XBRL-based Digital Financial Reporting

An Internationalized Resource Identifier (IRI) is a standard proposed by the IETF ( RFC 3987 ).  An IRI is a unique sequence of characters that identifies a resource.   IRIs are a generalization of URIs ( RFC3986 ) that permits a wider range of Unicode characters. A  Uniform Resource Identifier (URI) is a unique sequence of characters that identifies an abstract or physical resource.  The difference between an IRI and a URI is that IRIs use the more modern  Universal Character Set (a.k.a. UNICODE)  and URIs use the older ASCII character set. The newer IRI format is compatible with the older URI format. The most  current version of RDF  uses IRIs to identify resources. Don't confuse IRIs with URLs and URNs.  A URL is a web address. A URN is a Uniform Resource Name. This Venn diagram shows the relationship between these different tools: Why are IRIs and URIs important?  If you are thinking in terms of a single database, a silo of data, then IRIs and URIs might not make much se

A new business model is emerging which is the delivery of super-useful machine-readable knowledge products what will drive modern software applications.  Many knowledge products will be delivered in the form of high-quality knowledge graphs.  It is my view that accountants, auditors, and analysts will create such knowledge products to package and sell their hard earned knowledge. To understand what a knowledge product is; I want to explain the differences between data products, information products, and knowledge products.  In summary, data products focus on providing raw but trusted, accessible data for analysis. Information products provide information, often in a structured format like a report or guide. Knowledge products take this a step further by processing and organizing information to make it super-useful. In my view, NFTs will play a role in the delivery of knowledge products. (See the Data Product Pyramid ) Data is raw and unprocessed and tends to be understandable only in

 A kludge (or kluge) is an engineering/computer science term that describes what is best described as a workaround or quick-and-dirty solution that is typically clumsy, inelegant, inefficient, difficult to extend and hard to maintain; but it gets the job done. By contrast, elegance is beauty that shows unusual effectiveness and simplicity.  Accounting and audit working papers have evolved to become a kludge. That needs to be fixed. My solution is proposed in the document Case for Semantic Oriented Accounting and Audit Working Papers .  VisiCalc popularized spreadsheets.  Then Lotus 1-2-3 improved the capabilities of VisiCalc.  Then Excel improved upon Lotus 1-2-3.  Google Spreadsheet added some features beyond what Excel provided.  It is time for another improvement in the capabilities of the spreadsheet. Or, maybe the improved tool is not really even a spreadsheet. Perhaps in is more of a "knowledge graph" maybe with "pivot table" type capabilities with a built in

Audit is broken, obviously in need of modernization. Accountants are being overwhelmed with the volume, pace, and complexity of information.  Global multinational organizations using hundreds of spreadsheets to fulfil and manage their external reporting process. The status quo of financial accounting, reporting, auditing, and analysis is doomed.  But the question of what will be the replacement is still on the table.  While the status quo is certainly going away; it is still unclear what the replacement will be. But the flywheel is moving . A first step in the process of modernizing the global audit machine for the future is to provide the fundamental machinery that will be used by that global audit machine. Is the Excel spreadsheet the right machinery? What are the workable alternatives? Maybe logical spreadsheets . Here is some additional information to help you contemplate the modernized capabilities: (think compilation, review, audit working papers) Accounting/audit working papers

People seem to throw around the term "ontology" when discussing artificial intelligence and it is hard to understand precisely what they are referring to.  Also, I rarely hear the word "theory" used when discussing artificial intelligence. Not sure why that is. When trying to understand the capabilities of software to perform reasoning, the first thing to understand is the type of logical reasoning that is being performed.   Logic is a formal system that defines the rules of correct reasoning.  Logical reasoning is about arriving at a conclusion in a rigorous way.  There two broad categories of logical reasoning: deductive and non-deductive .   Deductive reasoning provides a result that is guaranteed to be certain , therefore the result can be relied upon without doubt and humans need not be involved in a process because of the certainty of deductive reasoning.  Non-deductive reasoning, on the other hand, is not certain , meaning it could be correct but it could al

