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HR Analytics: A huge gap between perceived importance and readiness! 


from 11.070 respondents* say: "People Data is important or very important"


from 11.070 respondents* say: "our organization is ready or very ready for HR analytics"

This gap as reported by Deloitte in their 2018 study Global Human Resources Trends* and is also confirmed by us talking to HR and business leaders. Therefore, we have explored the challenges leading to this gap, as well as show the role of prototyping to bridge the gap efficiently.

What are the challenges in HR and Business which result in this gap?

No funding: Compare to using analytics in marketing or sales, HR has a challenge to obtain funding for analytics as it is tough to create an Return on Invest logic and prove cause and effect

Lack of Diversity & Talent:  the typical HR career starts in HR and often ends in HR. This lack of diversity makes it difficult to innovate.  Transfering business talent into HR is therefore key

Risk aversity: HR has a culture of looking at downside risk, while in particular sales has a culture of looking at upside risk. HR leadership needs to quantifiy business values to be a true peer

Data & Tech: Many HR organizations are overwhelmed when it comes to tech. There is neither a clear IT/Tech strategy for HR nor processes to collect, clean and store data.

Set expectations and address fears about People Analytics

A key first step is to get the HR leadership to buy into HR Analytics. Besides recognizing that this will be a long journey of investing and learning, there are fears, which need to be adressed upfront.

Thereare two quotes from the book “Work Rules”, by Laszlo Bock (former Senior Vice President of People Operations at Google), which I think put the interaction of intuition, biases, and data-driven decision in HR perfectly in perspective:

I don’t think you’ll ever replace human judgment and human inspiration and creativity because, at the end of the day, you need to be asking questions like, O.K., the system says this. Is this really what we want to do? Is that the right thing?

One of the applications of Big Data is giving people the facts, and getting them to understand that their own decision-making is not perfect. And that in itself causes them to change their behavior

Laszlo Bock in "Work Rules"

former Senior Vice President of People Operations at Google

Use protypes to move from abstract discussions to applied HR work

A key sucess factor is to show a prototypical application to your HR colleagues or even the decision-makers as soon as possible. Why is this?

  • If you start a major HR analytics project you need funding and time. Often organization spend an enourmous amount of time on setting up the project, collecting the data, discussing technology, scouting for vendors or big consultancies
  • The typical results is, everybody in HR and business is hyped or concerned (depends who you ask) in the beginning. As nothing really happens for a long time, disappointment or “I told you so” is setting in, and your HR Analytic project might die before it really starts.
  • Instead, we are advocates of rapid prototyping and iterative learning (And yes I am biased as I have an ecommerce background). The great news is that via open source technology and programming languages such as R and Python, paired with a large global HR Analytics community, you can to get off the ground extremely quickly.

Seeing is believing…Show our interactive People Analytics to your colleagues!

Interested? Let us know how we can help!

Please select what you are interested in! *

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Featured Use Case:

Our app features an attrition use case, meaning we predict how likely a certain employee will be to leave the company. While the chance of leaving are interesting per se, we also put this in perspective with a) the performance of the employee, and b) the market value (approximated by salary). 

Data Used

The data used is sample data, which was provide by IBM Analytics. The data contains 1.500 people and 34 variables, such as age, salary, overtime, number of business trips, home-office, and so on. 

Method & Visualization:

Our app leads you in an interactive way through different steps: 1. uploading the data (use our sample data), 2. Exploring the data with a interactive dashboard, 3. Running different machine learning models to determine what drives the probabality of an employee leaving, 4. a result dashboard, showing you the endangered employees and charting  performance and chances of leaving. 

What's next?

Our suggestion would be to fill in the form and get free access to the app and the sample data. Then you can explore yourself. Then show and discuss the use case & dashboard with your colleagues. It is important to understand that this use case is just one example. If you have other questions, this can also be modelled.

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