High-quality predictive analytics, statistical modeling and data mining tools are the heart of a well-run modern organization. Organizations of all sizes, in all sectors and geographies, are using these tools to drive evidence-based predictions into the full range of business processes, operations
Prioritizing data mining projects is a delicate art, equivalent to the decisions that R&D managers face every single day. How should you prioritize your data mining efforts and allocate your limited resources most effectively? Most important, how do you decide what NOT to work on?
I’ve been trying to delve into a topic I normally avoid—clinical trials. But it’s not about how clinical trials are run, or how to design one. I’m more interested in what happens to clinical trials data once they are completed.
Do folks actually mine their clinical trial archive for insights?
A recurring question and point of debate in the realm of analytics is whether there exists any meaningful difference between data mining and statistics. (Text mining or text analytics is not addressed here, although this area of unstructured or semi-structured data analysis has certain