Spot the difference – data scientist vs data analyst


Revenues in the global big data market are expected to more than double from 2018 figures and reach $103 billion by 2027. Given figures like this, it certainly emphasises the need for organisations to ensure they have the right skills in place to capitalise on this obvious growth opportunity. And a key element is to leverage the expertise of data scientists and data analysts. Yet, in my experience, there is still some confusion on what roles and responsibilities these positions fulfil.

I came across this interesting article that examines the main differences (and similarities) between the two roles. And this discussion has become even more relevant today given the uncertainty of the market due to the COVID-19 pandemic. In fact, it is a question I get asked by many students in my Master of Business Analytics courses – and one that inevitably arises from clients not certain about the nuances between these positions.

In this industry piece, the author’s description of a data analyst is spot on. He writes that ‘the focus of data analytics is to describe and visualise the current landscape of data – to report and explain it to non-technical users.’ He goes on to cite skills in SQL, Excel, and Tableau (or other visualisation tools) as being especially important.

When it comes to data science, he describes it as ‘a field of automated statistics in the forms of models that aide in classifying and predicting outcomes.’ He writes that the top skills required by a data scientist centre around Python, SQL, Jupyter Notebook and algorithms.

While I do expect analytical modelling skills in a data scientist, as he mentioned, I also think a more rounded skill set is required from a ‘full-blown’ data scientist, including an understanding of business processes, business strategy as well as all the data analyst skills mentioned above.

Having said that, these fully rounded data scientists are a scarce commodity. Realistically, very few people have all these capabilities and even less have the inclination to get skilled in so many fields, especially when it comes to the business-side of things. However, for a small team within an organisation, or even a start-up, these are the skills needed in the ‘toolbox’ of the data scientist.

When it comes to larger teams, there are more specialised roles available. However, the team lead must be multi-skilled across all the disciplines even if they do not practice it from a hands-on perspective. In these teams, the data scientist role can be one that is more in the form of an analytical/machine learning/automation ‘guru’ who leaves the data wrangling, reporting, and business interaction to their teammates, which can include data analysts.

In many respects this is what used to be called an analytical modeller. Of course, given the slightly wider application of not just analytical models (such as machine learning and automation), the term data scientist is probably more apt within the current landscape. So, while data drives both these positions in the organisation, there are subtle differences, especially when it comes to the business expectations, of each. Irrespective, the competitive organisation of the future will require an element of both to remain relevant in an increasingly dynamic market.

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