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If you’ve been following the direction of expert opinion in data science and predictive analytics, you’ve likely come across the resolute recommendation to embark on machine learning. As James Hodson in Harvard Business Review recommends, the smartest move is to reach for the “low hanging fruit” and then scale for expertise in heavier operations.

Just recently we talked about machine-learning-as-a-service (MLaaS) platforms. The main takeaway from the current trends is simple. Machine learning becomes more approachable for midsize and small businesses as it gradually turns into a commodity. The leading vendors – Google, Amazon, and Microsoft – provide APIs and platforms to run basic ML operations without a private infrastructure and deep data science expertise. In the early stages, taking this lean and frugal approach would be the smartest move. As analytics capabilities scale, a team structure can be reshaped to boost operational speed and extend an analytics arsenal.

How to implement this incremental approach? This time we talk about data science team structures and their complexity.

Data science team structures

Embarking on data science and predictive analytics requires a clear understanding of how the initiative is going to be introduced, maintained, and further scaled regarding the team structure. We recommend considering three basic team structures …

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