XBRL-based Digital Financial Reporting

For purposes of communication within an area of knowledge (such as a science or business) we need an unambiguous vocabulary, a controlled vocabulary. The terms of a controlled vocabulary need to be articulated and related in a rigorous, explicit, consistent, formal way.  Other ways to describe this notion of a controlled vocabulary is a "jargon" or "specialized language" or "technical language". A system  is cohesive groups of interrelated, interdependent components that can be natural or human-made. A system has patterns. A system can be explained by a logical theory .  A logical theory is an abstract conceptualization  of specific important details of some area of knowledge. The logical theory provides a way of thinking about an area of knowledge by means of deductive reasoning to derive logical consequences of the logical theory. A logical theory enables a community of stakeholders trying to achieve a specific goal or objective or a range of goals/objec

Knowledge is an asset.  In fact, knowledge is our greatest asset .  Knowledge is an accumulation of your education and experience.  Your skills and abilities are knowledge assets. Knowledge can be represented in machine-readable form.  As an example, here is knowledge of AASB 1060 which is a financial reporting scheme represented as an XBRL taxonomy .  That knowledge is effectively the terms, associations between terms, rules, structures, and such. According to the Business Rules Manifesto, article 4 ; business rules should be represented in declarative form: " Rules should be expressed declaratively in natural-language sentences for the business audience ." Rules, in my view, can be represented or supported by an NFT or non-fungible token. To better understand NFTs, please see this document that explains the top 30 use cases of NFTs in the enterprise . Knowledge must be managed.  Machine readable knowledge needs to be curated to keep it current.  This curation and managemen

Artificial intelligence is not going to change the world.  It will be artificial intelligence combined with human intelligence that will enable change. AI + HI = Transformation Artificial Intelligence (AI) plus Human Intelligence (HI) will cause a transformation.  The Harvard Business Review article Digital Transformation Is About Talent, Not Technology , by Becky Frankiewicz and Tomas Chamorro-Premuzic, has it right.  The summary of the article says: Contrary to popular belief, digital transformation is less about technology, and more about people. You can pretty much buy any technology, but your ability to adapt to an even more digital future depends on developing the next generation of skills, closing the gap between talent supply and demand, and future-proofing your own and others’ potential. It’s really quite simple: the most brilliant innovation is irrelevant if we are not skilled enough to use it; and even the most impressive human minds will become less useful if they don’t tea

The document Key Trade Documents and Data Elements  (page 6) provides this explanation of the difference between "digitisation" and "digitalisation": Digitisation involves converting data from an analogue to digital format (e.g. taking a paper report and converting it to a pdf).  Digitalisation , on the other hand, is about transforming entire processes to be digital. It is about using technology to change the way that business-as-usual is conducted. Personally, I don't think that the above explanation goes far enough.  Details are left out.  I tried to explain this idea also with my notion of financial report levels which were inspired by the True Car breakdown of autonomous vehicles into levels which are explained in detail in this video I created, Financial Report Levels .  Here is a summary of those levels: ( maturity levels ) Level 0 (Provide information physically) : Not machine readable . An example of Level 0 is a clay tablet, papyrus, or paper as the

This is a collection of information that seems important. Most of this is from a LinkedIn post by Tony Seale . Embracing Complexity (Conclusion)  by Tony Seale Embracing Complexity (Part 1) Embracing Complexity (Part 2 Data) Embracing Complexity (Part 3 Cloud) Embracing Complexity (Part 4 AI) Geometric Deep Learning: The Erlangen Programme of ML video by Michael Bronstein Connecting your Data to GPT video by Tony Seale Graph Transformer: A Generalization of Transformers to Graphs Systems Theory Quality, Quality, Quality: The Status Quo is Doomed The Great Transmutation This is not about creating complex things. This is about looking at things as a system. This is about embracing and managing the complexity. A kluge is a term from the engineering and computer science world, it refers to something that is convoluted and messy but gets the job done.  Anyone can create something that is complex.  Anyone can create a kludge.  But it is hard work to create something that is simple and elegan

The International Chamber of Commerce (ICC) and the World Trade Organization (WTO) have created the Digital Standards Initiative . These three documents provide excellent information with regard to that initiative:  Overview :  Digital Trade Standards and Simplified Trade Systems  (see slide 5, apparently XBRL International is part of this effort) Vision : Key Trade Documents and Data Elements Standards :  Standards Toolkit for Cross-Border Paperless Trade (see the PDF referenced) Here is the general idea:  Today, annual global trade is valued at approximately US$ 28 trillion .  However, this process is highly dependent on paper documents in spite of efforts to digitize the process.  There are approximately 40 key trade documents and an estimated 4 billion pages of documentation that supports this global trade, currently only about 1 percent is digital.  Efficiency gains in the billions if not trillions of US dollars could be realized if the process was effectively digitalized. Of the

Kaggle is a an online community of data scientists and machine learning practitioners per this video, What is Kaggle: An Introduction and Walk Through .  You can use Kaggle to: Publish data sets or use data sets published by others Publish models or use models published by others Collaborate with others Enter competitions to solve data science challenges What I am most interested in right now is the fact that I can publish data sets.  The first data set that I published was SFAC 6 Elements of Financial Statements . It is my personal view that data sets or better yet information sets will be monetized by accountants in the future.  In my view, a data set can be tied to an NFT (Non Fungible Token). This next phase of financial accounting, reporting, auditing, and analysis will be driven by high-quality machine readable data and information sets curated by accountants.  Accountants might not grasp this idea if they don't understand that we are going through a phase change . Here is