In today’s world of connected people and things, the new data landscape is the centerpiece of digital change. Big and fast data is analyzed by data scientists. And yes, since being described as the sexiest job of the 21st century, I have seen data scientists become the popular kids on the block.
For good reasons, data science is about providing insights for fact based decisions and to generate more business. Organizations in big data intensive labor markets will continue to experience faster productivity growth than others.
The numbers speak for themselves and illustrate a strong demand for data scientists. Therefore, I believe organizations should not wait for a next generation of data scientists or leave the decision of hiring expensive employees (read data scientists) to start ups and businesses that need to be at the forefront of reaping benefits from data.
Looking at the United States it shows a shortage of 140,000 to 190,000 data scientists for 2018, plus an additional demand for 1.5 million managers and analysts is projected by the McKinsey Global Institute.
For some of us it might be interesting to look at emerging markets like India where the number of graduates is expected to be higher, taking into account their population of over 1 billion inhabitants.
But let’s face it, no company or even government can fill this gap simply by just changing graduate requirements and waiting for people to graduate with more skills or by importing talent. Although the subject of data is included in many graduate programs, the shortage of talent becomes even more challenging if we take into account that today university programs are not yet tailored to do data science. The lack of graduate programs delivering ‘ready to analyze’ data scientists underpins the importance of looking deeper into the skills of a data scientist.
Data science can be summarized as the interplay of data, statistics, technology and business. So by default, doing data science is about collaboration, teamwork and combining different skill sets. It does include but is not limited to statistics and mathematics. It would also include skills like computer science, machine learning, industry expertise (banking, insurance, retail etc.) and expertise on functional domains like sales, customer service or marketing, communication and presentation skills and last but not least, data visualization.
A wide set of skills will not necessarily make it easier for organizations to find and recruit data scientists in the war on talent that has already started. My colleague Bhima Auro recently wrote a blog on how organizations can hire talent.
I think organizations should consider their immediate needs for data scientists and apply creativity in attracting talent on the short term. They should look beyond just hiring a couple of individuals with hard core skills in statistics and mathematics, to behavioral and team playing skills. Partnering long term with external suppliers is an additional solution. Both will help organizations to build high performing teams that collectively have the required skills to support your strategic objectives in terms of revenue, growth and profit.
(About the author: Ruurd Dam is senior vice president and global practice leader at Capgemini focused on "Customer Value Analytics." This post originally appeared on his Capgemini blog, which can be viewed here)
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