How to maximize the output of a small data science team

In 2005, the National Science Board began advocating for a data science career path in an effort to ensure experts could manage the inevitable onslaught of future data. While the practice of data science reaches back to the 1960s, and includes several older disciplines like statistics, it wasn’t until these past 15 years that companies began to integrate data science into their daily operations and decision-making in a more comprehensive way. While integration has grown exponentially, data science is still an emerging field, and enterprises around the globe are now competing to find ways to transform data into profit.

As the new industry emerges and higher-education scrambles to adapt, the demand for expertise significantly outweighs the supply, resulting in a competitive job market with some of tech’s highest salary expectations. Although some top tech companies have the luxury of operating multi-tiered robust data science teams, most companies rely on lean groups of individuals to produce insights relevant across all sectors of the business—research and development, sales, marketing, operations, customer service, HR, and the list goes on.

Most companies have outgrown the myth of the 10x engineer, but it is also crucial to foster realistic expectations of data scientists. To maximise the output of a small data science team, companies must embrace the structures and resources necessary for them to thrive and generate impressive ROIs. Here are some simple tenets to follow.

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Full Text： itproportal

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