Working remotely from the comfort of your couch or while on a trip is now what life is like for most working professionals around the globe.
Work From Home (WFH), which had been a gradually growing culture for years, is now day-to-day for companies and organizations that were once not very supportive of the idea either.
And what caused this transformation is an answer we all know — the COVID-19 pandemic.
The pandemic was and continues to be a hindrance to the daily working of companies that can’t completely do away with in-office work. Though many firms took to remote working pretty quickly once the pandemic hit their countries, there were several who struggled with the transition. This was especially observed in the case of jobs that had relied on in-person conversations and operations.
Data Science, while not as lost as those others, was still a field that took the change to WFH hard. The data for numerous companies were stored on campus premises while many faced a backlog for request approvals.
Nevertheless, the show must go on.
And thus began the shift to remote working, including the ups and downs that come with it. Managers all over, meanwhile, were also facing several difficulties in managing their data teams and though some have adapted well, there are many that could still use a hand.
For them and you, here are some simple-to-adopt-and-execute tricks and tools to get your teams going.
Tips To Ensure A Productive Remote Working Experience
Let’s dive into how you can better manage your remote data science teams.
Collaborate on Chat-based Platforms
Communication is paramount for the success of any task, especially the ones which call for a team effort. When working from the office premises, this need for interaction was met easily through a physical meeting and conversation. However, working alone from their own respective homes can pose major problems for the team.
The use of collaborative platforms like Zoom, Slack, and Microsoft Teams which not only allow the user to share data and files but also provide the space to freely converse through text messages and video calls is therefore a definite must. It lets the team members maintain a kind of normalcy through dialogue on both personal and professional topics.
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Collaborate on Tech-based Platforms
Data Science teams often require the very basic facility of being able to work together on the assigned projects. Now, this could be because each member has a separate role they play in it, or because advice and/ or suggestion from a colleague is what may help them out of a knot, or simply because inputs of all players are necessary for the successful completion of the task.
Regardless of the reason, however, there are some platforms that allow easy collaborations for data science teams like GitHub and Jupyter Notebook. While GitHub helps the members in tracking changes in the source code and virtual code reviews, Jupyter presents itself as a computational notebook wherein code can be created, edited, and shared alongside explanations and images.
Standardize
When working in an individual capacity, oftentimes each member of a data science team may start using different software or an updated version of the same one. But when it comes to sharing and reproducing the same, a new difficulty crops up.
Whether a particular package can be reproduced and opened easily on another software or on an older version is always up to speculation. Thus, the best way to avoid this tricky situation is to have a centralized server with a standardized environment which should be mandatory for all to adopt. RStudio is an amazing tool that can help you with this task.
You should also extend your standardization efforts to team documentation and agreements by co-creating templates and using digital signatures that improve security, speaking of which…
Establish Stricter Data Security
Work from home often means lax security for many companies. After all, ensuring that your office’s computers do not engage with USB drives or that your firewall keeps information from leaking is relatively simple. But making sure that the data is not being misused or leaked when all personnel work from home is not as easy.
For this, it is essential to put the effort into first understanding firewall as a service. When you comprehend that a firewall can be tied to your company servers (cloud or local) instead of simply to the geographical location of your corporate devices, it is a step towards implementing it and thus making your security measures stricter. After all, cloud migration can also open up several avenues for a cyber-attack in today’s day and time. Perimeter 81 is a well-known name when it comes to securing your networks.
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Reduce Dependencies
Today, anyone who is even remotely associated with the field of data science knows and can easily comprehend the difference a timezone or a geographical location can make in a project. That essentially means that working together from the office just might be the best approach. But what do you do in situations such as the present pandemic, where gathering at the same place is not an option?
That is where melda.io comes in. This is a platform that is entirely location and timezone-independent, designed specifically for the data science teams working tirelessly around the globe. It also allows for multiple teams to access and reproduce the packages created simultaneously.
Closing Thoughts
Every office has its own employee- or work-based culture, procedures, and systems they cherish. And we absolutely respect that. But when moving to a new working system, i.e. remote working, it is crucial to modify the same so everyone stays on track and efficient.
While no two organizations may have the exact same needs, you can try a bunch of these tools to figure out which one suits your requirements the best. After all, choosing the right resources that facilitate these best practices is also just as vital.