A decade ago, Thomas H. Davenport (@tdav), a distinguished professor at Babson College, and DJ Patil (@dpatil), a former U.S. Chief Data Scientist, insisted that data scientists occupied the sexiest jobs of the 21st century because they are “the people who can coax treasure out of messy, unstructured data.”[1] The term “data scientist” was coined by Patil and his colleague Jeff Hammerbacher back in 2008 and they applied it to “high-ranking professionals with the training and curiosity to make discoveries in the world of big data.” Davenport and Patil insisted data scientists were “sexy” because they could tease insights out of data. They explain, “More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stops flowing, data scientists help decision-makers shift from ad hoc analysis to an ongoing conversation with data.”

Enter the Skeptics

A decade on, skeptics are questioning whether data scientists still deserve holding title of “sexiest job on Earth.” Journalist Nick Kolakowski (@nkolakowski) reports that Davenport and Patil haven’t taken the push-back on their assertion lying down. He writes, “Davenport and Patil recently took to the pages of Harvard Business Review to examine their old article and whether its central premise — that data science is one of the world’s fastest-growing and buzzed-about professions — remains accurate. For anyone who’s spent a lot of time training to be a data scientist, their answer should assure you: data science is still a very sexy job.”[2] Tech writer Veda Aravali isn’t so sure. She asks, “Is data science still the sexiest job in 2022?”[3] Her answer, “The world doubts it.” Nevertheless, she provides no evidence as to why she thinks that way. For example, she reports, “The data science job is more in demand than ever with employers and recruiters. AI is increasingly becoming popular in business, and companies of all sizes and from all locations feel they need data scientists to develop AI models. According to the US Bureau of Labor Statistics, the number of jobs requiring data science skills is expected to grow by 27.9 percent by 2026. There is no automated tool that can replace the skillset of a data scientist, as long as you continuously learn and create data-driven solutions.”

Hidden in her comments is the kernel of truth that may take the shine off of the data scientist’s sexiest job trophy — the rise of artificial intelligence (AI). In their latest article, Davenport and Patil note, “The job is more in demand than ever with employers and recruiters. AI is increasingly popular in business, and companies of all sizes and locations feel they need data scientists to develop AI models.”[4] They also admit that as advanced analytics have become essential to modern businesses, the number of jobs relating to big data have proliferated. “Part of the proliferation,” they write, “is due to the fact that no single job incumbent can possess all the skills needed to successfully deploy a complex AI or analytics system.” In other words, there are other sexy jobs now competing with data scientists.

Thanks to advances in artificial intelligence, data scientist Mikhail Mew predicts, “Data scientists will be extinct in 10 years.”[1] And, in his mind, that’s not a bad thing. He explains, “As advances in AI continue to progress in leaps and bounds, accessibility to data science at a base level has become increasingly democratized.” Davenport and Patil agree that AI is democratizing access to big data analytics. On the other hand, they note, companies still rely “on data scientists to ensure that citizen-developed models are accurate and that all relevant data is employed.” They add, “Professional data scientists themselves will focus on algorithmic innovation, but will also need to be responsible for ensuring that amateurs don’t get in over their heads. Most importantly, data scientists must contribute towards appropriate collection of data, responsible analysis, fully-deployed models, and successful business outcomes.”

Concluding Thoughts

Mew makes an interesting observation about data scientists in general. He writes, “The broad application of data science proves to be a double-edged sword. On one side, it is a powerful toolbox that can be applied to any industry where data is generated and captured. On the other, the general applicability of these tools means that rarely will the user have true domain knowledge of said industries before the fact. Nevertheless, the problem was insignificant during the rise of data science as employers rushed to harness this nascent technology without fully understanding what it was and how it could be fully integrated into their company. However, nearly a decade later, both businesses and the environment they operate in have evolved.” The traditional approach to analytics has been to assemble a team of three experts:

• A business domain expert — the customer of the analysis who can help explain the drivers behind data anomalies and outliers.

• A statistical expert — to help formulate the correct statistical studies, the business expert knows what they want to study, and what terms to use to help formulate the data in a way that will detect the desired phenomena.

• A data expert — the data expert understands where and how to pull the data from across multiple databases or data feeds.

Having three experts involved dramatically lengthens the time required to analyze, tune, re-analyze, and interpret the results. Cognitive technologies, like the Enterra Autonomous Decision Science™ platform, empower the business expert by automating the statistical expert’s and data expert’s knowledge and functions, so the ideation cycle can be dramatically shortened and more insights can be auto-generated. Although this may sound like cognitive technologies are taking over all high-skilled jobs, the fact is they augment rather than replace the human workforce. They help keep the data scientist relevant. Mew concludes, “Going forward, the skill set collectively known as data science will be borne by a new generation of data savvy business specialists and subject matter experts who are able to imbue analysis with their deep domain knowledge, irrespective of whether they can code or not.” This will be possible because cognitive technologies will help them do the heavy lifting.

Footnotes
[1] Thomas H. Davenport and DJ Patil, “Data Scientist: The Sexiest Job of the 21st Century,” Harvard Business Review, October 2012.
[2] Nick Kolakowski, “Data Scientist: Still the ‘Sexiest’ Job of the Century?” Dice, 12 August 2022.
[3] Veda Aravali, “Is Data Science Still the Sexiest Job in 2022? The World Doubts It.” Analytics Insight, 2 August 2022.
[4] Thomas H. Davenport and DJ Patil, “Is Data Scientist Still the Sexiest Job of the 21st Century?” Harvard Business Review, 15 July 2022.
[5] Mikhail Mew, “Data Scientists Will be Extinct in 10 Years,” Towards Data Science, 10 May 2021.