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Why data science teams should operate like startups


With the increased use of data in business, organizations are scrambling to mount data science teams. Because these teams are relatively new, there is no precedence on how companies should structure them, and many experts have differing opinions. Of course, the structure of the data science team will differ based on the company focus, team size, and the data itself. Companies, however, can take successful operational elements from startups and apply them to data science teams.

Because most startups fail, experts have narrowed down operational elements that are more likely to give a startup a chance to succeed. Three of these elements include using Lean and agile methodologies, instituting a flat structure, and pivoting quickly. As data science teams emerge and grow, they can adopt these three tactics to increase productivity and operate effectively.

Data science teams and agile methodologies

Data science teams enacting agile methods is not necessarily new since development teams around the world use an agile approach for success. According to the Project Management Institute), 71 percent of organizations report using agile approaches sometimes, often, or always. And PwC found that agile projects are 28 percent more successful than traditional projects.

Scaling data science and making the model operational are two challenges teams face. Often, data projects bring in devops teams and developers that need to work with the data science team, which can mean teams code collaboratively. The agile methodology works well in these scenarios, especially when models in production need engineering to deploy or multiple resources to support the model. Continuous integration and automated testing, two pillars of the agile methodology, work well with data science projects that combine multiple team members and ongoing work.



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