Data science entered the American business lexicon in October 2012, after theHarvard Business Review ran a feature story, “Data Scientist: The Sexiest Job of the 21st Century.” Written by Thomas H. Davenport (Harvard Business School professor and Delloite analytics advisor) and D.J. Patil (data scientist at Greylock Partners and former head of data products at LinkedIn), the article opened a lot of eyes to the possibilities of this emerging field.
Patil made a name for himself at LinkedIn. In the article, he recounts the brilliant work of a colleague there, big data specialist Jonathan Goldman. Goldman’s data insights helped make the career networking site the powerhouse it is today. Using data patterns and predictive modeling, Goldman was able to greatly expand user career networks, and thus page views, with the introduction of the “people you may know” feature. Around this same time, Jeff Hammerbacher, a Stanford PhD, was performing similar wizardry for Facebook. Together, Hammerbacher and Patil coined the phrase “data science.”
Complex Data Calls for Beautiful Minds
Data scientists quickly became known as key players in an organization – must-have hires. They are high-ranking professionals equipped with the advanced training and knowledge needed to harness the oceans of data large corporations are warehousing. Prior to the data explosion fueled by high technology, cloud computing, and globalization, data analysts were able to work with terabytes worth of rows of numbers stored on servers. But now that petabytes of varied data are warehoused in massive clouds, a more highly trained expert is needed.
To fill this role, companies are seeking hybrid geniuses who have expertise in computing, engineering, mathematics, statistics, data analysis, and business. A very select group of individuals can boast such a resume. However, that doesn’t quell business demands.
Greylock Partners, a venture firm that backed Facebook and LinkedIn, is concerned enough about the dearth of data scientists that it has devised its own recruiting team to channel talent to companies in its portfolio. “Once they have data,” says Grelock’s Dan Portillo, “they really need people who can manage it and find insights in it.”
The Harvard Business Review article equates today’s data scientists to Wall Street “quants” of 30 years ago. In the 1980s, investment banks were gobbling up hires with backgrounds in physics and math to devise new algorithms and data strategies. Demand led to universities developing master’s programs in financial engineering, which churned out a second generation of talent. This pattern seems to be repeating itself with data science.