As AI continues to simplify and automate many fundamental data science tasks, the field may face a reckoning over what it really means to be a data scientist in the age of intelligent automation.
About ten years ago, data scientists were proclaimed “the sexiest job of the 21st century.” wrote Tom Davenport and DJ Patil in Harvard Business Review. In high demand at the time, data scientists’ mission is to make “discoveries by swimming in data”. It’s their preferred method of navigating the world around them. Comfortable in the digital world, they are capable of structuring large quantities of informal data and making their analysis possible.
But will artificial intelligence make the role of data scientists obsolete? This is the view of Dominic Ligot, CEO and CTO of CirroLytix, reported in a recent HackerNoon. article that it was able to quickly familiarize executive-level course participants with data science techniques, without formal data science training or skills.
“The participants, mainly CISOs who generally do not code, found the exercises, designed with the help of AI, intuitive and practical,” says Ligot. “My goal was to immerse them in working directly with data and code. They particularly appreciated the opportunity to manually explore what modern cyberthreat monitoring and SIEM platforms typically automate, gaining insight into the processes happening “under the hood.”
The role of the Data Scientist is evolving
He also made another, even more telling observation: “What I took away from this course was surprisingly counterintuitive: Data science, as we know it, will eventually be replaced by AI,” a- he declared.
AI may be able to replace many of what Ligot describes as “vaguely defined” functions of data science. “Data science essentially combines computer science, statistics and business acumen, offering organizations the promise of actionable insights from large amounts of data,” he explained.
“As AI rapidly advances, it becomes clear that the underlying challenges in this area are harder to ignore. The advent of powerful generative AI could very well be the tipping point for a discipline that, in retrospect, may have been more vaguely and overrated than initially acknowledged.
See also: What has changed for data scientists this decade?
Data science “often turns out to be a patchwork of loosely related tasks that don’t always align perfectly, and many professionals in the field struggle with all the scale and complexity that the discipline demands,” he said. he added. This includes tasks such as data analysis, modeling and insight generation, all of which could be handled by AI.
“Generative AI has become a powerful force in the very areas where data science is weakest,” he said. “Tasks such as data preparation, cleaning, and even basic qualitative analysis – activities that consume a large portion of a data scientist’s time – are now easily automated by AI systems. The worst part is that AI is faster, more accurate, and less prone to human error or fatigue. »
The rise of AI-based data science tools “could force a shift in how we view the role and future of data science itself,” Ligot said. “As AI continues to simplify and automate many fundamental data science tasks, the field may face a reckoning over what it really means to be a data scientist in the age of intelligent automation. “
