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When talent is measured with data

When talent is measured with data

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Monday, 25 june 2018 | Redacción CEU

The fever of the data has reached human resource departments, but not without first passing through industries like marketing, finance or sales. Combining Big Data and Science Data techniques in this field is becoming a rising trend that, according to its promoters, will favor the management of talent and increase the profits of companies. What does the People Analytics phenomenon consist of? Can the talent of people be measured with data? Can an analytical approach be more human?

 

Companies have been using a big amount of data for years in order to better understand the behaviors and tastes of consumers and users. This interest in Big Data is typical of marketing departments, but it is beginning to move to other fields like the human resources area. People Analytics is a new approach that strongly came into technology companies such as Google and Facebook. Its objective was clear, boosting the talent within its own borders thanks to the intelligent analysis of data. Now, this model begins to extend to the rest of the business world.

The companies that bet on this approach have not only proposed to get to know better the members of their organizations and the candidates who postulate to their vacancies, they also want to boost the satisfaction and productivity within them. They start from the premise that the decisions in the world of human resources are often based on intuition, subjectivity or prejudice and that, therefore, it is easy to make mistakes. As a solution to this problem, they propose the definition and implementation of more structured processes that also offer a higher level of trust, being the treatment of data as its fundamental pillar.

How is talent measured in data?

Any member of a company generates a considerable amount of data in a constant way, even if the professional is not aware of this circumstance. Up to now, this information has only been taken into account at specific moments. However, over the years, these data are increasingly easy to measure due to the unstoppable development of technology. Workers, among many other things, send emails, share calendars, undergo activities that evaluate their behavior or offer feedback on work processes in their day to day. The People Analytics approach aims to take the traditional treatment of these data one step further.

This proposal sets out a more exhaustive analysis of all this information. Its goal is to facilitate and offer a greater guarantee of success in the decision-making process on issues such as the candidate recruitment, the employee promotion, the compensation and evaluation of performance or the composition of the teams. In short, it is about extracting the maximum potential of all these data. Therefore, the advantages which this new approach promises are many:

  • Getting motivation keys from professionals
  • The improvement of the candidate selection processes
  • The detection of the staff satisfaction levels
  • The promotion of bonds between coworkers
  • Knowing how to predict the number of people needed for a project and defining their profile according to each moment
  • Identifying the skill gaps in order to boost them
  • Predicting and getting to know how to react to the talent drain
  • The management of professionals in a more personalized way
  • Dedicating more time to asks of value in HR

Of course, this approach also raises controversies. The use of the data must also have well-defined limits. The debate about the boundaries of the privacy is still open. However, for this new approach to be effective, it must guarantee the transparency of the use of these data and, of course, it must adapt to the current regulatory framework, the General Data Protection Regulation (GDPR).

 

When talent is measured with data

Looking for the data professionals

Google was one of the pioneers in applying this new approach and creating a People Analytics team. In fact, the company gave name to this discipline. Although its name proposal is the most popular, there are also other alternatives like HR Analytics, Talent Analytics or Workforce Analytics.

Some of the projects that Google has carried out in this field consist of finding common characteristics in the most efficient work teams, Project Aristotle, or managing to identify the best leaders for their company, Project Oxygen. The company has also worked on other issues like talent retention, predictive modeling, the calculation of the value of the best workers or the developing of effective hiring algorithms through data.

The implementation of People Analytics in many companies is still incipient. Although tentative, all the steps follow the same direction. However, the reason why this discipline hasn't taken off yet is partly due to the lack of professionals within the HR sector who have the necessary analytical skills to transform these data into improvement actions. This professionals are called data scientist. Their work consists of the collection of data, the establishment of assumptions, the contrast, the diagnosis, the prediction and the continuous improvement and redesign of the processes.

Not only is the lack of specialized professionals the only obstacle which the standardization of the People Analytics model faces, but the information management systems of data are often insufficient, not properly updated or obsolete. It is necessary to focus on the quality of the data with which we work. On the other hand, companies that want to join the data fever should be willing to open the doors to information and be more transparent, if they really want to achieve remarkable results.

At The CEU IAM Business School we are aware of the importance of offering a training aligned to the current keys of organizational transformation. This is the premise that has guided us in the design of our Master's Degree in Human Resources, the Management of Talent and Leadership. A degree that offers a practical and current approach so that you can apply the knowledge acquired from the first day.

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