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AI Readiness Through Data Hygiene: A Practical Guide

Everyone wants AI right now. Very few teams are ready for it. The gap usually is not model access. It is data hygiene. If your CRM has duplicates, your warehouse models are lightly tested, your metric definitions change depending on who is in the room, and your dashboards already disagree, AI will not solve the problem. It will scale it. That is what “AI readiness through data hygiene” actually means: getting the underlying data trustworthy enough that scoring, automation, and copilots can be useful instead of noisy.

Marketing Attribution for SaaS: The Complete Guide

I’ve spent the better part of a decade helping SaaS companies figure out which marketing dollars are actually generating revenue. And the single most consistent thing I’ve learned is this: most attribution problems aren’t attribution problems at all. They’re trust problems. Data plumbing problems. Alignment problems. The company spending $80K/month on paid acquisition doesn’t need a fancier attribution model. They need their CMO, CFO, and VP of Growth to look at the same number and agree it’s real. That’s a fundamentally different challenge than picking first-touch vs. last-touch, and it requires a fundamentally different approach.

Why Your Attribution Model Is Lying to You

Most SaaS companies don’t have an attribution problem. They have a trust problem. Marketing says one thing, finance says another, and the CEO doesn’t believe either number. Sound familiar? The issue isn’t which attribution model you pick. It’s that your data pipeline, your tracking, and your reporting were never designed to answer the question you’re actually asking: “Is our marketing spend generating revenue?” Stop Obsessing Over Models First-touch, last-touch, multi-touch, data-driven — the model matters far less than whether your underlying data is clean, consistent, and connected to actual revenue.

Building a Modern Data Foundation with dbt: A Practical Guide

I’ve helped dozens of companies build data foundations. The ones that fail almost never fail because they picked the wrong tool. They fail because nobody agreed on what the foundation was supposed to do. This guide covers the decisions that actually matter: how to pick a warehouse, why dbt wins as the transformation layer, which architecture patterns work for mid-size teams, and how to migrate off legacy ETL without losing your mind. If you’re a data leader at a SaaS company between $5M and $100M in ARR, this is written for you.

5 dbt Implementation Mistakes That Kill Data Trust

dbt changed the game for analytics engineering. But like any powerful tool, it can create as many problems as it solves — especially when the implementation is rushed or the team doesn’t have a clear plan. Here are the five mistakes I see most often when companies implement dbt, and what to do instead. 1. No Testing Strategy This is the most common and most damaging mistake. Teams build dozens of models but write zero tests. Then they wonder why stakeholders don’t trust the numbers.

Data Activation Playbook: From Warehouse to Revenue

Your data warehouse already knows which customers are about to churn. It knows which trial users are most likely to convert. It knows which accounts are primed for expansion. It’s just not telling anyone. That’s the gap this playbook is about. Not building more dashboards. Not running more ad hoc queries. Actually getting your warehouse data into the systems where your team makes decisions and takes action — automatically, continuously, and without someone exporting a CSV every Tuesday morning.

Your Data Warehouse Is a Goldmine You're Not Using

Here’s a pattern I see constantly: a company spends six months and a significant budget building a data warehouse. They hire analysts. They implement dbt. They build dashboards. And then… nothing changes. The dashboards exist, but decisions are still made on gut feel. The data is “available,” but nobody outside the data team actually uses it. The warehouse is technically excellent and operationally irrelevant. The missing piece isn’t more data. It’s activation.