In a previous APEX of Innovation post, we examined the importance of the various roles that make up a typical data science team. In this article, we’ll be taking a global view and look at some chief considerations when building a data science team that spans multiple geographies, time zones, and languages.
Determine the Team’s Purpose
When embarking on a data science project, it’s important to first establish the goal of the initiative. The same can be said for building a global data science team. Tech leaders should begin by determining the data’s strategic purpose, what the organization is trying to achieve, and other critical questions. Armed with this insight, companies can then train and recruit the data science team most capable of collecting, analyzing, and leveraging data based on its strategic purpose.
Bridge the Gap Between Technology and the Business
Data translators are a relatively new addition when compared to more established roles such as data engineers and data analysts. However, data translators can bring a significant amount of value to a global data science team because they bridge the gap between the technical side and the team’s business application by producing accurate, actionable business insights from the data.
Of course, it’s important to have certain individuals who are specialists in particular areas. But building agile teams comprised of generalists who can function in multiple areas of the business can reduce bottlenecks and, in turn, increase the team’s overall productivity.
Create a Scalable Team
In a similar vein, scalability is important when building a high-performing global team. Ensuring that team members are cross-functional and value-driven is one way that companies can lay the framework for future scalability.
Consider Cultural Nuances
It’s not uncommon for managers to skip team building and similar exercises under the mistaken assumption that data is a universal language. But in order to collaborate on data analytics, team members must first be able to cooperate with one another. Work styles, cultural norms, and habits can differ vastly from country to country, region to region, and even office to office. It’s important that tech leaders are cognizant of this fact and take the time to introduce teammates, clarify roles, and foster camaraderie among their global data science team.
For more on these and other considerations, head over to CIOinsight.