Lifelong learning and Data Science.

Finding the time in the ‘muck and bullets’

I have been thinking about the soft skills that make a successful career in ‘Data Science’ (which is where I think I have ended up in my career).  Yes, communications skills are important, teamworking skills are vital, and there is a whole series of documents to be written about the importance of ‘Storytelling with Data’.  However, the subject of lifelong learning (or ‘sharpening the saw’ as Steven Covey would describe it in his great book, “The 7 Habits of Highly Effective People”) is an area that I have struggled with at times, and I wondered how common this was within the Data Science community.

The challenge I have most commonly encountered is that if I know a way to undertake a problem, that I am confident is reliable and will deliver the required outcome, my natural state is to solve the problem, and not contemplate whether there is a more efficient way of undertaking the problem at hand.  This ensures that the required output is delivered, but I am sure there are numerous moments where I have missed the chance to find a better way, simply because I played it safe (and likely inefficiently) to get the answer.  This may be a function of the bulk of career having been in consultancy, and the pressurized environment of ‘having to work at pace’, my bias towards a ‘Fixed Mindset’ rather than a ‘Growth Mindset’ (and the associated challenges that brings), or just being more conservative by nature.

What I know that does work for me is working with others – the opportunity to sit and discuss a problem with others, talk about how a challenge could be undertaken, what might be good to trial or explore, and working out how best to tackle the problem given the constraints faced in terms of time is enriching, challenging and really helpful to me.  Lockdown has diminished the opportunities to do this, but finding the time and space (virtually or ideally face to face) is the key for me to finding the right balance to ensure I keep on learning where possible.