Data Science: Short on Skills or Short on Problems?
In a recent interview Talent Analytics CEO Greta Roberts suggested that the data scientist shortage in HR analytics is a myth.
Rather than a shortage of analytics talent, she instead sees a lack of clear, business-relevant projects justifying mass data science hirings. As an example, she cites one large firm that went on a hiring binge of 30 data scientists. They were all fired a year later when it was realized those hires brought little value to the business. The problem? They didn’t have any problems to work on.
While I might quibble with the view that the world is already packed with quality data science practitioners, her core point is critical and spot on: data scientists are only useful when solving real problems. Organizations would do well to have a few compelling projects in the queue and some relevant data on hand when considering their next analytics hires.
But the onus is not just on those doing the hiring. Analytics experts themselves have a responsibility to cleanly communicate the kinds of questions they can answer and the kinds of problems they can help solve. They need to explain, to demonstrate, and to guide the process of project development, not sit back and wait for someone else to do the truly hard work of asking the right question.
Those running a business are well attuned to their own problems but they won’t necessarily know about the ever-growing data scientist toolkit. Nor should they. After all, they are busy creating value and maintaining a business profitable enough to provide career opportunities to this new wave of analysts.
Much of what leaders and decision makers know and expect from data science comes from frothy mass media stories about the power and promise of big data, analytics, and all that comes with it. That needs to change…but that change starts with the practitioners already in the building (plentiful or not).
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