XBRL-based Digital Financial Reporting

For the first time ever in about 7,000 years of accounting, a financial statement can be exchanged between two parties and portions of the information within that statement can be effectively understood by a machine.  Today, both humans and to some extent machines can understand financial statements and work with that information. As Denise Schmandt-Bessersat in her video on the origin of writing , between 5,000 and 10,000 years ago farmers in Mesopotamia, where agriculture was born, used physical object to count crops and animals . The distinction between types of crops or animals was made by using different types and shapes of objects pressed into clay.  It was around that time, in about 3200 BC, around 5,000 years ago, the first spreadsheet was invented. Below you see an example, a Cuneiform tablet with seal impressions: administrative account of barley distribution with cylinder seal impression of a male figure, hunting dogs, and boars : These farmers replaced the pressing of obje

Proficiency is the capability, skill, and knowledge that you might have for doing something.  Proficiency is a progression.  There are general levels of proficiency: literacy, fluency, mastery. Given that the IFRS Foundation is now talking about “ digital financial reporting ”; one could conclude that the era of XBRL-based digital financial reporting is here .  The FASB talks about digital business reporting and XBRL . In the document, Digital Financial Reporting: Facilitating digital comparability and analysis of financial reports ; in the section "What is needed to realise the benefits of digital financial reporting?" the IFRS Foundation provides four points for users of financial reports to realize the full benefits of XBRL-based reporting: be a complete and accurate representation of reported information; be structured in a digitally comparable format;  be publicly available at the same time as reported information; and be centrally accessible in an easy-to-use format. In

Regulation is information. For example, a public company submitting an XBRL-based report to the U.S. Securities and Exchange Commission (SEC) is an information exchange from the public company to the SEC.  The SEC then makes that information available to the public to make use of. That information is a machine-readable digital signal published by the reporting public company.  That XBRL-based information sends a digital signal to the SEC and to those in the public markets that want to use that publicly available information. That digital signal sends a machine-readable message that can also be used to generate a human-readable signal (HTML, PDF).   What message are you sending in your digital signal? A lot of public companies are making mistakes in their machine-readable digital signal.  For example, my personal measurements clearly show that the relations between high-level financial concepts in those messages have mistakes . I am able to extract information from those XBRL-based repo

In their article, Implementing the FDTA: From Data Sharing to Meaning Sharing , the authors Dean Ritz and Timothy Randle, do an excellent job explaining the notion of machine understandability.  The graphic below from page 24 of the document and explained on page 23 breaks down the notion of "understandability" into four levels: Presentation sharing Data sharing Meaning sharing Knowledge sharing with machine understanding Being able to differentiate each of these levels is very important to understand the notion of machine understandability. Understanding or comprehending information is a step in a process.  The second step is analyzing or accessing the information.  Finally, a conclusion can be reached or a decision can be made.  Each of these levels is explained in the sections below. Presentation sharing Presentation sharing involves the sharing of a digital document using some standard format like PDF, HTML, Excel,  OpenDoc such that the document stored on one computer sy

Knowledge representation and reasoning (KRR) is about converting information from an area of knowledge into machine understandable form and then enabling a machine such as a computer using software to process that information in a manner that is as good as a human could have performed that task/process or even better than a human could have performed that task/process. For example, some task or process currently performed by humans that, if measured, would achieve a sigma level of 3 which is a defect rate of 6.7% (about 67,000 defects per million opportunities) would be improved and would achieve a sigma level of 6 which is a defect rate of 0.00034% (about 4 defects per million opportunities). You are hearing me right, defects go from a whopping 67,000 down to 4.  Think I am joking or on drugs?  The Federal Deposit Insurance Corporation (FDIC) call report collection system went from 18,000 defects (reporting errors) down to 0 defects when it modernized their call report system to ma

I read something that gave me an idea.  Imagine a framework for thinking about artificial intelligence somewhat, as an analogy, similar to the notion of the legal framework.  The legal framework refers to the system of laws, regulations, and principles that govern our society. It provides a structure within which individuals, organizations, and governments operate; defining their rights, responsibilities, and obligations.  For example, summarizing the law in very high level terms. "The law"  is a set of rules that a community agrees upon. These rules help keep things fair and orderly, creating a virtuous cycle as contrast to a viscous cycle . Here’s how it works: Customs and Rules : Customs : People in a community follow certain practices and traditions. Rules : These practices become rules that everyone agrees to follow. Binding and Authority : When everyone accepts these rules, they become binding—like a promise. An authority (like a government) ensures that people follow

