Categorized data is the quiet backbone of every report that actually gets used. Without it, you cannot group, aggregate, or compare. So when a facility management project leaves you with a pile of uncategorized rows, you have two choices: manually tag each one, or build a rule set that does the work for you. Power Query and DAX can assign categories automatically, walking through exactly that second path. It is a practical, grounded solution to a problem every analyst has hit at some point. And it is a good reminder that the real skill in data work is not writing clever formulas, but knowing when a repeatable rule beats a manual fix.
What stands out here is the mindset behind the technique. Categorization is not treated as a one-off cleanup task. They treat it as a design problem. By encoding the rules into the data pipeline, they turn a recurring chore into an automated step that runs consistently. That is the shift that matters. It is the same kind of thinking we have seen elsewhere, like in Exploring Paragraph Structure: How LLMs Navigate Token Space, where structure is not just metadata but a way to make sense of raw output. And it connects to Jev vs LLMs: Evaluating AI for Practical Decision-Making, where the focus is on getting classification right under real-world constraints. In both cases, the lesson is the same: rules, whether written by hand or learned by a model, only earn their keep when they hold up in messy, real-world conditions.
Our take is simple. If you are still categorizing rows manually, you are not doing data work. You are doing data entry with a dashboard attached. This approach is not flashy, and that is exactly why it works. It does not promise magic. It shows you how to think about the problem, then gives you a concrete way to implement it. For anyone reading this, the practical takeaway is direct: start small, pick one recurring category assignment, and encode it. Once you see how much mental load that removes, you will wonder why you waited.
The one thing we would push back on is the assumption that the rules have to be static. The author solves the immediate problem well, but the next step is making those rules adaptive. If your categories drift over time, a fixed rule set will eventually produce the same kind of uncategorized rows you tried to eliminate. That is the open question worth watching: can you build a system that learns from its own misses? Until then, the manual rule approach is a solid foundation. It is accessible, transparent, and infinitely better than doing nothing. And in a world where Bridging Retrieval and Action: A New Approach to AI Tasks shows how connecting separate pieces can unlock new capability, the same principle applies here: automate the boring parts, and keep your attention for the decisions that actually require judgment.