Welcome to the latest edition of our monthly “Most Trending HR Articles”! Once again, we’ve selected the best HR analytics articles for you to read and inform yourself. Enjoy!
#5 Podcast: Busting the myths of employee engagement.
We’re starting this edition off with something a little different: A podcast! In Max Blumberg’s latest podcast, he talks with Laurie Bassi, CEO of a people analytics consultancy, about widely believed myths in employee engagement.
In their episode, they discuss:
- The relationship between HR and economics
- The common misconceptions around employee engagement
- How technology is unravelling this complex relationship and the asymmetry between different organizations
- Advice for HR professionals on how to rethink the way they approach the question of employee sentiment and its impact
- Kinds of data that could be relevant, and how to frame everything in the context of finance and performance.
You can find the podcast on the Tucana website or listen to it below.
#4: How men and women are treated differently at work.
You probably have heard about the #metoo movement that is shaking up the film industry. But there is another area of gender inequality which is even more prevalent: The difference in treatment between men and women for the same quality and quantity of work. Stephen Turban, Laura Freeman, and Ben Waber investigated this phenomenon by collecting concrete data, instead of just relying on surveys and assessments.
They collected communication- and sociometric data of 100 individuals within a company and then analyzed the data. They found out that the several commonly held ideas were wrong:
- They first saw that women had the same number of contacts as men. They were also just as central as men in the workplace’s social network.
- Women had the same type of access to leadership and were just as proactive as men in talking to senior leadership.
This shows that the difference in treatment between men and women was not due to their behavior, but due to how they were treated.
Want to learn more about the study and what your company can do about these findings? Read the rest of their insightful study here.
#3 Is HR in Europe Ready for Analytics
Recently, we were at HR Tech World in Amsterdam where the brightest minds in the field of people analytics came together, including David Green! He showed that, despite people analytics becoming more and more mainstream, Europe is still lagging behind the US.
In his article, David shows the foundational steps required to successfully bring analytics to the HR function. He also analyzes several reasons why Europe might be behind – and how they can be fixed.
Read his full article here.
#2: Data storytelling – Know your audience
Jonathan Ferrar is no newcomer to our list of authors in our “Trending HR Articles”. This time, he talks about an important and often overlooked aspect of analytics: Storytelling. You can have the greatest data analysis, but without the right type of storytelling your message will never receive its due attention.
Jonathan argues that this is due to the commonly held belief that data analysts think their message lacks credibility. They compensate by explaining the process and statistical methodology behind their analysis. However, this often backfires.
Your audience isn’t concerned about you, your process or your expertise. They are concerned about how the message you are there to deliver is going to impact the business.
He continues his article by showing the different ways in which you can customize your message to your audience. Depending on your audience’s level of knowledge (see the figure below), you have to adjust your message.
Read the full article here.
#1: Doctors swear to “do no harm”. Why don’t data scientists?
I love articles that go beyond the latest implementations of data analytics in HR and look at the broader perspective of data science. Therefore, for our #1 article of this month, we have Tom Cassauwers discuss the often-overlooked ethical side of data science.
Data science is making such progress that we hear all the time how data is the new oil. However, data scientist Charles Givre argues “that if mishandled, data can also be the new TNT”.
This is clearly shown when certain firms have to start refusing requests due to ethical considerations. An example would be when the consultancy firm TriFinance refused to determine whether it would be financially cheaper for a hospital to let someone die on the operating.
This is one of many cases where data science can have life-threatening repercussions. Tom Cassauwers discusses this more in-depth, with other examples, in his article here.
We’re not going to stop at #1! Recently, Paul van der Laken and colleagues (2017) published a study in which he and his team looked at the impact of HRM decisions. This is a great article for the data scientists among us. In the article, Paul explains why your ‘level of analysis’ is important. In particular, he demonstrates how the latent ‘bathtub model’ can accurately analyze subtle patterns in data at different levels of analysis. This can help you unveil longitudinal patterns in HR data.
You can read more about it here.