XBRL and Me

Right from the early days of collecting data, I could imagine a time when data would be extracted automatically from annual reports for analysis. Back then I assumed this would be achieved by clever machines using OCR and AI techniques (Some of the software I've built recently has attempted to use similar methods). In those days, we didn’t sell structured data, just standardised financial statements and ratios on bits of paper. The widespread adoption of computers allowed for the migration of this standardised data into structured databases. But often you were putting square pegs into round holes. Our solution was to expand the data set, so whatever was reported could have its own "hole", to create the world’s first as reported database. In practice, it only had 1600 data items (compared to nearly 16,000 tags in the XBRL US-Gaap Taxonomy!) so the description "as reported" was always stretching it a bit, but we were applying that old 80:20 rule and so more often than not a data item was as described. We even had an equivalent of company defined taxonomy extensions, where if a data item didn’t fit, it would be stuffed into a tagged “other” item (ensuring it all still added up) and broken out in a custom labelled table.

To cut a long and rather dull story short, I love the thinking behind XBRL. It’s a great idea. I have though become more than little curious with regards to it's implementation.