Industry Differences in Data Science Roles, Skills and Project Outcomes

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The AnalyticsWeek and Business Over Broadway Data Science Survey survey revealed industry differences in data science roles, proficiency in data science skills and satisfaction with work outcomes. The Information Technology industry is home to most data scientists. Data scientists who are Researchers outnumber the other types of data scientists in six of the top 10 industries that employ data scientists. Data scientists in the Professional Services industry, compared to data scientists in other industries, are highly proficient in all three data science skills: Business, Technology and Math/Statistics skills.

Figure 1. Data Scientists Work in Many Industries. Click image to enlarge.

Data scientists work in many different industries. In our survey of data professionals, we asked over 1000 data professionals about themselves and what they do. We captured their proficiency across three broad data science skills: Business, Technology and Math/Statistics. We also asked about the roles that these data scientists play: Business management, Developer, Creative and Researcher. Finally, we asked them about their satisfaction with the outcomes of projects on which they work.

Results revealed that many of the data scientists were from the Information Technology industry (26%) (see Figure 1). The next most popular industries were the Education/Science (14%), Consulting (13%), Financial Services (11%) and Healthcare & Medicine (9%) sectors.

Data Science Roles Across Industries

Figure 2. Differences in Data Science Roles Across Industries. Click image to enlarge

Next, we looked at the different roles that the data scientists hold in their company. Because of sample size constraints, we reported results for only 10 of the industries (14 industries have sample size of less than 20).

As you can see in Figure 2, Researchers dominated the Education / Science (83%), Advertising / Media / Entertainment (69%), Financial Services (65%), Healthcare & Medical (61%), Consulting (61%) and Government (59%) industries. Not surprisingly, Developers were the most common type of data scientist in the Information Technology industry (57%). Business data professionals were the most common type of data scientist in the Retain / Consumer Products industry (69%). The Creative data professionals were the most common type of data scientist in the Professional Services (58%) and Communications (57%) industries.

Proficiency in Data Science Skills Across Industries

Figure 3. Differences in Data Science Skills Across Industries. Click image to enlarge.

Next, I looked at proficiency in skills across the different industries (see Figure 3). In general, the pattern of proficiency in skills tend to be the same across industries. That is, in 9 of the 10 industries, data scientists possessed greater proficiency in Business and Math/Statistics skills than in Technology skills (except for Education/Science Industry).

Additionally, there were statistically significant differences for the three data science skills across industries. Data scientists in the Professional Services industry, compared to other industries, possessed some of the highest levels of proficiency in all three data science skills.

Data scientists in the Education/Science industry reported high proficiency levels in Math/Statistics skills (64) and the lowest levels of proficiency in Business skills (44).

Data scientists in the Education/Science and Healthcare & Medical industries reported the lowest levels of proficiency in Technology Skills.

Satisfaction with Outcomes of Analytics Projects Across Industries

Figure 4. Satisfaction with Outcomes of Analytics Projects Across Industries.

There was a statistically significant difference in satisfaction with outcomes (see Figure 4). Data scientists who work in the Education/Science, Consulting and Financial Services industries reported the highest level of satisfaction with project outcomes. On the other hand, data scientists who work in the Government, Advertising / Media / Entertainment and Communication sectors reported the lowest level of satisfaction with project outcomes.

Summary

The study results showed that industries differ with respect to: 1) the job role profiles of data scientists, 2) data scientists’ proficiency in data science skills and 3) satisfaction with outcome of analytics projects.

Survey results showed that nearly 75% of the data scientists come from five industries: Information Technology (26%), Education / Science (14%), Consulting (13%), Financial Services (11%) and Healthcare & Medical (9%) industries. Also, industries differed with respect to the types of data scientists they hire, the proficiency of their data scientists and the quality of outcomes of their analytics projects.

The frequency of data science roles varied considerably across industries. Researchers were the most popular type of data scientist in 6 of the 10 industries reported here. The other three data science roles were the most popular roles in the remaining industries. These different industry profiles could simply be a reflection of the amount and type of work that each industry requires of its data scientists. For example, Information Technology was the only industry in which Developers were the most common type of data scientist. Some industries, on the other hand, are driven less by technological innovation and more by insight generation (e.g., Education/Science, Healthcare & Medical) and creative problem-solving (e.g., Professional Services, Communications) in which other data science roles come to the fore including Researchers and Creatives, respectively.

Industries differed with respect to the proficiency in skills held by data scientists. Education/Science and Professional Services industries were the only industries in which data scientists possessed adequate Math/Statistics proficiency to do the work (proficiency of 60 or above). Communications industry was the only industry in which data scientists possessed adequate proficiency in Business skills.

Finally, there were industry differences in satisfaction with outcomes of analytics projects. In trying to understand the reasons behind these differences, I looked at types of data scientists in each industry and their education level. Our prior research found that these variables were related to satisfaction with outcomes of analytics projects; researchers reported significantly higher levels of satisfaction compared to Developers and Business data scientists. For the current study, the four industries with the highest satisfaction ratings are those in which at least 50% of the respondents are Researchers. However, two of the three industries that have the lowest satisfaction also have a high percentage of data scientists who are Researchers. Next, looking at education level, we previously found that data scientists with advanced degrees (i.e., Masters, PhD) were more proficient in many skill areas compared to data scientists who do not hold these degrees (i.e., high-school, Two-year degree, Four-year degree). However, when I examined the educational status of data scientists across industries, there was no clear pattern that high satisfaction industries (Education/Science: 77% have advanced degrees) are composed of more data scientists with advanced degrees compared to low satisfaction industries (Advertising/Media/Entertainment: 71% have advanced degrees). Further research is needed to better understand what is driving these industry differences in satisfaction with work outcomes.

While data scientists work in a variety of industries, many of them come from a handful of industries. Industries can be defined by the profiles of their data scientists. While each industry has data scientists representing all four data science roles, some data science roles are more prominent in some industries compared to others. The study findings suggest that optimizing the value of data science is a function of the company’s industry. A better understanding of industry differences in data science methods, practices and outcomes can help companies build teams with the right mix of data scientists for their industry and help recruiters find the best data science candidates for those industries.