November 1, 2017
At TDWI, we see increasing interest in open source for analytics. For example, in a 2017 survey, the majority of those planning advanced analytics projects are planning to do so using open source analytics technology, such as R and Python. At the same time, those already deploying advanced analytics (such as machine learning) are using a combination of open source and commercial products in a hybrid-sourced approach. In fact, many of those already using open source use it within a commercial product.
Although excitement is building around open source analytics, many organizations still do not know much about it, including how to get started and whether open source is even right for them. This TDWI Checklist Report discusses some best practices for evaluating open source analytics.