HR 4.0

The sense and nonsense of HR analytics

July 15, 2021 - 5 minutes reading time
Article by Marcel De Dood

Data is the new gold. All companies and processes want to become data-driven. We have to make decisions on the basis of objective data instead of 'on instinct'. This also applies to HR policy. That's why HR analytics is high on the agenda, because only then are HR departments really taken seriously. Do HR managers dream of an ideal that is unachievable? Time for a critical note.

Gartner and the Analytics Value Escalator

Research and consulting firm Gartner has developed a development model for analyzing business data, the Analytics Value Escalator. 'Looking back and justify' is the first step in that model, with only little value for the organization. Analytics only makes sense if the Business Intelligence system prescribes what needs to be done to get more out of the organization's human capital.

Analytics Value Escalator

The (non)sense of descriptive analytics within HR

Numbers tell the tale. That is why looking back on and reporting on HR's KPIs is very important. However, due to privacy rules, it is becoming increasingly difficult to properly report on the KPIs. Take the diversity policy. Without recording religion, sexual preference and origin, this can hardly be data-driven. And since it is no longer allowed in the Netherlands to ask about the nature and cause of absenteeism, the value of absenteeism figures has declined sharply. That value was already limited. When discussing HR reports with a management team, you mainly hear: “Ah, does Martin belong in my department?” "Yes, high frequency, that's what you get with young parents." Or: “That was the flu wave, wasn't it?” At such moments it often becomes clear that everyone has their own truth or that managers have a different view of their department than the administration suggests.

And the real value then?

HR analytics mainly uses data from the HR system. In the case of high turnover or absenteeism, correlations can be investigated with travel distance, age, number of years in office, salary compared to colleagues, absence history and the latest assessments. Data analysts find this far too limited and these kinds of connections actually boring. A good example of this can be seen in an article, which states: "It is not age, intelligence or dedication to the company that determines whether a mailman is good or mediocre, but owning a dog is the best predictor of good performance." I especially wonder how the analysts arrived at a reliable data collection that includes performance, intelligence, dedication and ownership of a dog. And what do you do with that knowledge? “You are a great candidate, smart and dedicated. But guess what, you like cats more than dogs. What a pity.” No, right?

Employee engagement

In my opinion, the most important predictor of absenteeism, turnover and performance is not owning a dog, but rather 'the involvement of the employee'. Google knows more about employee engagement than her manager. Google knows what time the employee is at work, how often and at what times she checks her social media, where her friends work and which job sites she visits during and after working hours. What does HR know about her involvement? At best HR asks the employee anonymously once a year and rarely the manager.

Bad manager disastrous for engagement

Research by the Hay group, among others, shows that the most important driver of employee involvement is in the HR system. The manager! The bad manager overloads employees, does not recognize good contributions, does not help with development, is not interested in people, hires the wrong people and does not challenge employees enough. In other words, the bad manager is disastrous for the involvement in his team. Employees therefore do not leave the company, but they leave the manager. The manager is the causal link between absenteeism, leave and poor performance that you are looking for with HR analytics.

Prescriptive Analytics

The holy grail of HR analytics is prescriptive analytics. The system infers from correlations that you should take measures A, B and C to improve employee engagement, after which turnover, absenteeism and performance improve. Does a good manager need this advice? Probably not, it is precisely the connection with employees – so that he knows what is needed – that makes him a good manager. Does a bad manager make better decisions based on advice from the system? Also not. If the system does the analysis well, the advice will often be that the manager should improve his performance. The bad manager will look for and find all the excuses to disguise his own role.

Solution

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At the table with the management with HR KPIs

HR analytics is different from the analysis of sales figures or logistics efficiency. It is about people who work together in complex organizations. Only a minuscule part of their work, thinking and doing is recorded in the systems available to HR analytics. The vast majority cannot or should not be used. With figures, HR comes to the table of the management. Because the necessary data is not available and analyses will add nothing, that seems far away. So make the most of the only important data that is available. Report and analyze her HR KPIs and track the manager throughout the organization. Confront him with those analyses. Not the safest way, but we all know: "No friction, no shine!".

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