Data science is a team sport. There are many facets to creating successful data science teams in a practical, operational sense. It’s rare to hire just one or two on staff, so remember that for data scientists as much as any other role, strength comes in numbers.

Outsource to innovate. If you do the math, a team of seasoned data scientists – let’s say only five – will cost you well over $1 million annually in fixed costs. Like many in IT functions, they’re likely to be pulled in many directions. Having a dedicated resource to optimize your systems with networks getting increasingly smarter with every interaction via machine learning is one way to ensure that projects are efficient while blending technology platform costs with the costs for data science talent that drives them.

Balance functional and strategic tasks. Part of the reason data scientists are so in demand is because they have concrete skills in predictive analytics that others – in IT and business roles – lack. That being said, you’ll need sufficient talent and resources to both write and maintain software and algorithms while also gathering insights from internal teams and customers to customize and optimize the logic behind them.

Set data scientists up for success with the right data management systems. High volume, omni-channel systems are very complex – and time consuming – to manage. Having a hub where data at the individual customer level is aggregated helps set the foundation for data scientists to really shine. Finding ways to automate processes so that the right data is available on demand will make any data scientist’s life easier and will make more possible under their strategic guidance.

Expect to "see inside the black box" of AI. A data scientist should be tasked with explaining the process of machine learning and artificial intelligence in layman’s terms to bring others into their realm throughout the enterprise. This is essential for gathering insights that make predictions stronger and actions more focused by design. And, as marketers take on greater oversight of data, it’s important that CMOs and other decision-makers find complementary talent and technology to help them see the big picture to explore all that’s possible with their data.