Insights on data preparation, taxonomy standardization, and making enterprise AI actually work in production.
Your infrastructure choice won't save you from bad data. Here's why most enterprise AI projects fail regardless of platform - and what actually matters.
Read articleDatabricks is exceptional infrastructure for AI systems. But it assumes your data is already clean, structured, and semantically coherent. Here's what happens when that assumption fails.
Read articleThe demo works perfectly. The board approves funding. Six months later, the project is quietly shelved. Here's why RAG implementations fail - and the hidden cost of skipping data preparation.
Read articleTraditional oil & gas companies are acquiring renewable portfolios at unprecedented scale. But they can't integrate what they can't classify. Here's why energy sector M&A fails at the data layer.
Read articleMost organizations rely on informal classification systems that exist only in institutional knowledge and undocumented Excel files. Here's why that breaks enterprise AI - and what formal taxonomies look like.
Read articleYou bought a world-class engine. Snowflake Cortex provides exceptional AI infrastructure. But engines need refined fuel, and your organizational data isn't AI-ready yet.
Read articleBigQuery processes petabytes serverlessly. Gemini integration brings AI directly to your data warehouse. But scale doesn't fix semantic problems, and SQL won't clean your taxonomies.
Read articleYour ML engineers are your most expensive resource, and they hate data plumbing work. Here's the cost arbitrage that makes outsourcing data preparation a strategic advantage.
Read articleIoT sensors + messy taxonomies = failed digital transformation. Energy operations need standardized asset classifications before AI can optimize anything.
Read article