The field of data analytics is dynamic with rapidly evolving innovations. To realize the potential of an enterprise-wide analytics program, leaders and managers at all levels need strong data literacy. Providing opportunities for continuous learning and sharpening of skills is necessary for a robust analytics enterprise able to reliably deliver measurable value to the organization.

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?