Read the map

The map is a searchable catalog of your data’s structure — datasets, columns, owners, lineage, and which columns look like personal data. Reading it well means knowing what Regixo actually measured, how much to trust each flag, and where the honest gaps are.

Read one dataset all the way through (the worked example)

Before the detail, read one dataset end to end so the order is concrete. Here is app-db / customers through all five steps:

Step 1 · Open the map

Open the searchable map in your browser. Run this one yourself — it holds the terminal open:

$ regixo open

Opens the map in your browser at localhost:4319. From the terminal you can search it too:

$ regixo search email

Three ways to reach the same map — pick whichever fits the moment:

Steps 2–4 · Read the map — counts, then columns, then sources

Read the counts first. The toolbar across the top carries the title, the two headline counts (datasets · personal-data columns) and a ↻ Refresh now button ("re-scan · stays local"). The counts size the whole job before you open anything; the two extra labels — Art. 9 and Art. 10 — each carry their own count in the filters. The Map is a workbench that fits on one screen. The rail on the left holds the search box, the filters, and one collapsible fold per source — each fold counting its datasets and its personal-data columns, and grouping the tables it flagged as holding personal data above the ones it didn't. The rail and the pane scroll independently. Below is the real thing — the toolbar, and the rail with the stripe fold opened. (The five filters sit above the folds; they are shown, with their live counts, on Classify & correct.)

Before you pick anything, the pane opens with that one line of plain English — and, under it, the trust panel (Reached · Fresh · Confirmed · Sealed — the same verdict an AI assistant gets from get_provenance; the Reached card is where coverage lives — "All 10 sources") and a card per source (datasets · personal · sensitive · when it was last scanned).

Open a dataset and the pane answers four questions in order: what it is (source, driver, when it was scanned, row estimate, who owns it in the database, and a plain-words summary), what it holds (every column, its type, its personal-data flag — and the small grey note saying how Regixo recognised it), what it's for (the description), and where the data flows. Every flag carries a Correct control, and a labels legend explains the colour codes without a hover. Below is the top of that pane — what it is, and what it holds. The description box and the lineage river sit under it, and are shown on Enrich & share.

What the scanner reads — and refuses

Regixo reads metadata only. It never runs SELECT *, never counts rows, and never reads a value out of a table. Concretely, per dataset it collects:

MySQL and SQL Server give tables, columns and types, but no auto row-estimates or auto lineage (those come from Postgres catalog tables). That’s a limit, stated honestly on the map.

How a column gets flagged

Every personal-data flag comes with the rule that matched — the small grey note beside the flag saying how Regixo recognised the column — so you can judge it. Regixo evaluates rules in order and takes the highest-confidence hit; a column is “personal data” when the best confidence is ≥ 0.6. That score is deliberately not shown beside the flag.

Step 5 · Decide what needs correcting — how far to trust a flag

A flag is the classifier’s best guess from the column’s name and type — never from a value it read. So for anything that looks off, ask two questions, and act on the first yes:

Anything you’re unsure of, check the rule the flag cites (the small grey note) and, if you can, the column’s real contents. You correct both kinds the same way on the next page — this step is only deciding which flags to touch.

Languages

The classifier ships term packs for many languages. Recall is benchmarked and passing for English, German, French and Dutch (≥ 0.85 recall on ordinary personal data, zero false Art. 9 on infrastructure). Spanish, Italian, Portuguese, Polish, Swedish, Danish and Romanian have terms, but their recall is not yet measured — treat those as best-effort and check them.

The kinds you’ll see

Each flagged column carries a human label:

Lineage

Lineage arrows show where data flows. They come from three places, and the map tells you which:

• Warehouse (auto) — view → table edges read from the Postgres/Redshift catalog.
• dbt — the model ref-graph from your manifest.json.
• Asserted by you — cross-system flows a scanner can’t infer (e.g. Stripe → your users table), added with regixo lineage (see Enrich & share).

Open a dataset and its flows draw as a vertical river: Comes from above, the dataset itself on the trunk, Feeds into below. Every chip reads name · source — so customers · app-db is never confused with customers · mysql-db — and each caption says who claims the flow (via dbt, via the warehouse, asserted by you). The head of each pane counts the reach: 2 direct, or 7 direct · 13 total when the chain runs deeper (a cycle-guarded closure). A join, a loop, or an endpoint Regixo hasn’t scanned is stated in words, never left to a guess — and a dashed chip is exactly that: an endpoint someone claims, that isn’t on the map.

How you know you’ve read the map well

• You read the two headline counts and the separate Art. 9 / Art. 10 counts — and sanity-checked any “0 Art. 9” against what you know your business actually handles.
• You opened every source fold and confirmed the sources you expect are all present (a missing fold means a source didn’t scan — that’s back on the previous page).
• You have a short list of flags that look wrong — the false positives to clear and the missed columns to add. That list is the input to the next page.