Much like last year, insights from our data respondents have highlighted the fast-growing nature of the data field, and the extent that it continues to develop.
Demand for Data Scientists is expected to rise 28 percent by next year, with up to 2.7 million available jobs in data. A widening gap exists between demand and professionals with data skills, and our findings suggest that a large number of professionals are already taking advantage of opportunities by transitioning into data roles.
76 percent of respondents did not begin their career in data, and 68 percent have been working for five years or less.
These numbers are relatively unchanged from a year ago, which suggests that we'll continue to see this trend until demand stabilizes (or until more professionals with data skills enter the workforce).
A majority of data professionals are also on small teams, with 37 percent on teams of less than five, and 26 percent on teams of five to 10 people.
The field continues to see a wide range of job titles. The most common is "Data Analyst," which represents 25 percent of respondents, but no other job title, of the 14 provided, garnered more than 10 percent. This is a sign that the data field is diverse, evolving, and filled with many pockets of specializations.
More than half of respondents regularly work with text data (56 percent), making it nearly as common as numerical data (71 percent). Image data is still significant but less common, with 15 percent of respondents working primarily with images. These findings are similar to last year, where we noted that this illustrated the rising importance of text and image data, which includes responses to an online form, social media posts, large bodies of text, and more.
83 percent of respondents ranked the data literacy in their organization as intermediate or low and 89 percent said that this lack of data literacy is impacting the success of their organization's projects. On that point, a further 59 percent said that their organization would be more successful if employees had better data skills. This seems to exemplify the high demand for data professionals, but also hints at an industry still transitioning into a data-centric mindset.
Much like last year, Excel is the most widely used analysis tool, although only 66 percent of respondents are using it the most (compared with 81 percent in 2019). SQL and Python come next on the list with 48 and 45 percent respectively.
In 2019, we dug a bit deeper into Excel's presence at the top of the list to see how the responses broke down by role. We looked at the major categories of respondent roles (Data Scientist, Data Analyst, Business Analyst, Data Analytics Manager) to see the distribution of tools they used.
What we found in 2019 and 2020 was that Data Scientists are using a broader range of tools than the other data roles. They were also the only category to cite Python as their most frequently used tool.
As we noted last year, it may be that Data Scientists have more experience and training, which would include exposure to a programming language like Python, as well as additional relevant technology.
More data professionals are working on the cloud now than aren't. Only 36 percent of respondents said they haven't worked with any cloud computing platform. The most popular cloud platform was Amazon Web Services, which is being used by 43 percent of respondents, followed by Google Cloud at 24 percent.
Spark is the dominant processing framework, with 43 percent of data professionals claiming it is their framework of choice. Hadoop came in second, at 24 percent.
Tableau, meanwhile, is by far the most widely used visualization tool, with 60 percent of respondents citing it. Next up was Matplotlib and ggplot2, Python and R's respective visualization packages.
Research is the activity data respondents are spending the most time with, as 35 percent cite it as taking up the largest chunk of their day, followed closely by data wrangling and cleanup at 31 percent.
The primary use of data, though, is the optimization of existing platforms, products, and systems at 49 percent, followed by the development of new ideas, products, and services (46 percent).
Data professionals are now using a wide variety of statistical and machine learning techniques, with the most popular being linear regression (55 percent have used it). However, it's important to note that 35 percent of respondents haven't used any of these techniques at all, which makes it clear that while machine learning is growing in importance, it is still not a part of the day-to-day for many data professionals.
75 percent of data professionals felt that digital skills training would help them be more effective in their role, with their most in-demand digital skills being additional data training. Clearly, there is a lot to learn in the data world.
When it came to professional development, online video courses are the most popular learning format for data professionals, although they do also like a more personal touch, with 61 percent taking in-person courses and 60 percent focusing on workshops.
A majority of data respondents, 63 percent, say they don't work with artificial intelligence (AI). This, however, is 15 percent less than last year, which would suggest that AI is playing a more prominent role in the lives of data professionals.
Fittingly, 78 percent of data respondents felt that AI would have the most impact on the next five to 10 years, with machine learning (70 percent) and internet-of-things technology (58 percent) coming in second and third, respectively.