People like to work together, even if they don’t always admit it. Just like bees, we enjoy collaboration, especially if this entails some bonding too. This is sometimes depicted with the term Honeybee Effect, which has applications in every endeavor that can accommodate teamwork. However, we tend to ignore that collaboration is sometimes not only preferable but also essential, particularly in more challenging projects, such as digging up insights in large and diverse datasets. So, let’s see how all this idea can yield some honey.
The honeybee effect is all about people working together on a project and doing so in an intelligent manner. This usually brings about a result that is objectively better than the best that any single member of the group would be able to deliver on their own. You don’t have to be in a modern and foxy framework like Agile in order to have the honeybee effect though. In fact, you can observe it anywhere where there is some intelligence involved in people’s collaboration. Interestingly, if you have people of average competence working in a honeybee fashion, you would expect them to outperform a group of very competent people working in a show-off fashion. This is why most successful organizations prefer team players rather than solitary geniuses, to man their work posts.
When it comes to data science, it is easy to see that there are various tasks corresponding to different parts of the data science pipeline. Naturally, these tasks can be undertaken by different people. If these people work together in a way that embodies the honeybee effect, it is quite likely that the team is going to be as good as a super competent data scientist doing everything by herself. The upside of this is that you wouldn’t have to pay some diva data scientist a very high salary and have the fear that he may take off when Google opens up a new data scientist position in one of its campuses. The downside of all this is that getting a group of people to work harmoniously is a very challenging task, even if the people themselves are willing to do so. There is always the need to organize and lead these people, something that most managers of data science teams find quite challenging, even if they are experienced in management. This is probably why there is such a high demand in chief data scientists, team leaders who are adept in the craft themselves. It’s possible of course to be led by a business person who is not trained in data science but such a leadership would be lacking in mentorship and technical aid. The latter can be fixed by involving a consultant in the whole process. As for the former shortcoming, well, the jury is still out on that one…
So, what does it take to cultivate the honeybee effect in a team and individually? Well, communication is the most obvious prerequisite. By communication we mean being able to express yourself adequately and, most importantly, understand what others want to say, without having to spend five hours in a meeting with them. Another factor of the honeybee effect is being aware of your strengths and limitations (or of everyone’s strengths and limitations, if you are the leader of the team). This will allow you to offer and accept help from your teammates. Finally, in order to get the honeybee effect going, you need to be able to own whatever you undertake and handle it professionally. This doesn’t mean that you won’t ask for help if you can’t deal with a particular issue in the data or the model you work with. However, you need to be independent in whatever task you undertake and rely primarily on yourself to get it done.
There are other aspects of the honeybee effect that you need to develop, of course, but these are the most important ones, in my experience. What about you? What factors of the honeybee effect do you observe and how would you incorporate them in your skillset?
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.