At Ozri in Brisbane, after a round of introductions with attendees from local councils, the discussion turned to a very pertinent topic for the GIS professionals sitting around the table — the trending demand for data scientists.

The term data science was only coined in the early 2000s. In 2012 the role of a data scientist was dubbed the ‘21st century’s sexiest job’ and in 2016, Glassdoor listed it as the top job for that year. It still is.

It seems that everyone wants a data scientist now. If you search for data science jobs in Australia on LinkedIn, you’ll get more than 600 results. On the other hand, if you search for GIS jobs, you’ll only get half that number.

What’s more interesting is that the average annual salary for a data scientist according to a recruitment portal in Australia is $127,497 whereas the same website put the average salary of a GIS analyst at only $100,000 per annum.

It seems that instead of taking over jobs, the proliferation of analytic technology has actually created new opportunities for making sense of the data that’s being generated on a daily basis — in Australia, that translates into 2.7 million new roles by 2020. But what exactly is a data scientist?

I’ve seen data science positioned as everything from a business function that improves bottom lines, to data scientists being responsible for attracting, retaining and upselling to customers and managing risk. 

The assumption here is that a lot of the data we work with is not spatial, as there’s a tendency by mainstream science not to consider spatial thinking as fundamental to the scientific process itself.

But if we overcome this ‘space scepticism’, it becomes apparent that GIS professionals have been analysing data – in a spatial context – to unearth insight that informs policies, improves bottom lines and helps attract and retain customers as well as manage risk for years.

Academic institutions have jumped on the bandwagon and invested in data science programs, churning out data scientists and analysts to meet the growing demand.

But not all organisations are going to market to fill this role; some look within their GIS ranks for those who have a knack for managing and analysing data.

This signals the need for a shift among GIS professionals to be prepared to step into the role of a data scientist.

These new opportunities might entail learning new things but will ultimately expand the role of the GIS expert to delivering insights by effectively using spatial data to make critical business decisions.

Whether organisations opt to hire new data scientists or look within for the GIS professional who can fulfil that role, it’s important to realise that the success of data science is fundamentally underpinned by GIS.

While data scientists look at data patterns and relationships through keywords, metadata and relational table links; GIS looks at them through real-world place/time models which accomplish many analytic purposes more simply and faithfully.

For more on the importance of data analytics in a digital transformation and the capabilities an analytics team needs to have, read Dr Amen Ra Mashariki's blog Want a smart city? Hire a chief analytics officer.  

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