Businesses are teeming with data. As a result, business leaders are building up their data science capabilities to help make sense of their data. Our research showed that an effective approach to optimizing the value of that data is to build data science teams rather than relying on a single data scientist. In today’s post, I want to explore the size of data science teams and its impact on work outcomes.
In our study of data science, we asked over 500 data professionals about their team size (i.e., When you work on projects that involve analytics, how many people generally work with you?) and their level of satisfaction with their work outcome on analytics projects (i.e., Overall, how satisfied are you with the outcomes of the analytics projects on which you work?). We also gathered information about company size.
We found that the overall size of the company (i.e., number of employees) was not related to the number of people who work on analytics projects. In general, companies tend to have about 3.5 data professionals who work on analytics projects (see Figure 1).
Also, size of the company was not related to satisfaction with work outcome of analytics projects. On average, satisfaction ratings of work outcomes is about 7.3 (see Figure 1).
Next, we looked at the relationship between team size and satisfaction with work outcomes. Results showed that, in general, data professionals are more satisfied with work outcomes of analytics projects when they work with other people on these projects. Generally, the more people who work on the project, the better the outcome (see Figure 2).
Data scientists tend to partner with about 2 to 3 of their peers when they work on analytics projects. Additionally, data scientists tend to be satisfied with the outcome of their work (about 7 on a 0 to 10 scale). Company size was not related to data science team size or data scientists’ satisfaction with the outcome of their work.
Finally, data scientists who work with many of their peers report being more satisfied with their work outcomes compared to data scientists who work with fewer of their peers (or those who work alone). A team approach to the practice of data science is the optimal way toward a successful data science program.