Whether you're a fresh graduate or someone who has been working for some time, chances are you might have - sooner or later - heard of the term "team building" and you wondered why you as an individual or the overall team needs this team activity.
Well...You're not alone.
Since I've discussed the my journey into data science and given out some practical guides on how to get into data science...
Let's get back to the real working environment.
In this post, I'll talk about the typical formation of a data science team in a company and why team building is important to data scientists.
If you're an aspiring data scientist or someone who is simply interested in understanding more about how data science team works together with the complement of team building, then this post is for you.
Please take note that the formation of data science teams in every company is different according to each company's requirement. Therefore, the sharing here is by no means exhaustive and is mainly based on my work experience and takeaway.
At the end of this post, I hope you'll see the importance of how a data science team works together as a whole and how team building is able to bring the cohesiveness of a team to the next level with positive results.
Let's get started!
Typical Formation of a Data Science Team
I'm not sure how team building works for other teams in other companies.
But there's one thing for sure, team building is very important to data scientists as they're not a standalone entity who bring projects to fruition.
Yes, going solo may work for Kaggle competitions, but unfortunately, not in the real world. Competing in Kaggle is undoubtedly one of the best ways to learn how to perform data preprocessing, features engineering, models building and optimization from experts around the globe. However, this step is just one of the pieces to solve the whole puzzle in the real world.
It's not surprising that most real-world projects require people with different specialization and expertise (data engineers, data scientists, data analysts, software engineers/developers, project managers etc.) in order to complete projects end to end.
But there's an exception.
Some early startups only hire one data scientist to literally cover all the scopes from building data pipeline for data streaming and collection all the way until bringing models into production.
As time goes by, the startup grows, so does the formation of a data science team (or data team - the team's name doesn't really matter). Therefore, as you may have noticed, a data science team mainly comprises of (combination of any of them):
- Data engineer
- Data scientist
- Data analyst / Business intelligence
- Software engineer/developer
- Project manager/ coordinator
Why Team Building is Important to Data Scientists?
The importance of team building in this post primarily focuses on data scientists' perspective but it's entirely applicable to other job roles to make team building more effective.
1. Get to know each other for better communication
Yes, you're right. The very first objective is to really get to know each other on a deeper level for better communication.
I was very lucky to have participated in 2-days team building activities when I first joined my organization. And you know what? That team building turned out to be the most memorable team building that I've ever had, not to mention the welcoming and friendly team members that I met despite our diversity.
Through the team building, we got to know each other more in depth in terms of background, culture, and experience. With the understanding of each other on a personal level, this would reduce the occurrence of conflicts and therefore lead to a more conducive working environment for learning and sharing.
And this benefit could absolutely be extended to any other teams in a company, which makes team building more effective.
2. Foster team cohesion and teamwork
Another thing that I love about team building is the customization of team building activities based on the needs of team members.
Despite the short period of time for team building activities, we were able to foster team cohesion and teamwork through the activities. Every activity was uniquely designed to bring awareness and thought-provoking moments to team members about the importance of teamwork at work.
Without team cohesion, each and every one of us would just be a disconnected piece of the puzzle.
Without teamwork, each and every one of us would not share a common ground to eventually reach and achieve our common goals.
Remember that data science projects are not one-man games and require different Subject Matter Experts (SMEs) to bring their respective expertise to the table as a data science team. This comes only after when a team is united which also brings me to the next importance of team building.
3. Encourage collaboration to boost team productivity and performance
One of the best things about team building (at least for my case) is that we got to know the strengths and weaknesses of one another through personality tests and discussion with a facilitator and among ourselves.
As different team members have their own unique strengths and weaknesses, social preferences, and technical knowledge, being able to understand these aspects proves to be extremely helpful when it comes to enhancing team productivity and performance, particularly in a data science team.
Not everyone knows everything, but everyone knows something. Only together can they make something bigger.
You may be a data scientist who is specialized in models building and optimization and has little ideas on what data engineering looks like. And there comes a day when you want to learn more about the technical aspects of data pipeline and architecture to perform certain tasks. Instead of googling on Stack Overflow
to frantically search for answers alone, you could seek advice from any team member who is experienced in this technical knowledge and learn from him/her.
Sometimes the answer is just one question away.
Getting tasks done quickly is one step towards boosting team productivity and performance and this example is just one of many.
There you go. Thank you for reading.
I hope you found something useful here. Indeed, being a data scientist alone is fun, challenging and rewarding at the same time. Being able to work with others as a team is even more important and rewarding.
Have you ever had team building experience in your team before? I'm excited to know how it went for you and learn more about your experience.