The current state of applied data science

Check out the Data science and machine learning sessions at Strata Data in New York, September 25-28, 2017, for more on current trends and practical use cases in applied data science. As we enter the latter part of 2017, it's time to take a look at the common challenges faced by companies interested in using data science and machine learning (ML). Let's assume your organization is already collecting data at a scale that justifies the use of analytic tools, and that you've managed to identify and prioritize use cases where data science can be transformative (including improvements to decision-making or business operations, increasing revenue, etc.). Data gathering and identifying interesting problems are non-trivial, but assuming you've gotten a healthy start on these tasks, what challenges remain? Data science is a large topic, so I'll offer a disclaimer: this post is mainly about the use of supervised machine learning today, and it draws from a series of conversations over the last few months.