There’s a lot of money to be made in data science, as a new O’Reilly report indicates. How much money? Over the last three years, “About half of [those surveyed] reported a 20% change [in salary], and the salary of 12% of the sample doubled.” With a median salary of $106,000 for US data professionals, those are significant jumps.
Yet, there’s also a lot of unemployment. At least, for those people who data science helps to put out of a job. What Patrick McKenzie wrote of engineers is equally true of data scientists: “You’re in the business of unemploying people.” Data, done right, makes systems more efficient and, inevitably, “efficiency gains” generally translate into “somebody will lose their job to a machine.”
But not all jobs. Gartner’s Svetlana Sicular described the importance of people and machines collaborating on data, something that the industry is only just now settling into accepting.
In the early, hype-prone days of big data, breathless reports abounded of machines replacing humans as data analytics uncovered “actionable insights” that humans would then execute. Reality, however, has been very different.
It turns out that machines aren’t very good at interpreting data. As biased as we people are (and we are, both in the data we choose to collect and the questions we ask of it), and as flawed as our analysis can be, people remain essential to understanding data. The key is to figure out the right balance between human and machine, as Sicular highlighted:
This smacks of common sense, because it is.