Published by HRMblogs, on 19/10/2022
By Lena Dirks (HR Transformation Consultant @ Attentia)
Even though the first HR metrics date back as early as 1978, we see that it took until 2000 for human resources to warm up to the idea of using data. If we look at the role of HR-analytics anno 2022, we see HR analytics it is often focused on the follow-up of operational activities. Things like ‘who has completed their performance review’ or ‘which are the popular e-learnings.’ Moving beyond this point of operational reporting and truly guide HR-policy, strategy and delivery is for many a faraway dream.
As someone who loves to work on HR-analytics projects, it is baffling to see that most ‘marriages’ between HR and data hold so little love. Below I list three obstacles that I found came back every single time HR and IT (or data) needed to overcome to be able to grow towards each other.
- Learning each other’s language.
HR and IT do not speak the same language. As we all know, speaking the same language makes the development of a relation easier but is not necessary to get it started. However, for a lasting relation, each party needs to adapt and learn the language, the customs, and the culture of the other. In many cases this big hurdle is not identified and is the biggest reason for miscommunications and misunderstanding which sour the relation, making the marriage anything but happy.
- Understanding HR data: Qualitative numbers and quantitative labels.
Both HR and IT needs to learn that within HR numeric does not mean quantitative and vice versa. In HR numbers don’t always hold the same meaning and are often not quantifiable. HR has a lot of what I call, ‘grey’ numbers. For example, in a performance review. Scoring someone an 8 out of 10 on ‘client centricity’ is a good score. However, it will have a different meaning when talking about a junior developer, a sales director, or a senior receptionist. Both the assumed level and the linked behavior will differ even though they all received an 8 out of 10. On the other side, non-numeric data like a ‘pass, fail or completed’ label for a course, can tell you a lot. It has a more black and white meaning than the 8 out of 10 on client centricity.
- HR data is often human based and therefor not always honest.
When it comes to emotionally or politically loaded information, people lie and distort. For example: When we are evaluating someone, and we are aware this will impact his or her career chances or the possibility of a promotion, other factors start to play a role. This is basic and normal human behavior. Confounding factors like ‘If I score them negatively, I have more chance for a promotion’ or ‘I like this person, I’ll score them higher’ or even cultural factors like ‘Belgian people rarely give 9 and 10 out of 10 compared to our northern neighbors’. There are even seasonal effects, f.e. overall people are more satisfied during summer, compared to the middle of winter. Never ask shortly after the winter holidays. Worst timing ever! These effects can confound your data so you need to be aware of possible underlying issues that can manipulate the data and lead to incorrect conclusions.
In short if we need to invest in a better understanding between HR and data analytics. Starting with learning each others’ language and respecting each others’ expertise. By doing so HR analytics projects will move beyond operational reporting and provide the added value that comes from joining both fields.
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