Could Algorithms Take Over the Work of Data Scientists?
In a recent post, Olivia Timson asks a compelling question: “Will machine learning make data scientists obsolete?” After being heralded as having the “sexiest job of the 21st century” a couple of years ago, are these rock stars already being put out to pasture?
There are several forces converging that may make some data scientist tasks unnecessary, Timson argues. For starters, thanks to advances in machine learning are producing algorithms that automatically handle the discovery and pattern matching that are part of their jobs.
Plus, many powerful analytics tools and platforms are now available to business users, who can produce their own insights. Data “is also being visualized without human input so that it can be analyzed by business people with no interference.” The move toward visualization, “can reveal intricate structures in data that cannot be absorbed in any other way, and if these can provide business people with what they need to know about the data, it’s hard to know where the data scientist fits in.”
Timson predicts that the areas where data scientists may see more tasks automated or scaled out to users are those tasks that involve enormous amounts of data and real-time, low-level decisions. “The volume of data that companies produce is now far beyond the capabilities of one data scientist to analyze, and it is inevitable that automation will consume the field entirely,” she states.
If you are an aspiring and current data scientist, don’t get too worried, however – organizations going forward will need individuals highly skilled in finding key data nuggets and turning them into stories – as well as deciding what data matters the most to the business.
Machines – no matter how intelligent – cannot innovate and create new businesses. They are but tools for the humans that use them. Data scientists need to have an intimate understanding of the business, and be willing to question previous assumptions.
Even more importantly, to a large degree, data science is a labor of love. As John Weathington, a fata scientist himself, so aptly described it at TechRepublic, “data scientists love to solve problems, write code, build charts and graphs, and work all night with others who love the same things.” For data scientists, he continues, “it’s not just a job, it’s a state of mind — they believe they were born to do their job, and they feel fortunate they can make a good living at it. It would be miserable to work as a data scientist if it’s just a job.”