GenBI: Three Shifts That Could Change How You Think About BI Implementation
At Google Cloud Next '26, a conversation about genBI made me rethink nearly 20 years of BI assumptions. Here are three shifts worth considering.
At Google Cloud Next '26, a conversation about genBI made me rethink nearly 20 years of BI assumptions. Here are three shifts worth considering.
I've been interviewing QA/QE candidates lately, and the bar is not what I expected to find. Here's what I'm actually evaluating - shared in the spirit of helping the right people level up.
AI has made it almost too easy to spin up GCP services without thinking through the cost implications. Here's how I built a hard cap that actually cuts off spending - using Google's own recommended pattern.
Someone asked me how I handle ambiguity. Here's my honest answer - and why I'm not entirely sure it's right.
The process of hitting a ceiling, figuring out what's missing, and coming back stronger works the same way whether you're learning handstands or distributed systems.
Around 2021, if you wanted AI in a data workflow, you picked a vertical. Coding assistance. Data validation. Each was isolated. That constraint is gone now — and it changes everything.
Code comments, unit tests, documentation, diagrams — the stuff that chronically didn't get done. Gen AI changed that completely, and the impact on data teams is bigger than most realize.
I spent years building OLAP cubes. Carefully maintained conformed dimensions, aggregation tables, multidimensional models. That infrastructure was real work. Today, it's increasingly obsolete.