Despite the vast literature around Big Data, the “Data Agents of Change” are still a mysterious bunch. They are called “Data Scientists”, but who are they really?
We don’t know what the exact job description for a Data Scientist is or how different they are from their ancestors, the Data Analysts. In fact, when I asked Merv Adrian who now works for Gartner, what a Data Scientist was, he answered: “a Data Scientist is a Data Analyst who lives in San Francisco.” Merv might have meant it as a joke – but he might not be too far off.
Despite the vast literature around Big Data, the “Data Agents of Change” are still a mysterious bunch. They are called “Data Scientists”, but who are they really?
We don’t know what the exact job description for a Data Scientist is or how different they are from their ancestors, the Data Analysts. In fact, when I asked Merv Adrian who now works for Gartner, what a Data Scientist was, he answered: “a Data Scientist is a Data Analyst who lives in San Francisco.” Merv might have meant it as a joke – but he might not be too far off.
As research indicates, the distinction between a Data Analyst and Data Scientist is not clear. Both are highly educated (85% have college education) and their salaries are similarly correlated to location and experience.
Another problem with Data Scientists is how needed they are: according to McKinsey, the US will need close to 200,000 analytical experts & 1.5 million more data-literate managers by 2015 to take advantage of “Big Data”.
Where will we find them? There are lots of options such as upleveling your Data Analysts or training passionate and statistically astute employees.
The unexpected gender equality phenomenon in the Data World could be an opportunity: according to the survey, compensation for women is slightly higher than for men in data science and related fields. To put that in perspective, Census Bureau estimates put the real wage gender gap somewhere at the 5-7% range, meaning that even after controlling for education level, time spent on maternity leave, occupation, and other factors, women tend to earn about 5% less than men (and the gap trends wider, not narrower, as workers age.)
The survey group was comprised of about 15% women and 85% men, which suggests that, compared to the population as a whole, men are overrepresented among the data sample still. Yet, the insight is worth considering, because it appears that women in data science have the opportunity to close the wage gap in a hurry. And even if by some data bias the true income figures for the industry aren’t quite as rosy, we love the idea that more women get into the tech profession through Data – it’s an awesome industry to be in.
