Many of us appreciate the need for skilling up and continuous learning. The rhetoric “there is a need for continuous learning” is constantly driven into us through various forms of messaging. From a nation’s point of view, having a workforce which updates itself according to changing business needs is advantageous. For the individual, picking up new skills carry the prospect of better employability and higher wages.

In reality, however, it is more important to have the right combination of skills and I would argue that picking up new skills or knowledge in a domain different from your existing one is more beneficial. This is so even if the new skill or domain is completely different from your current skills set.

Take the Human Resource (HR) profession for example. Here at JobTech, our analysis of online job postings reveals an increasing demand for HR managers to possess digital and data-related skills such as Tableau (for data visualization) and R (a statistical programming language that supports data mining and analysis). These skills serve to complement traditional HR domains such as Performance & Payroll Management or Learning & Development.

I have picked up Python, SQL and JavaScript among other technology skills which enabled me to work as a data analyst in JobTech. I am trained in Economics but I do not see this as a limiting factor. My training in Econometrics has helped me understand time series and machine learning concepts such as regressions to a deeper level. Economic thought models are transferable here too. For instance, imperfect information causes labour market frictions; job seekers are not aware of the available jobs which they are suited for. Thinking about how JobTech can ensure that the matching is performed right and at scale will help define our role in the labour marketplace.

Wei Xuan at Pycon 2018Recent picture of me speaking at Pycon APAC 2018

Or imagine that you are a Data Scientist in an e-commerce company. Your immediate peers and colleagues are likely also experts in data mining who spend time updating themselves on the latest advancements in machine learning and areas central to their work. However, armed with a multi-disciplinary approach to learning, you could apply knowledge from other fields, for example, Psychology, which enables you to tap deeper into the consumer psyche and blend your data insights to produce better and more relevant product recommendation engines. Exposure to other domains gives you additional perspectives and create innovation from gaps between different verticals.

Therefore, I believe that a multi-disciplinary approach of self-improvement could produce benefits beyond a mono-disciplinary one. Entrepreneur and author Michael Simmons[1] who studied Elon Musk’s career puts it in this manner “I call people like Elon Musk ‘expert-generalists’… Expert-generalists study widely in many different fields, understand deeper principles that connect those fields, and then apply the principles to their core specialty.” I would like to point out a caveat here. A multi-disciplinary approach does not necessary mean you do not go deep. Rather, go deep across different domains you see synergy in. In other words, the effectiveness may have diminishing returns if you spread yourself too thinly. Have a general goal in mind and learn across domains specific to where you want to be.

Lee Wei Xuan

Lee Wei Xuan
Data Analyst at JobTech

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[1] https://medium.com/@michaeldsimmons/how-elon-musk-learns-faster-and-better-than-everyone-else-a010a4f586ef