Using big data as a crystal ball for your career
Many people will know the film Moneyball: the story of the impoverished yet successful baseball team Oakland Athletics. The managers formed a team using sophisticated data analysis with only one goal in mind: to make the impossible possible. When I was reading the Algemeen Dagblad recently, I realised that this way of predicting the future is closer than we think. The article revealed that footballer Memphis Depay had chosen to transfer to Olympique Lyonnais with the help of a specialised data analysis agency. And indeed: the previously washed-up Depay suddenly made five new goals in the first ten games. Is this event a turning point for the world of sport? What role can big data play? And will this data change our career path and hiring policy?
Recognise patterns and choose a direction
Some people believe in the saying: “seven good years, seven bad years.” The bad years began at the end of 2008, and finally, the trust of companies and people is growing again. The strength lies in recognition of rise and fall. Data analysts observe these types of patterns and provide strategic advice accordingly. The first signs of growth in the labour market are, for example, increased flexibility paring. To better cope with peaks, we can outsource recruitment for permanent positions to specialised agencies and headhunters.
Your network is the most valuable tool you have to anticipate recruitment issues. Gain knowledge of the labour market and use this to appeal to active and latent job seekers. Xelvin consultants can demonstrate this art to their customers. The point is that we know precisely whether the candidate the organisation is looking for actually exists or whether it’s a fool’s errand. For example, what percentage of students live near the organisation? Do they leave the region when they go to college or university and then return? If you can corroborate this with knowledge and experience, you can gain insight into the possibilities and availability of potential candidates.
On the other hand, optimum team composition is the key to the success of an organisation. What if an organisation, just like the Oakland Athletics, also put together the ideal team based on big data? The perfect candidate has to have particular abilities for the position as standard. In the context of building the optimal unit, is it maybe better to take on someone with an opposite approach? Someone who can look at the processes and activities in a different way and complements the qualities of the other team members?
The shiniest needle in the haystack
Of course, we are also looking at this at Xelvin. What developments do we see within the various sectors, are there recognisable patterns to how we recruit candidates? For example, do they come via job boards, via our network, or do they respond spontaneously to current vacancies? Measuring is knowing. Based on this information, we can recruit more specifically and find the shiniest needle in the haystack. No, I’m not going to bore you with statistics and typical headlines about the scarcity of technicians. We all know that. We only have to find that one perfect candidate. When they finally sit at our table, we zoom in on their qualities and ambitions. Everyone has their motives, and as a consultant, we can show them what is possible. One objective is well thought out, the other unrealistic or merely unclear. After all, we can’t all have the same dreams, knowledge or experience.
Career path at the touch of a button
In specific recruitment processes, we use assessments to test our first impressions. Often candidates can recognise themselves in the assessment and sometimes it provides new insightful information. Could we link this to big data? Is it possible to demonstrate why a career switch is a good idea or not? Should we go with our gut instincts after a conversation, or would it help us to apply a statistical analytical approach? Could this prevent losing employees and could it promote flow within the organisation?
Memphis took the gamble, and he only concerned himself with one question: Where do I go, and how do I get better? Based on a personal conversation about what Memphis was looking for in a club, they analysed the various offers and advised him to join Olympique Lyonnais. Only time will tell whether this has been the right choice; Memphis is flourishing for the time being. Unfortunately, we don’t have a crystal ball in our possession, but what if we could big data to make the most accurate prediction? It could make the work of an HR Business Partner much easier. We could generate the career path of employees at the touch of a button. Does your organisation see opportunities for big data, and how are you planning to apply them?