There are not enough data scientists available to fill demand so managers and employees are thinking of creative ways to bridge the gap. This is the case in healthcare scenarios, but it is also true in manufacturing and engineering. Ramya Sriram reports
Demand for data scientists is higher than ever before and IBM predicts it will increase by 28% by 2020. With the industry in the midst of a skills shortage, businesses are struggling to fill the gap.
The incessancy of big data shows no sign of slowing down. So much so, that if businesses want to glean actionable insights from their data, they must now turn to specialised predictive analytics.
Machine learning and artificial intelligence (AI) are also helping companies to make informed decisions and to predict customer behaviour.
However, because there are not enough data scientists available to fill demand, companies are having to bridge the gap themselves.
The lack of data scientists is not the only issue holding businesses back. It is expensive to hire a full-time data scientist, which means start-ups and small and medium sized enterprises (SMEs) often cannot afford to hire highly trained data scientists to join their in-house teams.
This is a particular problem if the company doesn’t need a full-time data scientist but requires help with a one-off project writing an algorithm, building a recommendation engine or designing a predictive model.
The rise of the citizen data scientist
Gartner coined the term citizen data scientist to describe a person who generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside the field of statistics and analytics.
Employees can be upskilled and trained to do data analytics, using only a small amount of business resources, to do some of the tasks previously only done by a data scientist, statistician or mathematician.
Citizen data scientists rely on visualisation and other automated tools, such as DataRobot and Google Cloud AutoML, to make it easier for them to write algorithms and build models.
While they do not have all the specialized skills of a traditional data scientist, citizen data scientists bring their own, industry-specific knowledge to the analysis.
Whether the employee works in sales, marketing, finance, human resources or research, they will have a detailed understanding of the challenges facing their department and their industry. This domain expertise will come in useful when deriving insights in their field.
Does this mean the end of the traditional data scientist?
Citizen data scientists do not replace the traditional data scientist, merely complement them.
Companies will still require highly qualified data scientists, particularly for complex and specialized analysis of data or for a large amount of data that requires specific tools and accuracy.
By working together, citizen data scientists and traditional data scientists can strengthen insights and ensure accuracy.
So, what can businesses do if they need to hire a data scientist, but don’t have the resources to hire one full time? Thankfully, there is another option.
Companies can hire a freelance data scientist, to access specialised data science skills on-demand. A freelance data scientist can help a company with a one-off project by giving them access to the required skills independent of geographical location.
Citizen data scientists present a good solution for companies building models and using predictive analytics, because they reduce the strain on data scientists and minimise resource use.
However, businesses still require the specialist skills of traditional data scientists. To access specialist skills, consider hiring a freelance data scientist, who can help with your project for a manageable, affordable cost.
To find the perfect, highly qualified data scientist for your project, post your project on Kolabtree.
Ramya Sriram is digital content manager of online platform for freelance scientists Kolabtree.