This is an updated version several prior versions of this similar graphic. (This regarding what I called the XBRL stack , and this on problem solving systems .) All of these are inspired by another similar graphic that describes what is referred to as the Semantic Web Stack or "layer cake" . What I am trying to show is a technology neutral framework that addresses all the aspects necessary to create a smart (cognitive) business application or service .  Basically, all of these pieces are necessary and need to be addressed in one way or another: (working draft) Starting at the bottom and then working up, here is information about the parts of this graphic above which will help you understand what each box in the above graphic is getting at: Identifiers : (URI, IRI, ID, LEI) URL  (Universal Resource Locator, more information ) URI (Universal Resource Identifier, more information ) Namespaces  (xml:names) Identifiers  (xml:id) GLEI LEI Data Types : (W3C XML Schema) W3C XML Sche

You know, I never thought I would see the day; but here we are!  The Financial Accounting Standards Board (FASB) has published information about US GAAP semantics in global standard machine readable form. GAAP Meta Model Relationships Taxonomy Physical files in US GAAP XBRL Taxonomy for 2024 The GAAP meta model representation uses Accounting semantics  arcroles 1.0  first created by Campbell Pryde, the CEO of XBRL US but have subsequently been published by XBRL International as a global standard recommendation. One thing that is incredibly interesting and useful is the capability to view and link to specific information in a tool provided by the FASB.  For example, if you click here you will be taken to specific information about inventory valuation reserves which includes references to the formal Accounting Standards Codification (ASC) published by the FASB. As I see it, this is a milestone in financial accounting, reporting, auditing, and analysis.  This is a game changer.  People w

Everyone loves sexy information dashboards that they can use to do things like manage a small business effectively.  But here is the deal: if the information in that dashboard is wrong and/or not timely or is otherwise not trustworthy; then what good is the dashboard? (Below is BlueIQ ) I am a certified public accountant (CPA) and have been doing financial accounting for about 45 years with a focus on accounting information systems.  In addition, I studied and received an MBA in what was then  referred to as "world class manufacturing techniques" then and is now called  Lean Six Sigma . Many small businesses (and a lot of big businesses) have problems with their reporting systems because information that is put into their systems is incorrect or incomplete.  Further, the systems are not set up well which causes further struggles when you try and get information out of the system.  Another problem is that descriptive metadata that helps you get information out of the system e

The article by Dan McCreary, Knowledge Graphs: The Third Era of Computing , points out that we are entering a new era in computing.  Interestingly, the article starts out explaining that "computing" began about 5,000 years ago in around 3,000 BCE when accountants created what amounts to the first knowledge graph in the form of facts stored in the rows and columns of a table on a clay tablet. Those clay tablets were replaced by papyrus scrolls; the papyrus scrolls were eventually replaced by paper; and the paper, as the author puts it, was replaced by tables in a relational database of an enterprise resource planning (ERP) system that could be processed using COBOL which was an early computer language used for processing business information. Further, as pointed out by the article, accountants demanded ultra high quality in their representations and a clever Italian accountant figured out that if you used two independent but synchronized representations and as part of your con

Here is one accountant's perspective on knowledge graphs.  My overall philosophy and technique is summarized in the document Logical Digital Twins of Financial Reports which is an accumulation of about 20 years of trying to figure out knowledge graphs.  The methodology that I use summarized by the Seattle Method .  The tools that I use can be understood by using Auditchain Luca, and here is information relating to using that software . And finally, these use cases and test cases helps you understand what I can do with knowledge graphs. Happy to take any feedback what improves that I am trying to do with knowledge graphs. Ping me on LinkedIn . I have been told by others that I have a unique approach to knowledge graphs.  This blog post summarizes that approach which has a focus on high quality, leverages a high level model to manage complexity to make creating software approachable to business professionals, uses Lean Six Sigma principles and philosophies, and I come at this holi