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Understanding that XBRL-based Digital Financial Reports are made up of Distinct Identifiable Pieces

Did you know that the 2014 10-K financial filings submitted to the SEC by public companies reported a total of 8,816,913 facts?  Here is other information: (from the 6,751 reports analyzed)

  • 7,404,893 (or 84%) of facts were reported using base concepts from the US GAAP XBRL Taxonomy.
  • 1,412,020 (or 16%) of facts were reported using extension concepts created by the reporting entity.
  • 495,825 networks were used to organize the reported facts.
  • 73 networks were used on average per report, with a range between 5 and 321 networks.
  • 1,306 facts were reported per report, with a range between 31 and 33,216 reported facts.
  • 5,226 (or 77%) entities reported using classified balance sheets
  • 110 (or 1.63%) entities reported income (loss) from equity method investments after tax and as part of special reporting items

There is a lot more interesting information. The document Understanding that XBRL-based Digital Financial Reports are made up of Distinct Identifiable Pieces summarizes that information and much, much more.

But the primary thing the document does is prove beyond a shadow of a doubt that (a) digital financial reports are made up of lots of distinct identifiable pieces, (b) what those pieces are and, (c) what the relations between the pieces are.

Why is this important?  This information is very helpful in creating software useful to business professionals to create digital financial reports correctly.  Errors such as the minimum criteria can be avoided by having software manage the mindless tasks of keeping these sorts of details consistent so that business professionals can focus on the aspects of such reports which require judgement.

Still don't understand why all this is important?  Then you need to brush up on your knowledge engineering skills.

If you do understand, then the next thing you will want is this concise summary of the things and relations between things which make up a financial report.  The next step is to distill the pseudo code into machine-readable business rules.  Why is that needed?  To build useful software.

Posted on Sunday, May 3, 2015 at 09:32AM by Registered CommenterCharlie in | CommentsPost a Comment

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