Tabular Data

Everyone wants the grand AI replacement story. The model eats the stack, digests the workflow, and emits profit. Very tidy. Also, usually nonsense. The more interesting pattern emerging in applied AI is smaller, less theatrical, and considerably more useful: the model is not the system. It is an intervention inside the system. It edits one field. It predicts one missing signal. It routes one candidate generator. It enters through a side door, preferably wearing a badge. ...

Search Me: Why PIPER Makes Tables Findable When Metadata Goes Missing A data catalog is supposed to answer a simple question: where is the dataset I need? In practice, it often answers a different question: which dataset owner bothered to write a decent title, description, and tag list? That distinction matters. A table may contain exactly the columns, ranges, patient attributes, locations, dates, or transaction variables a team needs, while its metadata says something thrilling like “export_final_v3.csv.” The dataset is technically present. It is not findable. This is a familiar enterprise tragedy: the data lake is full, the catalog exists, and still everyone asks the same analyst where the useful files are. Excellent digital transformation, naturally. ...

A database column named CURRENT_BAL_AMT is annoying. A column named gbstk is worse. Somewhere inside an enterprise data warehouse, these names are perfectly normal. Somewhere outside the original engineering team, they are tiny locked doors. The usual solution is not glamorous. Someone asks a data engineer. The data engineer asks an older data engineer. A wiki page is found, partly wrong, last updated during an earlier economic cycle. Eventually, “current balance amount” or “overall processing status of sales document” appears in a data catalog, a semantic layer, a search index, or a text-to-SQL system. Humanity advances by one abbreviation. ...

A manager asks the analytics copilot, “Which regions are underperforming this quarter?” This sounds like a normal business question. It is also, technically, a small swamp. Which regions? Sales regions, operating regions, logistics regions, or customer billing regions? Underperforming against what: forecast, last quarter, budget, peers, margin, revenue, retention, or some executive’s private sense of disappointment? And “this quarter” may mean calendar quarter, fiscal quarter, quarter-to-date, or the latest complete quarter if the finance team has not closed the books yet. ...

TL;DR for operators Most enterprise RAG failures do not begin at the chatbot. They begin earlier, when the retrieval system slices policy manuals into arbitrary chunks, flattens tables into textual porridge, ignores metadata, retrieves semantically similar but operationally wrong passages, and then asks an LLM to look confident. Naturally, the LLM obliges. It has excellent manners. ...