The insights gleaned from our data respondents point to a nascent, fast-growing industry that is still very much in the early stages of its development. Here are some of the major findings from BrainStation's 2019 Digital Skills Survey™.
Demand for Data Scientists is expected to rise by 28 percent in the next two years, with a projected 2.7 million new data jobs. Clearly, a large number of professionals will have to transition into data roles in that time, and our findings suggest that this is already well underway.
79 percent of respondents did not begin their career in data, and 65 percent have been working for five years or less.
A majority of data professionals are also on small teams, with 38 percent on teams of less than five, and 71 percent on teams of 10 or fewer.
While the aforementioned demand for "Data Scientists" is growing, most data respondents work under a different title, with Business Analyst, Data Analyst, and Computer Scientist leading the diverse list of job titles.
Somewhat surprisingly, 61 percent of respondents said they worked with Text data, with Numerical data coming in at 73 percent. Numerical is traditionally considered to be "data," but these findings illustrate the rising importance of Text and Image data, which includes responses to an online form, social media posts, large bodies of text, and more.
52 percent of data respondents rated the data literacy in their organization as "Basic." In fact, responses for this answer were more than the other three categories (Intermediate, Advanced, and Expert) combined, which may speak to the need for foundational data education and skills training across organizations.
When it came to what technology data professionals were using, Excel emerged as the most widely used tool, at close to 81 percent. This was followed by SQL, Python, and Tableau.
Excel's presence at the top of the list was somewhat surprising, so we dug a bit deeper to see how these responses broke down by role.
We looked at the five major categories of respondent roles (Data Scientist, Data Analyst, Researcher, Business Analyst, Data Analytics Manager) to see the distribution of tools they used. What we found was that Data Scientists had less of a reliance on Excel. They were also the only category to cite Python as their most frequently used tool. Interestingly, their responses also included a much wider range of tools.
One potential explanation for this may be that the Data Scientist title implies additional experience and training, which would include exposure to a programming language like Python, as well as additional relevant technology.
Most respondents also said that Data Wrangling and Cleanup took up the bulk of their time on the job. The primary use of data, meanwhile, was devoted mostly to the optimization of existing platforms and products, as well as the development of new ideas, products, and services.
Breaking these responses down by the five major roles again, we see that Business Analysts and Data Analysts tend to focus more on optimizing existing solutions, whereas Data Scientists and Researchers primarily work on developing new solutions.
Linear regression was the most frequently cited machine learning technique at 54 percent, although a good portion of data respondents also said they did not use machine learning. Interestingly, after linear regression, the most frequently cited machine learning techniques were some of the simplest, which may indicate how useful a foundational knowledge of data science can be.
When it came to professional development, data respondents seemed to prefer a more personal touch to education. 72 percent had participated in online courses; 68 percent in workshops, seminars, or conferences; and 63 percent had taken in-person courses. Considering that a large portion of data professionals are transitioning from different careers, this isn't that surprising, as it indicates a desire for collaboration, feedback, and a more hands-on learning experience.
There is also an indication of this preference for interpersonal learning in data respondents most frequently cited resources, as Online Forums, Colleagues, and Blogs came in as the top three.
The vast majority of data respondents, 77 percent, say they don't work with artificial intelligence (AI), which flies in the face of what many might expect given the press attention AI has received over the last few years. It is possible, however, that data respondents, being more intimately familiar with AI, are more hesitant to throw around the term than Marketers or Executives.
They did, however, feel that Machine Learning and AI would have the most impact on the next five to 10 years, with blockchain and internet-of-things technology coming in third and fourth, respectively.