The challenge of Data Science

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I recently saw this – https://dartthrowingchimp.wordpress.com/2015/03/19/data-science-takes-work-too/ which is basically an article about the workload of Data Science.

This is a personal and opinionated piece, and all my views are my own and do not reflect anyone else’s. Yet I feel strongly as a working Data Analyst that one of the real unseen challenges is communicating or having people communicate the hard work aspect of it. So I welcome articles like this.

I have seen personally the situation where confusion about what a ‘model’ was led to a very difficult work environment for me. These miss-calibrated expectations that it would just be ‘magic’ or like a feature put unrealistic load on me.

Now maybe one of the things that data scientists must do is ‘explain’ the difficulty and the challenge. Today for instance it took me 3 hours to do a relatively simple bar chart – partly because of the difficulty in finding the data and adjusting the axes etc.

This was not an automated, scripted, process this was a bespoke data visualization developed by me to help share with colleagues and stakeholders the story of the current department I am in. And their challenges and key performance indicators.

I think what is often not acknowledged is just how complicated software and data analysis is – it takes an mixture of hard work, domain expertise, data visualization and modelling – and all these things are changing. I’ve built complicated models and reporting that need changed after 3 months because an API or database changes!

So I think we should share more of our challenges, and our frustrations and our success stories. Our success stories should also not be explained as if we are geniuses – we are just humans with rare and valuable skills.

So this should be explained constantly to stakeholders, and perhaps one of the things we can do is to get our colleagues to sit with us through a data analysis project or mini-project. Rather than just barking unrealistic expectations at us 🙂

I’m still thinking about this, but as Jay says I suspect the biggest problem is that ‘I think most people who don’t do this work simply have no idea.’

Perhaps the lesson here is the following – never underestimate the skill and craft of those you work with, and learn how valuable that is without making lots of assumptions.

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