When the machine-readable data is wrong by 4,874x

Almost every screener, terminal and finance site you have used is built on the same raw material: XBRL, the machine-readable tagging that public companies attach to their filings. It is what makes it possible to compare five thousand companies at once.

It is also wrong often enough to change a conclusion.

The case

While building the screening pipeline behind this site, a routine check flagged a company whose balance sheet looked impossibly clean: total borrowings of $6M against a multi-billion dollar enterprise. Kraft Heinz is many things, but debt-free is not one of them.

The number came straight from the standardised data. Opening the filing itself and reading the debt footnote gave a different figure entirely.

Source	Total borrowings (FY2019)
Standardised XBRL field	$6M
Debt footnote, same filing	$29,244M
Difference	4,874x

Nothing was restated. No fraud, no revision. Both numbers were technically “in” the same document.

Why this happens

Companies do not all tag the same concept the same way. There are several legitimate tags that can carry borrowings, and a filer may populate one, several, or a company-specific extension tag instead. A consumer of the data that reads only one field gets whatever happened to be there. The same split runs through capital expenditure, where Amazon has not used the standard tag since 2017 and seven of sixteen companies on a recent screen report it under a different one.

The failure mode is quiet, which is what makes it dangerous:

• It does not throw an error. A missing tag returns nothing, and nothing frequently becomes zero somewhere downstream.
• The result stays plausible. $6M is a number. It sorts, it charts, it passes a range check.
• It compounds. Net cash, leverage, coverage and any valuation built on them inherit the error silently.

A screen for “companies with low debt” will happily hand you the most leveraged food company in North America.

How it gets caught here

Three rules, applied to every figure that carries weight in an article:

1. Standardised data locates, it does not confirm. XBRL is used to find where a number should be. The value that gets published is read out of the filing text and reconciled against the tag.

2. Absence is not zero. If a value cannot be confirmed, it is marked unconfirmed and the dependent metrics are not computed. An unconfirmed borrowings figure means no net-cash figure — not a net-cash figure that assumes zero debt.

3. Anything that survives has to be reproducible by you. Every article on this site ends with the documents its figures came from. If a number here is wrong, the link to check it is on the same page.

What this costs

It is slower. A pipeline that trusts the standardised feed can cover the entire market in an afternoon. Reconciling against source documents cannot.

That trade is the reason this site exists. Broad, fast and occasionally wrong by four orders of magnitude is already available everywhere, for free. Narrow and checked is not.

What would make this wrong

The reconciliation depends on the footnote being read correctly, and that step is done by a person. It is not immune to error — it is only better than the alternative. When it fails, the correction appears on the article and on the corrections log, with the date.