So let’s return to Binita, Chao, Dmitri, and Rebecca. Could our new terms be of value when trying to efficiently and coherently speak about these four professionals, their roles, and their careers? As we’ve already described, Data Businesspeople, Creatives, Developers, and Researchers tend to have distinctive skills. What else can we say about them, based on their survey responses? In this section, we summarize the most interesting results.
Data Businesspeople like Binita are those that are most focused on the organization and how data projects yield profit. They were most likely to rate themselves highly as leaders and entrepreneurs, and the most likely to have reported managing an employee (about 80% have). They were also quite likely to have done contract or consulting work, and a substantial proportion have started a business. Although they were the least likely to have an advanced degree among our respondents, with about 60% having a Master’s or beyond, they were the most likely to have an MBA, at nearly 25%. But Data Businesspeople definitely have technical skills and were particularly likely, like Binita, to have undergraduate Engineering degrees. And they work with real data — about 90% report at least occasionally working on gigabyte-scale problems, which far exceeds quarterly financials in a spreadsheet. Also of note, Data Businesspeople skewed a little older in our demographics, and nearly a quarter were female, higher than our other varieties of data scientist. But only about a quarter said they had described themselves as a “data scientist,” much lower than the half of the rest of our respondents who had done so!
As we’ll discuss later, data scientists can often tackle the entire soup-to-nuts analytics process on their own: from extracting data, to integrating and layering it, to performing statistical or other advanced analyses, to creating compelling visualizations and interpretations, to building tools to make the analysis scalable and broadly applicable. We think of Data Creatives like Chao as the broadest of data scientists, those who excel at applying a wide range of tools and technologies to a problem, or creating innovative prototypes at hackathons — the quintessential Jack of All Trades. Our Data Creative respondents latched onto the term Artist like no other group. Similar to Data Researchers, they have substantial academic experience, with about three- quarters having taught classes and presented papers. Common undergraduate degrees were in areas like Economics and Statistics. But unlike Data Researchers, relatively few Data Creatives have a PhD. Respondents like Chao do have substantial business expertise — Data Creatives were actually slightly more likely than Data Businesspeople to have done contract work (80%) or have started a business (40%). As the group most likely to identify as a Hacker, they also had the deepest Open Source experience, with about half contributing to OSS projects and about half working on Open Data projects. We also saw that Data Creatives were somewhat younger and more likely to be male than other respondents. Curiously, they responded most positively to our final question: “Did you feel that this survey applied to you?”
We think of Data Developers like Dmitri as people focused on the technical problem of managing data — how to get it, store it, and learn from it. Our Data Developers tended to rate themselves fairly highly as Scientists, although not as highly as Data Researchers did. This makes sense, particularly for those closely integrated with the Machine Learning and related academic communities. But, like Dmitri, Data Developers are clearly writing code, probably production code, in their day-to-day work. About half have Computer Science or Computer Engineering degrees, and about half have contributed to Open Source projects. More Data Developers land in the Machine Learning/ Big Data skills group than other types of data scientist. They were also least likely to have done consulting work, managed an employee, or contributed to an Open Data project.
One of the interesting career paths that leads to a title like “data scientist” starts with academic research in the physical or social sciences, or in statistics. Many organizations have realized the value of deep academic training in the use of data to understand complex processes, even if their business domains may be quite different from classic scientific fields. People like Rebecca who end up in the Data Researchers Self-ID Group tend to be from these backgrounds. The majority of respondents whose top Skills Group was Statistics ended up in this category, for instance. Nearly 75% of Data Researchers have published in peer-reviewed journals, and over half have a PhD. (Our skewed-by- personal-connections respondents reported a surprising number of psychology and political science degrees.) On the other hand, Data Researchers were least likely to have started a business, and only half have managed an employee.
 Harlan D. Harris, Sean Patrick Murphy, and Marck Vaisman, Analyzing the Analyzers, O’Reilly Media, Inc., 2013.