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The use of Big Data-based processes is a trend that is gaining popularity across industries and is already playing an important role worldwide, especially in process optimisation. Because with the advancing digitalisation of everyday office processes, the density of available data that can be collected and analysed is also growing. These analysis processes not only make it possible to recognise patterns and name them, but also to explain them in further steps, to predict future (probable) developments and thus to make an important contribution to the strategic planning of a company.
But for whom is it worthwhile at all? Which processes can be improved with the help of big data? And how is this to be evaluated? These are the questions I would like to address today.
Where is the HR-related use of Big Data worthwhile?
First of all, it is important to bear in mind that Big Data processes are used wherever data sets are too large to be processed and analysed manually by humans. Consequently, the application of such processes only makes sense if appropriate tools for data collection are upstream. So it is most likely less worthwhile for the small bakery shop around the corner, but more worthwhile for medium-sized companies (50 to 249 employees), while large companies or even corporations should by no means sleep through this development. Because well-functioning big data tools enable managers to make better-informed decisions in less time.
The use and opportunities of Big Data analytics in HR
A prime example of the use of Big Data analysis in HR is recruiting. This should be largely digitised anyway and can thus be comprehensively analysed in the best case. Each phase of the candidate journey can be analysed for "breaking points" - i.e. points at which a disproportionate number of applicants drop out. This method is in no way intended to replace the interviewing of newly acquired team members regarding the application and onboarding process. Rather, it can be supplemented and additionally sharpened.
The part of the selection process that takes place on the company side can also be further automated - perhaps you remember my article on the topic of "AI in recruiting"? Procedures that were carried out exclusively by humans in the past can be analysed and logic correlations and metrics can be derived that could contribute to the first automated assessment of applicants in the future. What is important here is that regular checks take place and the system is expanded to include correlations that are not based on a logical hypothesis.
Another field of application is change management. Regardless of the degree of fast-moving nature of the industry in which your company is located: paradigm shifts take place from time to time, especially with regard to digital work processes, which should be implemented as quickly as possible and across the board by the workforce in order to have their full effect. With the help of Big Data tools, it is very easy to determine which specifications are implemented better and which less well. This can be enormously helpful, for example, when planning further training measures, as measurable weak points can be targeted.
Of course, there are numerous other examples of use, but I would like to limit myself to one last one with human resource management and the associated human resource strategy. This is where so-called people analytics comes into play, i.e. the analysis of employee-related data. It makes it possible, for example, to draw clear conclusions about which employees work most productively or least productively with whom, at what time and on which topics. Such information is of course enormously helpful when it comes to assigning teams to projects and adapting working models. The theoretical potential of such analyses is almost infinite, as a data set can be analysed for practically any variable - even in dependence on another.
In sum, this information then unfolds its full potential. Because if you can clearly identify where strengths and weaknesses lie in the workforce in terms of areas of expertise, where preferences lie in terms of working hours and models, as well as partners:inside in teamwork, this allows for a much more efficient use of available resources and better strategic planning.
But - and this is an issue we would like to address in a moment - as helpful as these insights may be, they should always be balanced between the protection of employees' privacy and the benefit for the company. When in doubt, a responsible manager should always put the interests of his or her own employees first, because they are the most important resource of the entire company.
So: Is all this legally and morally defensible?
Let's move on to the elephant in the room: the issue of data protection. When it comes to data analysis in HR, you can and should align your moral compass with German legislation. Here, Big Data is also confronted with Big Data Protection. In order not to lose sight of the legal situation, regular training sessions for the HR department are essential.
Employers are obliged to obtain permission from the employees concerned for any form of data collection. In the case of personal data, this permission is always linked to a fixed purpose. If another variable or context is to be examined two weeks later, a new voluntary consent is required - because in this way employees know at all times how and for what purpose their data is being processed. It is different if the data is completely anonymised after it has been collected, so that it is no longer possible to draw conclusions about which employees can be found where in the data record. In this case, only a one-time consent is required, after which the data may be processed and examined again and again.
Not only when it comes to data protection, but also when it comes to morality, a distinction should be made between methods in which data is completely anonymised before it is analysed, for example in order to examine and improve process structures, and methods in which personal data is used to closely examine individual employees and analyse their behaviour and working methods.
For a long time, not everything that is technically possible and seems to make sense from the company's point of view is also morally justifiable. An extreme example would be the evaluation of toilet usage times. If a team member spends an above-average amount of time in the restroom, he or she works less - and should therefore earn less than employees who spend significantly less time in the restroom, right? This may seem fair at first glance, but this form of data analysis crosses numerous moral boundaries. After all, trust and respect towards one's own employees are the basis for a successful cooperation from which both sides benefit.
Following these guidelines, big data tools can still be very useful without invading the privacy of workers too deeply. Almost every process can be analysed for bottlenecks and limiting factors, which can then be optimised in a targeted manner. Furthermore, developments such as future staffing needs in the event of expansion can be predicted, which provides additional support for strategic planning.
