With the advent of new technologies such as Big Data, Artificial Intelligence (AI) and Machine Learning (ML), we have also witnessed the swift emergence and even swifter rise of a new kind of professional profile: the data scientist. As recent market research by Gartner shows, this new breed of computer scientist also brings with him (or her) a new category of tools in the form of Data Science and Machine Learning (DSML) platforms.
Last year, LinkedIn labeled data scientist “the most promising job of 2019”. And what’s more, according to that professional network’s founder, data science-based jobs actually grew 15 to 20 times over a three-year period. It clearly pays, therefore, to be a data scientist these days - literally as well as figuratively.
Expert data scientist: hard to come by
From a recruiting perspective, the main problem with data scientists is that they need to acquire high levels of advanced education in niche areas of math and computer science before they can even enter the field. The large majority of expert data scientists have at least a Master’s degree, and nearly half of them have PhDs. Most common IT professionals simply do not have the skills to build the algorithms required to power predictive analytics, data mining, and other big data techniques that businesses need. So while there is currently a huge demand for people with such skillsets, there are not nearly enough applicants to meet that demand.
That leaves the actual hiring of an expert data scientist out of reach for many if not most businesses. Luckily, along with the growing demand for expert data scientists, the amount of business intelligence (BI) tools has also been steadily growing. And, as is often the case in our industry, combining the right software with the right employees has created another new and equally crucial role, namely that of the citizen data scientist.
Citizen data scientist: homegrown
A citizen data scientist is basically a software power user with above-average analytics skills, to the extent that he or she is adept at applying analytics to solve business problems. More specifically, a citizen data scientist should be tech-savvy enough to perform moderate data analysis tasks, using software features like drag-and-drop tools and prebuilt models to create analytical models without code.
But the truly great thing about citizen data scientists is that, unlike expert data scientists, you don’t need to look for them outside of your organisation in order to recruit them: they are already a part of it. They could in fact be anyone from a web developer to a marketing director, provided that they can apply their industry experience and business knowledge to data analysis. It is important to keep in mind, though, that they cannot and do not replace expert data scientists. Instead, they complement them or, more to the point, they complement each other.
Data science tools
Just as both profiles work best when they work together, both profiles also work best when they have the right tools to support them. And this is where Gartner’s Data Science and Machine Learning (DSML) platforms come in, supporting variously skilled data scientists in multiple tasks across the data and analytics spectrum. In my next post I will take a closer look at this emerging new market and share some of Gartner’s main insights with you.