While a citizen data scientist may not perform a formal job function at a company, they still play a vital role and may participate in breakthrough discoveries. When citizen data scientists are able to master the tools used by the experts, they act as valuable members of an organization. Citizen data scientists do not replace data scientists, but are intended to collaborate with them to accomplish more work in shorter timeframes.
How to become a citizen data scientist
Anyone can perform the role of a citizen data scientist, but it helps to have a parallel background in something similar to the field that the experts are researching. Becoming a citizen data scientist involves doing some research and following a few simple steps:
- Request access to the newest and best data.
- Learn how to use business software and other analytical programs.
- Stay familiar with security protocols and be careful not to compromise raw, protected data or secured storage areas.
- Work with an expert in the research. For instance, some companies have created a role called data guardian, who is someone that checks in with data scientists to learn best practices and receive other guidance.
- Become familiar with complex skills like machine learning, business analytics, statistics and coding in various programming languages.
The importance of citizen data scientists
The role of a citizen data scientist has become more important for organizations to incorporate as there is a shortage of trained data scientists. Instead, data science roles can be filled by employees with various backgrounds that know how to use big data tools and create data models. By using skills across teams or training employees in new areas, organizations can save money, operate more efficiently and make better use of data.
Citizen data scientist vs. analytics translator
Analytics translators are similar to citizen data scientists in that they do not require specialized data analytics or IT training. However, analytics translators start the process that is carried out by a data scientist or citizen data scientist. They use tools and business intelligence to help identify patterns, trends, problems and potential opportunities is cross-functional initiatives like production or pricing. Once the initial research is done by an analytics translator, it is passed on to the rest of the data analytics team to dive further into the nuances, produce reports and make decisions.