Making Machine Learning Interpretable Without Sacrificing Accuracy
Complex models can still explain themselves—if you ask the right questions in the right ways.
When Simple Models Beat Complex Algorithms
Why linear regression and decision trees often outperform sophisticated algorithms in production environments
Building Recommendation Systems That Don't Annoy Customers
Why stellar accuracy metrics often mask recommendation experiences that frustrate rather than delight users
Why A/B Tests Fail to Detect Real Business Improvements
Most A/B tests are designed to fail before the first user sees a variant
Building Analytics Teams That Actually Deliver Value
Why your analytics function produces reports instead of results, and what high-impact teams do differently
Why Correlation Discoveries Rarely Lead to Business Action
Most analytical insights never become profitable interventions because correlation and causation require fundamentally different reasoning.
The Surprisingly Effective Power of Business Rules in Analytics
Domain expertise encoded as rules often outperforms pure machine learning—knowing when to use each creates analytical systems that actually work.
The Truth About Real-Time Personalization
What real-time personalization can actually deliver versus what vendors promise, and how to measure whether it's worth the investment
Why Your Customer Segmentation Probably Doesn't Work
Most segmentation projects produce beautiful clusters that gather dust—here's how to build segments that actually change what you do.
Predictive Maintenance: What Actually Works Beyond the Hype
Cut through predictive maintenance hype with proven techniques, realistic ROI frameworks, and practical implementation strategies that actually reduce downtime.
The Real Reasons Machine Learning Projects Fail
Why technical excellence can't save projects doomed by organizational failures—and how to spot warning signs before investing millions
Why Most Churn Prediction Models Fail in Practice
Discover why high-accuracy churn models fail to save customers and how intervention-focused prediction design transforms retention outcomes
The Hidden Cost of Bad Data Quality in Machine Learning
Why fixing your data often delivers better ML returns than upgrading your algorithms, and how to identify which quality issues matter most
How Feedback Loops Silently Destroy Model Performance
Your model's predictions shape its training data—learn to detect and break the loops that silently erode performance