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"
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 HR Analytics
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 behaviorLaszlo Bock in "Work Rules"
Use protypes to move from abstract discussions to applied HR work
- 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 HR Analytics to your colleagues!
Interested? Let us know how we can help!
Featured Use Case:
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.
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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