King Kong vs. Godzilla: Code Generator vs. Code Reviewer

There's a particular kind of engineering conversation I genuinely enjoy. Not the kind where someone brings a problem they already have the answer to and just wants you to nod along. I mean the kind where someone says something that makes you stop and reconsider what you thought

How Product Organizations Scale Without Splitting Strategy from Execution

Quick recap from my previous article: In high-performing product organizations, strategy and execution aren't split between different roles. Every product team needs someone who owns both the "why" an

Legacy Migration Starts with Understanding, not Inventory

An alternative to scale and direct legacy migration activities

How we use Claude to write code

Claude is a tool. It helps us think faster and write code faster. But the developer is still in charge. The goal is simple: better code, clear thinking, and full responsibility. This document explains

Introducing DANA: Conversational AI for Legacy System Modernization

Imagine your data expert could answer every team's questions at once, without a single meeting. That's DANA. Built by DataChef, DANA is a conversational AI that learns from your domain experts, unders

The Missing Right Side of Your dbt DAG

If you maintain a dbt project long enough, you end up with a familiar problem: you can see what’s upstream of a model, but it is surprisingly hard to answer what is downstream in the real world. Which

Don't Split Product Strategy from Execution

Mention the Product Owner role to someone from a startup or scale-up background, and you'll likely get a blank stare. They'll probably google it later to figure out what you meant. On the contrary, pe

Spec-Driven Development: Ship Features with AI Guardrails

Something has fundamentally shifted in how we build software. A year ago, if you told us we'd ship a production web application, 8 major features, 11 releases, 17,000 lines of TypeScript across a full

Event Storming and Context Mapping for Data & AI Product Managers

Discovery and requirements gathering are among the most critical phases of data product management. Yet they're also among the most challenging. Too often, data teams fall into the trap of being order-takers, responding to ad-hoc requests rather than...

Product Managers and the Art of Engineering a 'Yes'

If you spend any time reading about product management online, you'll frequently encounter a piece of advice: saying no is the product manager's superpower. There are countless guides on how to say no, why to say no, and frameworks for saying no with...

Copilot VS. Custom LLM: Navigating the Generative BI Landscape

For years, Business Intelligence has followed a familiar, often frustrating pattern. An executive has a question, an analyst hunts for the answer, engineers build data pipelines, and eventually—days or weeks later—a dashboard appears. By the time the...

Is Your Organization Ready for Data Mesh? A Practical Readiness Check

You are being asked to make a long‑term bet on data architecture. Most conversations frame this as a choice between data lake and data mesh. Vendors, internal teams, and reference architectures encourage you to pick a side. 💡 “Data lake vs data mes...