Across industries, machine learning drives value – building revenue, cutting costs and making organizations more competitive. But machine learning can be challenging to implement: data scientists are scarce, and many analytics teams lack the right tools for today’s big, complex and fast data.

In this paper, we discuss:

• Why so many data scientists say they could be more productive than they are today
• Why companies pay data scientists top dollar – then fail to provide the right tools
• What data scientists do most of the time. (Hint: it’s not machine learning)
• The key components of an agile platform for machine learning
• Simple steps you can take to build a high-performance data science team