Ten years ago, Thomas H. Davenport and DJ Patil published an HBR article proclaiming data science as the “Sexiest Job of the 21st Century.” They defined the role as “a high-ranking professional with the training and curiosity to make discoveries in the world of big data.” They underscored that businesses could reap significant advantages by deploying data scientists to make sense of enterprise information. 

At the time, no university programs offered data science degrees. In addition, there was a general lack of consensus on how the role integrated with other functions, where data scientists could add the most value, and how their performance should best be measured. Despite the relatively novel nature of the profession, the authors already predicted that a shortage of trained data scientists would become an enterprise constraint—a challenge that companies continue to grapple with today. 

So, what other variables remain the same, and what has changed over the last decade? To answer that question, Davenport and Patil recently authored a new HBR piece confirming that data scientists are more in-demand than ever. Among the fundamental changes to the profession are: 

Data Science Has Become More Institutionalized 

As the authors put it, data science was a relatively nascent function in 2012—bearing little resemblance to our current environment in which it is well-established across industries. One reason behind this change is the rise of data science degree programs along with courses and certificate offerings designed to upskill workers or train employees from different areas of the business on data science best practices. 

A Redefined Scope 

A decade ago, data scientists were typically tasked with all the responsibilities of a data science application, ranging from conceptualizing the use case to interfacing with key stakeholders to developing the algorithm and deploying it into production. Today, numerous other roles can assume many of these tasks. For example, machine learning engineers, data engineers, and AI specialists. 

This is undoubtedly a positive change, as no single role can possess all the requisite skills to deploy a complex AI or analytics system successfully. However, Davenport and Patil stress that organizations should develop classification structures for the various data science related-careers and skills to optimize resources and ensure effective collaboration. 

Technology Changes 

Finally, the HBR piece points to trends such as cloud-based processing and data storage, AutoML, and the citizen data scientist movement as factors that have contributed to the evolution of the data scientist role.

Head over to HBR to read the piece and determine whether data science is still the century’s sexiest job.