With a PhD in Particle Physics and a proven track record of delivering physics and maths based applications, data scientist Thomas Stainer has a clear goal: to get HR departments to embrace the use of data in their daily work. In the video below he explains what HR can learn from politics, marketing, and fusion.
HR is currently experiencing a tech boom, with descriptive analytics being used to drive HR decisions and make huge impacts to businesses. But we can still learn a lot from other disciplines such as politics, marketing and even fusion.
In his presentation during PAFOW Live with Al Adamsen, Thomas gives some examples and explains how they can be translated to the HR context.
During elections, polling predicts what the outcome of the election will be even before all the votes are counted. When you leave the polling station after casting your vote, you are asked who you voted for. These exit polls are basically a way of surveying people, with the crucial fact that the numbers are really small.
In HR, surveys are important to get valuable insights into your workforce. Getting a representative sample of your population while avoiding survey fatigue is key.
Replace voters with workforce and polling stations with offices, departments or teams and we can apply the same techniques to predict behaviour.
In Marketing, the use of data is prevalent. Every click, every mouse movement, every sort of behaviour that you can imagine on the web is being tracked & analysed. By examining time-series data and trends in customer behaviour, such as customer attrition or churn, marketers gain more insight into their customers and are able to predict their future needs, desires and behaviours.
As in Marketing, we also have a lot of time series data in HR. We might be working with different timescales, looking at months & years instead of seconds & minutes, but the techniques are transferrable.
In the same way, marketers gain more insight into their customers by analysing how long someone stays on their website (customer churn), HR and business leaders can gain more insight into their workforce by analysing how long someone will stay at their company (employee churn).
Fusion reactors are very expensive to build, maintain, and experiment with. You can’t just go ahead and try out every possible what-if scenario. So how does this work in fusion?
A digital twin is a digital representation of a physical asset, in this case, the reactor. With this digital twin, we can run simulations to find the answers to our what-if questions.
Just as we can have a digital representation of a fusion reactor, we can also create a digital twin for your workforce. In this way you can run all the scenarios you can imagine and get some predictions on them, by using inputs such as historical data.
Which makes the question ‘If we fire 50% of our staff now, will this benefit us in the long-term?’ a lot less risky to answer.