Success With Analytics Starts With Data Literacy

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.

Making Good Use of Negative Space in Machine Learning

Be Smarter Than Your Devices: Learn About Big Data

Why Machine Learning Is the Coolest Science

The Persuasion Paradox – How Computers Optimize their Influence on You

Healthcare Analytics: Exploration vs. Confirmation

Data is Not Oil. It is Land.

Tiger vs. Jack – Asking the Right Questions

A Deep Dive into Deep Learning

Do We Have the Right Data?

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?

5 Key Reasons Why Analytics Projects Fail

Fraud, Anomaly Detection, and the Interplay of Supervised and Unsupervised Learning

The Data Speak: The World Is Getting Better

Why Data Literacy in the C-Suite Matters

Top 3 Lessons Learned While Drinking from the Data Science Firehose

Monte Carlo Simulation – a Venerable History

Simple, Attractive, and Wrong: An Introduction to Linearity Bias

Top 3 Objectives Before Starting an Analytics Project

Machine Learning: It is a Mistake to Lack Relevant 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.

Finding Fraud When No Cases are Known

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?