Common Logic (CL), an ISO/IEC standard , is a logic framework intended for information exchange and transmission.  The ISO documentation describes common logic thus: " Common Logic is a logic framework intended for information exchange and transmission. The framework allows for a variety of different syntactic forms, called dialects, all translatable by a semantics-preserving transformation to a common XML-based syntax ." What common logic enables is explained by the graphic below from Introduction to Common Logic : It seems that common logic is trying to achieve at a lower level of abstraction what I am trying to achieve with something like the Seattle Method and the forthcoming OMG  Standard Business Report Model (SBRM). What common logic does provide the semantics of a logic based system.  What DATALOG does is provide the implementation of that logic based system.  DATALOG is a subset of PROLOG.  ISO also has a PROLOG standard . Together, the ISO Common Logic standard

Intelligence is an idea that philosophers and scientists have been debating for thousands of years.  To this day, there is no single, universally agreed-upon definition of intelligence.  However, here are some things to help you understand intelligence: Intelligent beings and systems can learn and adapt by acquiring knowledge and skills from training and experience and apply what they learn to new situations and can change their behavior based on new information and changes in the environment. Intelligence involves the ability to identify problems, analyze the problems, and come up with effective solutions to those problems. Intelligence involves the ability to evaluate information critically and form sound judgements based on known information and common sense knowledge. Intelligent beings and systems can use logic and logical reasoning to draw correct conclusions from information and make sense within their specific context. Intelligent beings and systems can understand complex ideas

It is important to understand how computers work in order to get computers to do what you want them to do.  The strengths of computers and the obstacles that get in the way of using computers were summarized well by Andrew D. Spear in his paper Ontology for the Twenty First Century: An Introduction with Recommendations ; here is his list summarized into bullet points with some modifications made by me: Fundamental strengths/capabilities of computers : store information reliably and efficiently (tremendous amounts) retrieve information reliably and efficiently process stored information reliably and efficiently, mechanically repeating the same process over and over instantly accessible information, made available to individuals and more importantly other machine-based processes anytime and anywhere on the planet in real time Major obstacles to harnessing the power of computers : business professional idiosyncrasies; different business professionals use different terminologies to refer t

The AI Ladder says (page 5) that 81% of business leaders are confused about artificial intelligence.  Personally, it has been my observation that about 99.9% of not only business leaders, but also technical professionals and I am coming to find even including AI researchers are confused about the real capabilities and potential of AI.  What is my basis for reaching the conclusion that I have reached?  Well, about 20+ years of working to make XBRL-based reporting work and reading the book The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do .  I also have some background in formal logic which does help.  I am no expert in AI, but I am really good at understand what does and does not work and testing. While I am no AI expert, I am very qualitied to ask really good questions. Hype, hype, hype.  For example, Elon Musk has promised full self driving cars since 2015.  So how is that going?  I really like Elon and what Elon and Tesla and SpaceX are doing.  But com

The Three Little Pigs is a fable about three pigs who build their houses of different materials. A Big Bad Wolf blows down the first two pigs' houses which are made of straw and sticks respectively, but is unable to destroy the third pig's house that is made of bricks. This three little pigs analogy can help you think about the construction of your enterprise knowledge graphs. In his article, Data is the new Mud , Mike Dillinger, PhD brings forth the notions of "knowledge bricks" and "soaring towers or sprawling castles"; but also the notion that the data architectures of enterprises are problematic, like mud.  In a prior post, I used the analogy of the freemason of the information age .  Putting these ideas together, this is what I see. I pointed out in my freemasons blog post that a brick wall is made of exactly two things: bricks , mortar .  Mike Dillinger points out, correctly, that you still need to create good bricks .  Basically, the brick making pro

The Department of Philosophy of Texas State provides this excellent differentiation between a condition that is necessary and a condition that is sufficient : "A necessary condition is a condition that must be present for an event to occur. A sufficient condition is a condition or set of conditions that will produce the event. A necessary condition must be there, but it alone does not provide sufficient cause for the occurrence of the event. Only the sufficient grounds can do this. In other words, all of the necessary elements must be there." They further explain that, when you assume that a necessary condition of an event is sufficient for the event to occur you are committing a causal fallacy. This is the problem: accountants creating and/or modifying financial report models. This is a problem of (a) control and (b) enabling business professionals to have that control. Solving the problem is hard but very possible with deliberate, rigorous work. If you know the story of Sam

For a machine-readable information set  (an information product) to be useful, that information product needs to be trustworthy.  One such information product is an XBRL-based digital general purpose financial report . The guidance provided by the Seattle Method enables accountants and others to use a proven, industrial strength, good practices based, standards-based pragmatic approach to creating provably high quality XBRL-based general purpose financial reports when report models are permitted to be modified . When a report model can be modified (a.k.a. customized ), the “wild behavior” of accountants creating such customized reports must be eliminated, keeping report models the accountants create within permitted boundaries.  The report model, in the form of an XBRL taxonomy, is a machine readable (and also human readable) representation that serves many purposes: Description or specification : It is a description or specification of a report model. Construction : Reports can be co