A citizen data scientist is a professional who uses data science as part of their job, but whose main job is not in analytics or data science. They are referred to as citizen data scientists because they lack any formal training in statistics or data analytics, but have learned enough to perform their job function. They are found in data heavy jobs such as marketing, finance, and sales. Citizen data scientists create and utilize predictive models to analyze data and make decisions.
Most organizations rely on data to help them make important decisions. Traditionally trained and educated data scientists are few and far between in addition to being expensive for companies to hire. Not only is training citizen data scientists cheaper, but citizen data scientists combine the best of business expertise and data science understanding. Typical data analytics focus on collecting, analyzing, and reporting data from the past. Citizen data scientists, however, specialize in predictive and prescriptive analytics to help understand what will happen in the future. They use data and machine learning models to determine the ideal course of action for a business or how significant business decisions may turn out.
Data science gives organizations distinct competitive advantages over companies that lack similar capabilities. However, the power of citizen data scientists is often underused. Organizations can best utilize citizen data scientists in the following ways:
Managers must ensure their data scientists have everything required to support their role. This includes people, software, tools, and data. Managers should not make assumptions about a citizen data scientist’s abilities to analyze data and make detailed analyses, instead they should provide requisite training in data literacy. Citizen data scientists should be supported by other professionals, such as data engineers and business translators, that help to fill in the gaps in their abilities.
Organizations should identify where the tools used by their citizen data scientists may be lacking, and provide additional tools and capabilities to bolster these areas. The tools they provide should complement those they are already using. Organizations should add these tools over time so that they do not overwhelm citizen data scientists.
Business extension projects are an ideal opportunity for citizen data scientists to contribute to an organization. Citizen data scientists aid in decision making and allow expert data scientists to complete complicated projects. Organizations must carefully select the business extension projects they assign to citizen data scientists. They should look for projects that focus on existing business processes and products and that allow citizen data scientists to collaborate and learn from expert data scientists.
Citizen data scientists work most efficiently when used as an accompaniment to expert data scientists. Leaders should build communication and collaboration between the two throughout their analytics processes. Citizen data scientists should work with expert data scientists to develop their models and make decisions.
Data is being generated at an ever growing rate and the need for data scientists is equally expanding. The growing need provides opportunities for many people to enter the data analytics industry. A few ways to become a citizen data scientist are:
Data literacy is the knowledge and ability to understand data and glean relevant insights from that data. A data literate person knows how to analyze data to solve a problem. Individuals can improve their data literacy by collaborating with data scientists or through taking online courses.
Becoming familiar with the tools data scientists use is essential to getting a job as a citizen data scientist. Some of the most useful tools include Tableau, KNIME, and Excel. Citizen data scientists will also benefit from basic skills in programming.
There are a number of platforms that contain real datasets for experimentation. Those wanting to become a data scientist can access these datasets and practice using the tools above to find insights.
One of the most important parts of data analytics is the ability to communicate findings with decision makers in a persuasive manner. A data scientist should know how to use data to back up recommendations and understand the key attributes of an effective presentation.