People analytics is based on data science and is another hype in the world of human resources. However, despite the fact that those who manage human resources talk about people analytics with a lot of enthusiasm, in reality, very few companies have the opportunity to truly address this topic and profit from it. Namely, managers and management structures within companies are most often aware that the company's profit, productivity, and even success are closely related to employees, that is, their involvement in the work they do. This is evidenced by the growing number of studies, so managers consequently recognize the potential use of data science and take active steps towards incorporating these very diverse solutions into their daily business. Still, there are a number of challenges:
1. The primary problem lies primarily in the data itself. On the one hand, they do not say much about themselves, i.e. they can suggest different conclusions, so it is important how the data will be "packaged", interpreted and what hypotheses will emerge from them.
2. This leads to the next challenge. Namely, while companies are willing to spend a fortune on customer data, employee data is often put at the bottom of the priority list. For this reason, often the basic infrastructure is not well developed for the initial data collection, employee data are divided between different systems that are not well connected and synchronized, so they do not "communicate" with each other. Accordingly, in order to get some data, that is, to start any matching and analysis, it is often necessary to invest a lot of time in preparing data for processing, that is, collecting, cleaning and sorting them.
3. The third challenge concerns the deficit (also in the world market) of profiles that would properly overcome the first two above challenges, i.e. that would carry out and interpret analytics, as these staff must have equally good technical knowledge in programming and statistics, but also in HR.
Finally, those who manage to overcome the first three points should not relax, as there will be no shortage of challenges further on the path. Namely, data interpretation is the beginning of the people analytics journey. What follows, and is certainly the biggest challenge, concerns responding to recommendations obtained through data analytics. Many companies are happy to run an analysis, but turn a deaf ear to carrying out a recommendation as they may not like what they get through data, either because another priority has emerged in the meantime, or because... excuses are plentiful. Without carrying out a recommendation, without the reaction to the recommendation obtained through people analytics, it is impossible to see the result and get valid feedback, that is, to confirm whether the analysis is correct or incorrect.
Finally, every analysis, even the one in the domain of people analytics, needs to be related to business goals to actually make sense. It is best to start with the question WHY do we want to analyze something, WHAT is the problem, and only then decide on the choice of data and the type of analysis. Also, the selected analytics should be incorporated into day-to-day operations, rather than, like most companies do, launching people analytics projects once a year to set and/or evaluate annual goals. An illustrative example is the employee satisfaction questionnaire, which is administered once a year, or less often. This may once have made sense, but in today’s fast-paced world where everything is changing rapidly, the period of action between identifying a problem and developing a plan and impact is very short. That is why it is much better, more efficient and more agile to use, for example, the Net Promoter Score (NPS) technique to get such data - which in most cases costs nothing and can be done continuously.
An additional recommendation is to start working on minor problems. For example, through the NPS one can determine the so-called pain-points of employees. Choose one and think about why it is a pain-point and what you think can eliminate or reduce it. Create a guerrilla implementation plan.
Essentially, experimenting on smaller problems, and with less demanding analytical techniques and data, which allow for rapid experimental action and getting feedback on the effects of the plan, is key to success. But it needs to be used for the right keyhole, and that is people, employees. People analytics brings long-term results, as it was said at the beginning, if used to involve employees; truly engaged employees will give their best at work. Those who have managed to engage this potential are the companies with the largest increase in profits, production, customer satisfaction - a true and sustainable "win-win" scenario.
