Rarely is there a discussion of automation that doesn’t touch on whether the technology will reduce or even eliminate the need for human labor in certain positions. In the data science field, a Gartner prediction that 40 percent of data science tasks will be automated by the end of this year has some automation detractors arguing that demand for talent will decrease.
But, according to a recent article by Paul Mah, these fears are unfounded. He outlines a number of reasons why automation is unlikely to signal the death knell for data science roles. Among them:
Automation is simply a way to do things faster
Automation allows data scientists to offload tedious, repetitive tasks and devote more time to interesting work and innovation efforts. According to Mah, “…much like how the proliferation of digital has changed what office workers do, more capable data science tools will inevitably change how data scientists work. Not only will automation empower data scientists to do more, the impact and value of their work to the business organization will also increase.” In this environment, data scientists will be more valuable than ever, assuming that they are able to keep pace with the changing technology landscape.
Overcoming automated errors
Automation solutions are only as good as the technology that powers them, and errors frequently occur due to poor underlying modeling. This reality underscores why data scientists still have a prominent role to play as automation matures. As Mah puts it, “Another reason why humans in the loop are not going away soon would be the inability of automated tools to realize that they might be going off tangent…In a nutshell, data scientists are required to verify the correctness of the results coming out of automated tools and make sure that the models are operating optimally.”
The role of human judgement
In a similar vein, automated solutions lack the ability to understand the business problem. This is a critical limitation, as challenges are not always technical and rather require a knowledge of the broader context in order to interpret the results correctly. Mah writes, “This means that data scientists play an indispensable role when it comes to formulating various assumptions: from the proxy variables they need, a realistic time frame for analyses, as well as defining appropriate control groups for accurate comparison. This requires human judgement, which is something no automated tool can offer.”
While the industry will certainly evolve as automation increases, there will always be a need for data scientists to provide the expertise, decision-making, and interpretation skills of which the technology is incapable.