Posts

Information Exchange Dynamics (Brainstorming)

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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
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Swiss XBRL Taxonomy

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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
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Human Readable Knowledge Graph of Business Report Model

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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
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Natural or Neutral Formatted Information

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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
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The Threat of Inaccuracy

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" 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
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Unprecedented Clarity

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A couple of weeks back I had a "significant learning moment".  Someone drew the following diagram on a whiteboard and asked, "What does this mean?" Ask ten people and you will very likely get multiple different answers. Who is to say which answer is right and which is wrong?  Basically, if one does not specify what the above means with clarity, then those looking at that diagram are free to make assumptions as to the intent of the creator of the diagram. Let's take this example further.  What does the diagram below mean and how does the diagram below differ from the diagram above? And yet another diagram; how does what you see below differ from what you see above? So, let's keep going.  How does the diagram above differ from the diagram below?  Note that I have added an arrow from Thing 1 to Thing 2. And what about the difference between the diagrams above and below. Note that I added a label that describes the relation between Thing 1 and Thing 2. And now l
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Logic Systems

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The semantics of first-order logic are agreed to and very well understood.  However, not everyone uses the same terminology to describe their understanding of first-order logic so there tend to be many different descriptions of first-order logic using different terminology.  There is no one agreed upon standard description of first order logic.  Current descriptions of first-order logic tend to be technical in nature.  None of those versions are both complete and explained in terms that a business professional can understand.  For example, current descriptions seem to enable the description of the necessary "privative" artifacts and imply the notion of higher level artifacts, but don't explicitly provide common higher level artifacts.  (An example of defining higher level artifacts is provided by Atomic Design Methodology .) And, therefore, I have to come up with my own set of terminology and description to describe what I am trying to describe in terms that are approacha
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