XBRL-based Digital Financial Reporting

The Seattle Method is a tested, proven, industrial strength, good practices-based, standards-based pragmatic approach to creating provably high quality XBRL-based digital information. A new version of the Seattle Method documentation has been made available. This new version provides the following enhancements: Added a few missing sections. References to the most current examples and prototypes. Incorporates feedback received from others. New version of Auditchain Luca provided. Production version on a significantly more powerful server. Enhanced version of Essentials of XBRL-based Digital Financial Reporting that uses Platinum examples and test cases. What I am realizing is that the ideas above are broader than just accountancy; pretty much every area of knowledge has similar issues related to the effective exchange of information and the quality of that information. Additional Information: PLATINUM Use Cases, Test Cases, Conformance Suite PROOF Auditchain Luca Production Version E

In this blog post I am inventorying the important things that impact information exchange which I can later reassemble and synthesize. Much of this information and thinking is inspired by work of Sean McGrath . Blending Technology and Liberal Arts As Steve Jobs pointed out, there needs to be a proper blending of technology and liberal arts. This applies to hardware, operating systems, and software applications. Direct vs Mediated Communication There are two types of communication: direct and mediated. Direct communication is one person having a conversation with another person; if there is a question about information context, those involved in the communication can directly discuss and resolve any context issues.  Mediated communication is indirect and resolving context issues can be more challenging because parties involved in the communication cannot really have a discussion about the immediate context. Vagueness is the Enemy In an email Sean McGrath said, “Vagueness is the enemy of

Switzerland has published a revised version of their XBRL taxonomy which appears to be part of a Standard Business Reporting initiative . This blog post provides information about that most recent version. For more information see this . Here is background information for the Swiss CH Taxonomy. I was involved in testing this most current version of the Swiss CH Taxonomy.  What I did was put a copy on my we site and used the taxonomy as if it were being published.  Here is summary information about that Swiss CH Taxonomy: Summary of all Swiss CH Taxonomy artifacts English version of Swiss CH Taxonomy Reference implementation of report Approximately 607,000 small- and medium- sized enterprises could use this XBRL taxonomy to report to the Swiss government. Additional Information : XBRL is an Extra Fancy Knowledge Graph Logic Systems Seattle Method Value Explained (Pillars of Quality and Trustworthiness) Switzerland Mandates Machine Readable Climate Disclosures XBRL Switzerland and Audi

The following is my best effort to define the knowledge graph of a business report from multiple interoperable technical implementations including: XBRL Cloud 28msec Pesseract Auditchain Pacioli Auditchain Luca Suite A business report, including financial reports, are composed of blocks of information that can be identified as specific "disclosures" or in the case of financial reports, financial disclosures. ( Image from Wikicommons ) This same business report model was implemented by five different software vendors and are interoperable.   However, there are three issues with my business report model that I am aware of.  First, the model has an XBRL bias.  I  don't understand how to remove that bias or I would.  Second, I am not an information technology professional or knowledge engineer so I don't have professional skills related to building sound models.  This is basically the best that I could do given my understanding of XBRL, financial reporting, and how to cre

One of the unique aspects of XBRL-based information is that the information is readable both by machines and by humans.  This article explains how that capability is achieved. Simple "Hello World" Example Here is a very simple " Hello World " example of XBRL-based information. (Here is the same report in the XBRL International test case format .) This is the same information rendered for editing a report as opposed to reading the report: Here is the same information in the form of a sharable viewer which you can use to have a closer look at this small Hello World! example. Here is the same information in the form of a set of HTML pages that also provide a means of viewing the information from within the machine readable XBRL files. How This Works So the rendering of the information that you see by multiple different software vendors in multiple different but all very human readable formats is not a one off.  This  capability is by design. Here is a comparison of

" The Threat of Inaccuracy ".   I wish that I could say I came up with that phrase, but I did not.  That phrase is from a whitepaper published by Fluree, Decentralized  Knowledge Graphs Enable Most Accurate Generative AI Results . Effectively, what that whitepaper says is, "Garbage in, garbage out." How is artificial intelligence going to deal with inaccurate information? Think about something.  Why would you expect information provided by artificial intelligence, generative or otherwise, to be useful if there underlying input information has inaccuracies? Things like workflow automation are a result of or consequence of the capability to remove inaccuracies from input information.  And notice that I am using the term "information" and not "data". People don't seem to be grasping that what is happening is a paradigm shift. "Data", "information", and "knowledge" are not the same thing.  Information is application