of independent data and variables. The problem with that is that once you’re monitoring behavior you’re already too late to change it, which is why most predictions currently lack meaning. Findings like these underline a structural need for gaining a better understanding of what data can do for human capital management. Simply put, executives need to educate themselves to better their understanding of how to obtain data and what to do with it. Unfortunately, this appears too big a step for many companies, while the potential benefits are grossly underestimated. In a way it’s baffling to see that companies always look into resource optimization but seem unable to understand that humans are their most important resource, or at least they don’t feel the need to quantify that resource in real and predictive data. The amount of effort these companies put into analyzing their products, should also be put into their employers to actually make a much needed difference.
