Using Citizen Data Scientist to Bridge Data Skills Gap
By Arijit Banerjee
A perfect storm is brewing in the world of analytics. On one hand, organizations are prioritizing the use of advanced analytics such as predictive and prescriptive analytics to run their businesses better and on the other, there is an acute shortage of expert data science talent. The current gap between the demand and supply of deep analytical talent is already 50%, and it is expected to get worse. IBM predicts that, by 2020, the demand for data scientists will further increase by 28%. While the war for scarce expert analytical talent continues to rage, companies are striking middle ground by training their in-house talent to tackle certain analytical tasks. The result: the birth of the citizen data scientist.
How are citizen data scientists mitigating the skills gap?
Gartner defines a Citizen Data Scientist (CDS) as “a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.” Citizen data scientists are essentially ‘power users’ who do not have a background in computer science but have expertise in business analytics, risk management, marketing, and operations. Even though they lack an IT background, they know what questions to ask and what insights are relevant for business decisions.
Here are three ways in which organizations are turning executives and managers who are not embedded in IT into citizen data scientists to optimize available talent and bridge the existing skills gap.
Investing in rigorous training
Business leaders are shifting their focus from hiring to employee development and training when it comes to fulfilling their demand for data and analytics skills. Creating citizen data scientists out of corporate employees who are not IT professionals, requires intensive training and support to help them go beyond surface level insights.
Who’s doing this?
SAS has been an early mover in this area. It has developed a unique program called “Data Science for the Business User” to address its skills gap and add value to corporate decision making.
Deploying technologies that are as easy as drag and drop
Organizations are deploying tools and technologies that make it easy for citizen data scientists to extract insights for business decisions - on their own - without the need for data scientists. Employing Natural Language Generation (NLG) based tools is one way of promoting data literacy among employees. It is one of the easiest technologies that not only assists employees in performing data-specific tasks but also makes the transition smoother. NLG, a subset of AI, converts complex data into clear, natural language to overcome practical skill gaps as well as any psychological inertia towards learning analytical skills.
Who’s doing this?
Back in 2016, IBM empowered tennis professionals through its Watson data analysis program, powered by NLG, to gather unique insights into players’ performance – essentially turning tennis professionals into citizen data scientists.
Democratizing data science and analytics
Another way to enable employees to become efficient citizen data scientists is to make data easily accessible to all employees in order to reduce dependency on professional data experts. Open source engines such as GoogleTensorFlow, deep learning libraries like MXNet and programming languages such as Python and R are contributing to the rise of citizen data scientists by democratizing the lower levels of data science.
Who’s doing this?
Software tools and BI platforms such as Tableau and Sisense are providing solutions to companies for democratizing and simplifying data for their employees.
Citizen data scientists are not meant to replace data scientists. Both roles work in sync to create greater value for the organization. As citizen data scientists become more efficient and self-sustaining, experienced data scientists can focus their energies on tasks that require a higher level of expertise in analytics. According to the PwC Global Industry 4.0 survey, around 40% of the companies surveyed globally and 29% in the Asia-Pacific do not have dedicated data analytics departments and rely on the analytics expertise of individual employees. Forward-looking employers are paving the way for their employees to leverage advanced data self-service by creating a culture that allows them to adapt, grow and transform into citizen data scientists.