FEATURED POST

In my recent posts about building autonomous analytics systems  and first-party data strategy , I've focused on what to build and why. Now let's talk about a critical architectural decision that affects cost, complexity, and capabilities: real-time versus batch processing. Everyone wants real-time analytics. The promise is compelling: see what's happening right now, respond instantly to changes, make decisions with the most current data possible. But here's what I've learned

Over the past few weeks, I've been writing about agentic AI systems and how they're transforming analytics. But there's a critical foundation that often gets overlooked in all the AI excitement: your data strategy. The best algorithms in the world can't compensate for poor data quality or incomplete data collection. And with third-party cookies disappearing and privacy regulations tightening, organizations that haven't invested in a robust first-party data strategy are going

A few weeks ago, I wrote about agentic AI in analytics  and how autonomous systems are transforming how we work with data. The post resonated with many in the analytics community, but talking about concepts is one thing—actually building something is another. So I decided to put theory into practice. I built an autonomous anomaly detection system for Google Analytics 4 that runs 24/7, monitors real GA4 data, and alerts me when something genuinely unusual happens. Not as a pro