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Data scientist shortage : everyone needs data science…
With the volumes of data generated globally due to the advent of cloud innovation and technologies, there has been an upsurge in the need for in-house employees that can make sense of data for increased business growth and development.
Businesses, big and small, have woken up to the immense possibilities present in taking advantage of these vast data to gain insight and make profitable business decisions.
…but there is a data scientist shortage
Despite the enormous data scientist job ads that go out frequently, it is unfortunate that the demand-and-supply ratio of talent acquisition has remained unbalanced.
Here are some stats for your consideration:
Data Scientist is the most fantastic job in 2019 (LinkedIn)
By 2020, openings for data scientists will rise from 364,000 to 2,720,000 (IBM)
There’s a 35% difficulty in recruiting for data science positions, resulting in a 250,000 job shortfall (IBM)
Perhaps this situation favors the handful of data science professionals positioned to quote chart-topping compensations to prospective employers.
But the same cannot be said of employers and business owners who can not compete competitively for the retention of these talents and thus miss out on the impressive ROI that comes with such talent investment.
Small and medium businesses are in trouble.
Yes, SMEs understand all the benefits of AI / ML / Data Science, but they don’t have the expertise or the funds to do it. They stay in the dark, their data is not talking to them.
Closing Up the Shortage Gap
Several creative ways have been suggested as a workaround to the shortage of data science professionals.
Here are some of them.
● Engaging Professionals who are open to a career switch
With all the attention that data science has gathered, due primarily to the thousands of dollars that data science talents are compensated, it is an understatement that many tech and non-tech professionals are enthusiastic about switching to the field.
Several tech consulting firms train enthusiasts with data analytics background in data science business for onward recommendation to employers.
Such training takes a hell of a time and does not immediately address the shortage.
● Retraining Current Employees
Some organizations searching for data scientists take the search initiative inward by screening through their technical skill workforce for suitable candidates that can be trained for the job.
The challenge in this is the risk of such full-blossomed talents taking a hike for a better opportunity.
● Broadening the Search Scope
As you may already know, there’s an unbelievable disparity in the supply of data science talents based on geography.
We sure can’t compare the supply of data scientists in the developed world to other parts of the world.
But with remote and flexible work arrangements becoming the future of work, there is an increased probability that candidate pools from which corporates find suitable talent will become widened.
However, concerns about data security and protection that’s globally making rounds might make it problematic to remotely engage talents across continents due to data migration’s inevitability.
But what about no-code machine learning as a solution?
What if there is a better, viable, and cheaper solution for addressing this talent shortage?
Why should advanced data analysis always have to be strictly handled by data scientists?
In Graphite, our thoughts and activities are ongoing in developing AI-enabled tools that help businesses sense and leverage data in their operations without specifically-skilled data science professionals.
With our SaaS tool, businesses of varying types and sizes can assign average data science tasks to virtually anyone in the organization — powered by Graphite’s AI automation and Machine Learning ready-to-go templates.
Moreso, by design, Graphite promotes data literacy through the data storytelling capabilities that simplify and ease up data understanding and explain insights.
With our unique “dashboards go vertical” approach, Graphite allows you to explain your data, your process, and insights beautifully and simply, ensuring adequate data democracy, transparency, and widespread usage.
