Are you a citizen data scientist? Do you even know what the term means? I’m keen to debunk any derogatory connotations behind the term and show that citizen data scientists are a vital part of the analytical ‘ecosystem’.
I was a Government Statistician when data science first became popular and I remember not really understanding the meaning of the term data scientist. More recently, I came across the term citizen data scientist and was yet again left a bit puzzled, even though by then I was considered a data scientist myself. So what is a citizen data scientist and how are they different from data scientists?
Defining citizen data scientists
ChatGPT describes a citizen data scientist as “a non-professional, who possesses a certain level of expertise in data analysis, and who is able to use data-driven approaches to solve business problems”. It suggests that a data scientist is “a professional who uses statistical and computational methods to analyze and interpret complex data sets”. Is it OK to disagree with ChatGPT? Having made the transition from statistician (or citizen data scientist) to data scientist, I feel qualified to comment.
I think the main difference is that citizen data scientists lack expertise in the software development activities required to develop, manage, run and deploy models. However, they are just as valuable as data scientists, albeit in a slightly different way. Here are five reasons why they have an important part to play in data science and why we need both roles.
- Citizen data scientists may have fewer qualifications - but their experience can make up for that
Qualifications are important, especially when you are developing extremely complicated methods to complement and/or replace human processes. However, on their own, they don’t make a great data scientist. Citizen data scientists often have more real-world experience and can therefore provide insights into how data science techniques can be used in practice.
- A lack of understanding of data science is a data science problem
As a data scientist, it’s part of your job to help people understand data science. If someone doesn’t understand, it’s because you didn’t explain it right. Citizen data scientists are often much better at simplifying technical detail so that non-technical people can understand and can therefore act as a bridge between data scientists and business users. This is crucial because if managers don’t understand what you are trying to do, then you won’t get the sign-off/investment you need.
- Understanding business needs outweighs a fancy model
You can have the best model in the world, but if the users aren’t on board, it’s a waste of time. Some of the best data scientists I’ve ever worked with have learned this the hard way. Citizen data scientists are often far more realistic about what works for business users and what’s achievable. They also often place more emphasis on understanding the complexities and nuances of the data and engage with subject matter experts and front-line staff who are working on the problem day in, day out. This leads to more successful projects because issues are ironed out before deployment. This business/data science collaboration is essential to solving the data science skills gap.
- It’s not stupid to admit to mistakes
It’s common for people to say that there’s no such thing as a stupid question. I’m not sure I agree with that; I just don’t care if my question is stupid. It is more important to understand something than to avoid looking stupid. In my experience (and wildly generalising here) data scientists often don’t like to admit to mistakes and will blame ‘the model’ or ‘the data’. Citizen data scientists tend to put less pressure on themselves to be perfect and find it easier to admit to mistakes. We are all human and a little humility goes a long way.
- Every day is a learning day
Whatever your job title, there’s always something new to learn. There are also always people who want to learn from you, so take time to share your knowledge and support your colleagues. As I look back on my transition from citizen data scientist to data scientist, my most valuable lessons have been through learning from others. The two groups can learn a lot from each other!
And a bonus tip...
It doesn’t matter whether you are a coding expert or you rely on point-and-click tools to explore data science techniques. Nobody can solve a problem on their own, so you need to work collaboratively regardless of your job title.
