Why data science teams collaborate
Almost all technical roles need to collaborate as part of their daily functions. But data teams have unique needs that make collaboration deeply embedded in all of their workflows.
The main objective for data teams is knowledge. They arrive at answers by running experiments, creating prototypes and iterating on feedback rapidly to get from queries to insights to a deliverable for business stakeholders. This process is called exploratory programming, and it’s what separates data roles from other technical ones than say, software engineers.
And exploratory programming means a lot of data professionals’ days are spent iterating back and forth and navigating uncharted territories to answer questions for which they don’t have answers beforehand. And it also explains why they have such a need for collaboration between team members and with stakeholders early on and often throughout that process.
How to build a collaborative data science team
As you can see, the best data science teams know that collaboration in data science is not just a convenience; it's a necessity. Collaborative operational data visualizations and data platform team structures are just a few examples of how collaboration is reshaping the field.
But how do modern data science teams arrive at that ideal state where real time collaboration takes a front seat in their daily workflows?
They start with collaborative data platforms and tools that are built precisely for this purpose. One such tool many companies have turned to when building a data science team that needs to be collaborative is online collaborative Jupyter notebooks.
Collaboration in Action: collaborative Jupyter notebooks
Jupyter notebooks have become a staple in collaborative data analysis and data science workflows, but to understand why they have become the go-to tool for collaboration within data teams, we first have to understand somewhat of a laundry list of their must-haves to be able to collaborate in 2024:
- A unified platform for querying, coding, visualizing datasets, and providing written context for team members
- Shared integrations and environments for seamless project collaboration without any complex setup needed
- Both real time and asynchronous collaboration tools, ensuring project continuity with built-in tracking and version control for easy collaborative editing, change tracking and restoring previous work
- Diverse sharing options, including browser-based links, project invitations with specific permissions, and the ability to publish work as interactive, professional outputs like articles, dashboards, or apps
- Centralized workspaces serving as a primary resource for all data projects, simplifying the process of finding, storing, replicating, and building upon team members' work
With teams placing an emphasis on enabling collaborative editing and online collaboration, this demonstrates a growing demand for more interactive and inclusive work environments in data science. This trend is not going away any time soon, and cloud-based notebooks are increasingly the answer to that demand.
Looking ahead: the future of collaborative data science
As we look to the future, the trajectory is clear: data science will increasingly rely on collaborative approaches. Platforms and tools that support this collaboration will become more sophisticated, and their adoption is what will separate data teams of the past and modern ones in 2024.
As we continue to push the boundaries of what's possible in data science, the role of collaboration will only grow in significance, shaping the future of this dynamic field.