Imagine you did not know what an Excel Pivot Table is. Imagine you had never calculated a correlation. How could you possibly know about the beauty and power of data? HR professionals and Data Scientists speak different languages. Carsten Knaut, Head of HR Transformation at QIAGEN, suggests you put processes, IT tools and data aside for a moment. Instead he proposes to find a common language that enables HR professionals to ask the right questions, which data scientists can than translate into applicable insights.
The problem: Buzzwords can scare colleagues away
Carsten describes a typical dialogue between a HR Professional and a Data Scientist:
DS: "Data / people / HR / Predictive Analytics will enable you to make your work and impact measurable."
HRP: "Oh yes, but hey: we are not Google, we don’t have reliable data, we don’t have the capacity etc. etc."
Often HR Professionals see a lot of barriers when thinking about data analytics, resulting in not very constructive conversations with Data Scientists about this topic. Frustrating for both parties. Carsten compares this to the Babel fish:
Towards a common language: An alternative dialogue
As a solution to this problem, Carsten suggests Data Scientists should ask HR Professionals the right questions to start the dialogue:
DS: "Which... myth would you like to burst / gut feeling would you like to verify / rumor would you like to falsify?"
HRP: "Oh if I could I would like to… check if turnover is really a bad thing / verify that we are paying below market / proof that our sales training generates revenue."
This is an easy example, but it shows how simple it is to start a constructive conversation by asking the right questions.
Helpful surroundings for HR Analytics
A wish list every Data Scientist has when it comes to data analysis:
- an integrated IT landscape
- consistent data
- dedicated HR Analytics resources
- Data Analytics knowledge
But what if none of these exists? Then start small and make data analytics tangible. Show the organization what the business question is, state clearly if system integration is needed, how much data sources you need, what resources you need and if you have to make use of any special expertise. This gives HR Professionals some kind of image of the impact on the organization. HR Analytics is more than ‘retention prediction’, states Carsten.
So... How to bridge the Data Analytics gap?
- Focus on the why helps to find a common language
- Acceptance of imperfect surrounding condition helps to find a realistic starting point
- Elementary examples help to make the value of Data Analytics tangible