5 Best Practices for Data Science Team Collaboration

Five ways to help your data science team collaborate more effectively and ensure projects deliver real business value.

5 Best Practices for Data Science Team Collaboration
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A data science project is made up of a wide range of skills, with different team members playing different roles. Everybody has their set of skills and responsibilities, which all play a big role in collaborative technical work. 

However, we’re still facing the backlash of the global pandemic and the rise in people continuing to work from home. Naturally, that will cause a shift in the way people work and operate. 

So what can data science teams do to collaborate more effectively? Let’s look into it.


Ensure Models Make it to Production


It's a known fact that there are so many models that have taken time, energy and money to build, but they rarely make it into production. According to VentureBeat AI, 87% of data science projects never make it into production. That’s a pretty high number! But why is it so high?

This is due to the data science element of the business and the actual aim of the business do not connect. And the main reason why they do not connect is that there is a gray area of what the data science team needs to produce to ensure they are meeting the business's needs. 

Better communication between the data science team and the decision makers of a business will allow members of the data team to effectively produce what is required. This can be done by answering the following questions:

  1. What is the business problem?
  2. Is it possible to solve this problem?
  3. Will the business adopt solutions from the data insight?

Answering these three questions allows the data science team to have an in-depth understanding of what needs to be done. 


Document Projects


A data science project consists of people with different roles, from data scientists to data engineers, product managers, IT admin, and more. When working on a project, documenting everything you do provides everybody in the team with a clearer understanding of the process of the project, and what needs to be done next. 

Data science projects will not always be successful, but documenting your every move allows you to learn lessons from the project and what to do next time to ensure success. 

Two rules that you should take with you when documenting projects are:

  1. Although documenting is helping to collaborate with your employees now, it is also collaborating with future employees.
  2. Walk before you run. Operate your data science project like a research paper. Don’t rush to produce the end product, but build an end product that is effective and successful at meeting the goal of the business. 


Knowledge Sharing


By documenting everything, you are also providing knowledge sharing across the company. The data science team holds a lot of ??valuable assets in the company. One of the biggest challenges a lot of companies face is the multiple production of the same work or resource. 

Creating a knowledge share where everyone can have access to information such as code, projects, and models will save your organization a lot of time not reproducing the same thing twice. 

Knowledge sharing works hand in hand with documenting your projects, as employees should be able to see what data sources the data scientist used, the modeling approach, the environment versions, and more. 


Version your Work


Now to get a bit more into the technical elements of data science projects. The majority of data is stored as flat files or can be accessed through relational database systems. However, the biggest challenge that data science teams face is when members of the team download the raw data and produce their work locally without pushing the intermediate data versions back for other members of their team. 

Unfortunately, other members of the data science team will complete the same work, causing a repetition of workloads. Sharing your work is very valuable, as it gives your coworkers a chance to take advantage of the work you have done, so they can work from that.

All your work should be versioned and pushed back to a non-local system, allowing others to see the changes and pull the changes for them to work on it. 

You can ensure this by:

  1. Use a shared server for your team.
  2. Automation tools that push intermediate data files back to the appropriate location. 
  3. Make use of integration tools such as Slack and GitHub, so you can get notified of changes being made. 


Data Pipeline


Data pipelines allow for the flow of data science projects as the data processing elements are connected in series, where the output of one element is the input of the next one. Rather than spending extra time running two or more commands to go from your raw data to your end result, using a data pipeline allows you to see the entire transformation with a single command.

Not only will this reduce the amount of time spent trying to rebuild your project from scratch, but it also provides you with a structural understanding of your data transformation.


Wrapping it up


Although there are other practises that you can use to ensure an even better data science team collaboration method. However, these 5, if done correctly and effectively will allow your team to progress in a more effective and productive manner.

Want to learn about automating your data science workflow, have a read of this: Automation in Data Science Workflows
Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.