In this blog, Jeff Deal discusses the problem of organizations waiting until they have perfect data before starting an analytics project. In our experience the mistake of “waiting for perfect data” probably kills more projects than any other. So how do you know if you have the right data?
In this blog you will learn that the less probable the interesting event is, the more data it takes to obtain enough to generalize a model to unseen cases, and why some projects probably should not proceed until enough critical data is gathered to make them worthwhile.
BLOG: Fraud detection is about finding needles in haystacks and requires reliably labeled instances of fraud and non-fraud behavior to train a predictive model to best separate fraud from non-fraud cases. But what do we do when labels are not just rare, but are completely absent?
In this blog from Elder Research, Peter Bruce explores some of the differences between data science and statistics, and discusses the “Method of Moments.”
Peter Bruce, President and Founder of Statistics.com, explores statistical bias and some common sources of algorithmic bias that can beset analytics projects.
In this blog Elder Research Data Scientist Will Goodrum uses surfing analogy to highlight the importance of selecting the right problem to solve with analytics.
Guest author Harpreet Singh Sua explores how fraud analytics has played a major role in developing new methods and strategies that can be used to prevent fraud.