How do the very-in-demand data scientists differ from analysts?
“The recent emergence of the digital enterprise has created a seemingly insatiable management appetite to amass and analyze data,” write Jeanne G. Harris and Vijay Mehrotra, in the Fall 2014 issue of MIT Sloan Management Review.
Companies are hungry for data scientists to make sense of the information they’ve compiled, putting these particular analysts in high demand. “Today’s data scientists are often singled out as a breed apart — and for good reason,” argue Harris and Mehrotra. “They tend to be better programmers than most statisticians and better statisticians than most programmers.”
Harris and Mehrotra surveyed more than 300 analytics professionals in the U.S. to find out what differentiates data scientists from other quantitative analysts. In their article “Getting Value From Your Data Scientists,” they detail some of the distinctions that emerged.
Among the differences they found between data scientists and analysts:
Types of data:
Analysts: Structured and semistructured, mostly numeric data
Data Scientists: All types, including unstructured, numeric and nonnumeric data (such as images, sound, text)
Analysts: Statistical and modeling tools, usually contained in a data repository
Data Scientists: Mathematical languages (such as R and Python), machine learning, natural language processing and open-source tools that access and manipulate data on multiple servers (such as Hadoop)
Nature of work:
Analysts: Report, predict, prescribe and optimize
Data Scientists: Explore, discover, investigate and visualize
Typical educational background:
Analysts: Operations research, statistics, applied mathematics, predictive analytics
Data Scientists: Computer science, data science, symbolic systems, cognitive science
Analysts: Percentage who say they: are entrepreneurial: 69%; explore new ideas: 58%; gain insights outside of formal projects: 54%
Data Scientists: Percentage who say they: are entrepreneurial: 96%; explore new ideas: 85%; gain insights outside of formal projects: 89%
As Harris and Mehrotra note, “Data scientists were much more apt than analysts to say their projects often or always address new problems (85% to 58%) and to say that they find surprising and valuable business insights outside of formal projects (89% to 54%).”
The research also explored the challenges of managing data scientists. A common complaint is that data scientists “don’t see a need to explain or talk about the implications of their insights, which makes it difficult for them to partner effectively with professionals whose business expertise lies outside of the technical realm.”
For more on Harris and Mehrotra’s research, including their seven recommendations for how to manage data scientists for maximum business value, read the full article. And for thoughts about how companies can automate the data scientist function, read Michael Fitzgerald's recent blog post "Data Scientist In a Can?."