Understanding the notion of a Data Scientist
Before we dive into the subject and define “Citizen Data Scientist”, let’s first understand the concept of the Data Scientist. Data Scientists are responsible for the management, analysis and use of data in a company. At a time when the quantity of data held by companies is exploding, the key role of the Data Scientist today is knowing how to make the best use of the amazing quantity of data that companies hold.
Adding the term “citizen” to this job title might seem strange, even jarring. The concept is a recent one, dating back only to 2015, and was created by the American advanced technique research and advisory firm Gartner, which observes and analyses tends, anticipating changes in the world of technologies.
Adding the term “citizen” (typically someone without specific scientific training) to a very precise and technical job title is surprising. Gartner defines a citizen data scientist as “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.”
Can anyone be a Data Scientist?
As tasks are simplified and automated, ordinary citizens will, in the future, be able to do the work of data scientists. Gartner estimates that by around 2020, 40% of tasks currently performed by data scientists will be automated.
The key to this simplification of business activities lies in the automation of repetitive and manual tasks, which will ultimately allow everyone to analyse data and make data projections, taking the different client use cases into careful consideration.
This phenomenon does not only apply to the universe of data analysis; it also applies more generally to all economic activity: in banking and the field of energy distribution, for example, this evolution has already occurred.
Business use cases are the pillars of the simplification of the data scientist’s function. The definition and use of these business use cases inherent to the challenges of data are therefore the fundamental element that will facilitate the transition between the Data Scientist and the Citizen Data Scientist.
The Design Thinking approach is also important, as it provides companies with the necessary flexibility and internal collaboration for training and monitoring, as well as promoting the data and its use by different users.