The Key To Data Disruption And The Convergence Of Data Science Personas Is Asking the Right Questions
Sat, April 10, 2021

The Key To Data Disruption And The Convergence Of Data Science Personas Is Asking the Right Questions

Businesses nowadays are reconciling large amounts of disparate data / Photo Credit: Dmytro Zinkevych (via Shutterstock)


Businesses of today are reconciling large amounts of disparate data, requiring leaders to strategize on how they can make sense of all the information they have on-hand, wrote Dean Stoecker of business news platform Forbes. Executives tend to turn to the assistance of data scientists to help them transform data into actionable insights. Sadly, there are not enough data scientists to solve the plethora of problems businesses face. Therefore, operationalizing data can no longer be the responsibility of one profession or one department. Addressing this issue not only relies on bridging the skills gap. In fact, the skills gap issue can be closed by engaging both data scientists and citizen data scientists and working to converge them into one persona.

Businesses are deluged with new information, resulting in the imbalance of supply of people delivering them and the demand for answers. This leaves complex questions unanswered and talent and money overlooked. Businesses are challenged with turning data into actionable insights to enhance business results and understand how to leverage existing talent. Therefore, analytic maturity is now the key indicator of a company, as business profitability and viability will fall. To engage data scientists and citizen data scientists, they must feel supported to accomplish the tasks that best suit their specific strengths and goals. Data scientists want to leverage their advanced skills to work on unique cases, but they need citizen data scientists to do lower-level analysis. 

Businesses can achieve this by empowering citizen data scientists to fulfill tasks that have been put on a pedestal, but are very achievable. For instance, they can be tasked to build predictive models, which are often delegated to data scientists. It’s not hard but the challenge lies in “understanding what question to ask,” giving companies an opportunity to rethink how talent is utilized to spark curiosity among data workers.  

Most importantly, all data workers in the organization should be empowered to ask the right questions. This can be done when a company assigns responsibility beyond one role or department. Data workers should also be empowered with the right technology and tools to converge the personas of data scientists and citizen data scientists. This way, the convergence of human and machine will also be fostered.