The #1 Way to Make Your Data Science Team Succeed

Your analytics project team is typically comprised of individuals from different departments (IT, business, and analytics), with a variety of experience, skill, and knowledge levels, as related to data science and artificial intelligence. This makes communication and collaboration absolutely imperative to a successful analytics project.

This blog will explore the steps you can take to ensure effective collaboration and communication across your team to drive a successful advanced analytics project.


Get the team onboard.

It’s important to have a strong foundation for your analytics team to build off of. Ensure your team understands what you’re doing and why you’re doing it.

You want to get rid of any idea that AI is magic — it’s a science.

Make sure you’re on the same page about the insights you are looking to create and the outcomes you are looking to achieve. As a result of your analytics project, you should be saving money or making money (understand additional ROI means here). Ensure this is clear with your team before moving forward. 


Be transparent.

No one on your team benefits when you don’t share what’s going on with your analytics activities. Instead, communicate openly about it. Discuss what you’re doing to find results, why you’re looking for those results, and what the outcome of this project will be.

Everyone needs to have a voice in order for the model to be successful. Encourage your team to share any input they may have throughout the process.


Utilize recurring quick-hit meetings.

Quick-hit meetings are 30-minute, repeating meetings that happen weekly to bi-weekly, to create a feedback loop and ensure that collaboration occurs. This is not a project checkpoint for budget, scope, and timeline discussion; it’s a collaboration checkpoint to be utilized for data quality and business value.

As part of these meetings, your team should be checking if the numbers look right. Do they make logical sense? If not, why might these numbers not be what you expected? 

This is a crucial step. In an example from a blog we recently wrote, we were working on a data set that showed when a specific operator was working on a machine, the machine had the most failures. If we took the data without talking to the business and built an algorithm that predicted machine failures, that operator would have been a key variable in predicting those failures.

Knowing that collaboration is important, prior to building an algorithm we worked with the business on the data. What we discovered is that the specific operator was doing twice the work of any other operator! Thus, of course, that operator would have more machine failures and should not be considered in predictive failures without accounting for that volume disparity.

We get asked very often about how to influence or better create a data-driven culture. This method is one of the most effective ways to educate your organization on data-driven processes and promote a data-driven culture while realizing business benefits.


Encourage non-data scientists to get involved.

The non-data scientists on your team just might be your most valuable analytics asset. Take advantage of this and explore how you can upskill employees with a business background to enable enhanced analytical activities on their end.

These individuals are called Citizen Data Scientists and when you bring them into your project, you benefit from the diverse background and set of experiences they have, unrelated to data science.

Transparent communication and overall collaboration across your analytics team are both critical to a successful analytics project. Enable these by communicating thoroughly at the beginning and throughout a project. Scheduling recurring quick-hit meetings is an effective way to do this. 

Additionally, there’s a benefit in bringing your non-data-science-focused team members in on the project, as it can be helpful to have a variety of experiences and viewpoints.


Interested in more ways you can ensure your advanced analytics team is successful? Download our 5 Strategies to Make Your Advanced Analytics Initiatives More Successful infographic.

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