XBRL-based Digital Financial Reporting

Knowledge graph system logic, the "things" and "relations between things" that  graph theory calls "vertices" (a.k.a. nodes, points, entities, things) and "edges" (a.k.a.  links, lines, relations, associations), looks like a big graph hairball as some people call it an example of which you can see here: Why?  This common view of a graph is because there has to be some "general view" of the information provided by the graph.  This is just like a general view of the information within a relational database is a table ; a set of ROWS and COLUMNS for exactly one table at a time. So what is interesting is that with a graph database the default view is everything in the graph of knowledge.  Whereas the default view of everything in a relational database is one table at a time. Further, it does not really matter whether the information that populates your knowledge graph comes from, say, CSV files, Excel, JSON, XBRL, RDF, PROLOG or whateve

On the one hand as I point out in this article Systems Thinking ; the complexity of financial reports is increasing, the volume of financial reports is increasing (they are getting larger), and the pace  of such information coming at us all is increasing. At the same time, as pointed out by this article The Talent Dilemma Facing the Auditing Profession  the work performed by accountants and auditors is not seen as fulfilling, long hours are used to deal with those increases, stress results from the longer hours, increased education requirements (in the U.S.) for the CPA because of the resulting complexity, are impacting accountants and auditors.  Further, studies are saying that artificial intelligence is going to replace most bookkeeping, accounting, and auditing jobs. This study includes accountants and bookkeepers in the top five jobs replaced by AI.  So why would anyone go into accounting?  Already, the number of accountants has gone down by 300,000 in the past two years in the Un

Someone made me aware of the paper, REA, Triple-Entry Accounting and Blockchain: Converging Paths to Shared Ledger Systems (by Juan Ignacio Ibañez, Chris N. Bayer, Paolo Tasca and Jiahua Xu).  The authors of the paper say the are striving to correct common misconceptions, acknowledge the influence of key individuals, and map out the overlapping paths of REA, TEA, and blockchain. As a bit of a side note, I was also made aware of the Triple Entry Accounting Inaugural Conference  where people thinking about this stuff will soon meet. While pulling those three pieces together (REA, TEA, and blockchain) is very useful; personally I would go farther and suggest that the overlapping paths the following pieces should be discussed and figure out how they can be pulled together into a system that works like a well oiled digital machine for financial accounting, reporting, auditing, and analysis: Resources, Events, Agents  (REA); Algorithmic Contract Types Unified Standard (ACTUS); and other w

We are going through an industrial revolution for the fourth time .  The Forth Industrial Revolution: what it is, how to respond , published by the World Economic Forum explains what is going on. Things are getting increasingly complicated and practices that once worked in the industrial age no longer work in the information age. Case in point is, say, the financial report of a bank.  Below you see a comparison of a balance sheet of a bank in 1906 with that of a bank in 2018. "Silo thinking" or having a "silo mentality", which most enterprises tend to have, will no longer work with the increased complexity, volume, and quantity of  information.  Our new networked world, our networked economy has new rules that apply because this is a new paradigm.  Applying old, outdated rules makes it harder to understand the change that is occurring . Systems thinking is based on systems theory  and is implemented using systems engineering .   Systems thinking is described well

Business professionals are (a) not trained for having precise discussions of these sorts of issues with software engineers, (b) don’t care to have such technical discussions about these sorts of issues with software engineers, (c) are not interested in the theoretical or philosophical or religious debates that commonly exist related to these alternatives between different software engineers, (d) if the alternatives were appropriately articulated to a business professional, who tend to be very practical, they would most often error on the side of system safety, reliability, and therefore predictability. A logical theory enables a community of stakeholders trying to achieve some goal or objective or range of goals/objectives to describe and agree on important logical details related to the shared understanding for some area of knowledge in order to implement a logical system that works effectively for that purpose. This is my best summary of the moving pieces related to creating a logica

As I see it, the forthcoming Standard Business Report Model (SBRM) that is being created by OMG and my Seattle Method  logical conceptualizations are both grounded in a fixed system of logical patterns.  The frameworks are fixed.  There are a "fixed" or "finite" number of logical patterns of objects that fit together in specific ways within the framework.  But you can put any number of objects described by that fixed set of logic into that framework. That logic can be transformed between any physical technical format that can support the logic contained in that logical conceptualization.  I have already effectively exchanged the logic of the LOREM IPSUM representation of the PROOF of all the logical patterns ( this documented use case ; this conformance suite test case ) between Excel, XBRL, JSON bidirectionally; and HTML, PDF, and PROLOG one way. What I mean by "fixed system of logical patterns" is the following.  The Seattle Method and SBRM each provide

Getting artificial intelligence to work effectively involves understanding how computers work ( engineering ) and understanding how an area of knowledge works (sensemaking). It also involves communicating detailed information using an approach that is approachable to non-technical business professionals.  In my view, that approach is logic. Business professionals have an innate understanding of logic.  Logic can be used to create a logical conceptualization that can be described using a logical theory which can be enforced using a logical schema to keep semantic hygiene  of a global standard knowledge assembly of the logical system ( systems theory ) where it needs to be ( sigma level 6 ). Complexity must be embraced.  There are no short cuts.  Full stop. An area of knowledge is a highly organized socially constructed aggregation of shared knowledge for a distinct subject matter.  An area of knowledge has a specialized insider vocabulary or “jargon”, underlying assumptions (axiom

(This article is inspired by a similar article, A personal digital twin for healthcare , that provides a vision for machine-readable digital electronic healthcare records and their potential use. Also, this IEEE article was very helpful.) The first I have heard about the notion of a " digital twin " was in the paper, Imagineering Audit 4.0 (figure 3 on page 10), written by Jun Dai and Miklos Vasarhelyi of Rutgers University.  In that paper they used the term "mirror world" and used that term to describe the use of technology to create a virtual copy of the real world. (Note that this PDF provides a high level summary of that paper) Here is one graph of a "mirror world" (a.k.a. digital twin) that is provided by Dai and Vasarhelyi: It is becoming more and more clear how to actually build such a digital twin and process the machine-readable information contained in that digital twin.  This would very likely be a knowledge graph system that is specific to f