You don't need to be a Data Scientist or have Data Scientists in your organization to start leveraging advanced analytics.
What is a Citizen Data Scientist?
According to Gartner, Citizen Data Science is “an emerging set of capabilities and practices that allows users to extract predictive and prescriptive insights from data while not requiring them to be as skilled and technically sophisticated as expert data scientists”.
Clearly, there is a lot to unpack here, so let's start with the difference between the role of a Data Scientist versus a Citizen Data Scientist.
Role of a Data Scientist versus a Citizen Data Scientist
A 'Citizen Data Scientist' is someone who is employed within a functional area of the business and is able to combine their knowledge of the business with advanced analytics to add value to the organization. They are typically not part of a dedicated data science or advanced analytics team. Rather, they're a representative within their own domain who understands the fundamentals and concepts of data science, but more importantly, understands how to leverage those data science tools and techniques to solve problems within their domain. That is, they use these data science skills as a complement to their primary role, not as their primary role.
It's important to understand this distinction, as a Citizen Data Scientist should not be expected to perform the same role as a Data Scientist. And nor should a Data Scientist perform the same role as a Citizen Data Scientist. Rather, the two roles should be complementary, with Data Scientists focused on exploring, discovering, creating and unlocking new techniques which may be relevant right across the business. And Citizen Data Scientists focusing on applying, adapting and scaling these techniques specifically within their own domain.
What knowledge and skills should a Citizen Data Scientist have?
Firstly, unlike Gartner, we avoid making distinctions between the two roles by making reference to proficiency in the use of techniques, or in terms of the complexity or difficultly of the types of problems being solved. In-fact, we believe that distinctions such as these, 'can' be a common mischaracterization for many organizations who are looking to grow their citizen data science capability.
In fact, it may be that a Citizen Data Scientist has a greater understanding of a particular data science technique than their Data Scientist equivalent, simply due to the fact that they dedicate the majority of their time towards using that technique to solve their business problems. And likewise, the particular domain that a Citizen Data Scientist operates in may be highly complex or even highly analytical in itself, which makes distinctions on the basis of complexity dangerous to make.
But what we will say, is that all Citizen Data Scientists should have at least a foundational understanding of data science. Including understanding the data science workflow, relevant statistical concepts, trade-offs of broad techniques, how to correctly frame data science problems, and also how to avoid common pitfalls and errors when applying data science techniques to their problems. These 'building blocks' of data science are essential for any Citizen Data Scientist to grasp, well before they consider focusing in on applying any particular type of technique to their domain.
On the tooling side, the Citizen Data Scientist should have the ability to use tools to apply techniques which are suited for problems within their domain. But it's important to recognize that a Citizen Data Scientist may not need to have any coding or scripting skills. There are tools such as KNIME for example, which allow the development and use of data science workflows using a completely visual means. Or, it may be that the dedicated Data Scientists within the organization can package solutions which allow the Citizen Data Scientist to make necessary adaptions in a fairly low-code manner.
Why are Citizen Data Scientists important?
Traditional Data Scientists are in high demand. And as a result, it can be extremely difficult and expensive to recruit and retain a large team of talented individuals who have already earned the 'Data Scientist' title. Not only that, but trying to find a Data Scientist who also has a deep understanding of a niche domain, may be even less realistic for many organizations.
But what many organizations do have, is a pool of talented individuals across the organization who have an interest in data and the right set of underlying skills and traits to perform a citizen data science function. That includes more traditional IT roles, such as Business Analysts, or more specialized roles, such as Engineers or operations experts. These individuals often work with data day-to-day, but may not have been exposed to data science concepts or techniques.
And so, while we are not advocating that Citizen Data Scientists eliminate the need for Data Scientists, we do believe that a solid citizen data science development and training plan can provide a more suitable and efficient means for organizations to add value to their organization through use of data science.
Want to learn more?
Contact us if you want to start working towards building your organizations citizen data science capability.