The Secrets to Building Highly Effective Data Science Teams

The use of data science has become increasingly essential to success in today’s business climate...

The use of data science has become increasingly essential to success in today’s business climate. As more teams and positions are being created in the field of analytics, leaders have found establishing successful data science teams to be a difficult task to manage. Let’s explore the key elements to building a highly effective data science team for your next project as well as how to maximize your current talent.

Promoting Curiosity is Key

Curiosity is at the root of any successful team or project. Data can help organizations uncover and understand almost every aspect of their business, from key marketing drivers to even forecasting seasonal changes in sales. But first, your analytics team has to be willing to continually ask the data questions and perform deep dives into the information. While data visualizations provide comprehensive value when it comes to your data, not every issue or insight can be seen from the surface. 

Additionally, curiosity and exploration are important in fostering engagement amongst analysts. Increased engagement can lead to greater involvement in projects and higher quality insights that may not have been discovered otherwise. Implementing a weekly brainstorming discussion or providing access to educational courses can assist in stimulating curiosity when it comes to how your team approaches company data.

Make Experimentation and Research a Priority

Time management is another fundamental element to team effectiveness, what you spend your time on is vital to efficiency. It’s important to assess where time could be better spent and identify any possible inhibitors or distractions.

To evaluate how your team’s time is being allocated, start by asking questions such as: 

  • What type of projects do you typically spend the majority of your time on?
  • How often do you spend time on research or long-term projects?
  • Do non-technical users in your organization have easy access to data or do they require assistance from a data scientist?

The answers will illuminate where your team’s focus lies in the day-to-day. Your team shouldn’t be constantly tasked with requests for administering access to data or answering basic ad-hoc questions. These types of tasks can easily be supported by improved infrastructure or self-service analytics tools. While your data science team might be highly proficient in these assignments, an abundance of these requests will ultimately distract your team from their data science research and higher-level work. You should make a point to limit these requests in order to emphasize research and experimentation. 

High-Level Goals are Known and Understood

The team needs to understand the end game of any project and how it fits into the organization’s overall goals. It’s not uncommon for technical teams to experience a disconnect from business teams and their objectives. Making sure these business targets are known and understood will allow your team to better communicate their findings in relation to company goals. 

Furthermore, you should clearly define how your data science team should be interacting with other areas of the organization. Team members should regularly be involved in discussions surrounding strategies to stay up to date on the key drivers behind projects across departments. This will ensure effective collaboration between your data science team and any business stakeholders. 

Feedback and Continuous Improvement

There is always room for improvement, continuous learning is fundamental to the long-term success of any team. Be sure to carve out time at the end of a project to review performance. You should not only highlight positives and team contributions but also evaluate processes or methods that could be improved. Routine feedback will assure the success of future projects and give those involved an opportunity to progress as a group. 

Overall, there are ultimately many factors beyond this list that contribute to building a highly effective data science team. While every team is unique, providing a foundation for alignment with the business side of the organization, good communication, and exploratory research is key to success. 

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Picture of Scottie Todd

Scottie Todd

Digital Marketing Lead

“Level 4 marketing wizard on a quest for
data insights one blog post at a time.”


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