Congratulations! You’ve embraced machine learning and data science and your organization is well on its way to building a system that helps you deploy predictive analytics for greater insights. You’ve already done a lot of the heavy lifting, from finding the right platforms and tools to help you build your models to understanding the steps required to empower machine learning in your organization. The good news is that you’re on the right track to building a data science infrastructure.The better news is that now you’re ready to start going beyond your own data and into the wide world of external data. Running machine learning models exclusively on your internal data is a great start, and it can give you some powerful insights. However, the results will always be somewhat limited, and may even fail when presented with data and conditions that are radically different from the historic ones they have seen.The answer? Feed your models external datasets that can give them greater context —...
