How Do You Measure Success?

Building a Successful Analytics Project from the Ground Up

Like a house built to withstand the seasons, a successful and sustainable analytics project must start with a firm foundation, a purposeful plan, a seasoned team, and the right tools and materials.

A Holistic Framework for Managing Data Analytics Projects

Roadmap to Becoming a Data-Driven Organization

Leveraging Data Analytics to Increase ROI

The Persuasion Paradox – How Computers Optimize their Influence on You

Can Data Analytics Really Deliver 1300% ROI?

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?

Hiring a Data Analytics Consultant

Are We Using Machine Learning?

It is a Mistake to Ask the Wrong Questions

Hype or Reality: The ROI of Machine Learning

Developing an Analytics Strategy: The Analytics Champion

Developing an Analytics Strategy: The Role of Culture

Jumpstart Your Data Science Team with Experts

Choosing the Right Analytics Problem

This blog explores the difficulty knowing how to get started using data science and predictive analytics and how choosing the right problem and focusing on a few key guidelines delivers greater business value and gains support for analytics from key stakeholders.

Building a High-Functioning Analytics Team

Goodhart’s Law, Evolving Threats, and Model Monitoring

Elder Research Data Scientist Stuart Price discusses Goodhart’s Law and the risk that any metric applied to a competitive or adversarial system will change behavior. If your adversary has a good chance of figuring out your metric, how can you keep your system from being gamed?