Seeing Digital Financial Reporting Differently

Why does a butcher create a high level model of a pig?  Well, if you give the different cuts of pig meat a name, that enables a butcher to have a different conversation with customers than if the meat cuts did not have names.  Is there one global standard of pig meat cuts? Apparently not, a quick search of Google shows different graphics, but fairly similar. Cow meat cuts also have names, providing a higher level model of a cow that can be discussed.

The quick answer as to why there are names for the different cuts of meat of a pig, cow, and other animals is utility.  Those names provide something that you don't get if the names do not exist.

Conversations with others has revealed that I apparently, and uniquely, see XBRL-based digital financial reporting differently than others in five very specific and important ways.  Note that unlike many others; I am not trying to meet some regulatory mandate imposed by some regulator.  What I am trying to do is figure out how to effectively employ XBRL-based digital financial reporting such that it is actually useful (i.e. BETTER, FASTER, CHEAPER). Improving efficiency without maintaining or increasing quality is useless.

Here are the five significant differences between my approach to XBRL-based reporting and the approach of others:
  1. Quality: To me quality is job one. Again, improving efficiency without maintaining or increasing quality is useless.  That is the driving objective for all the other things that I do differently.  When others create a financial report they put "stuff" in the report and then they reach some sort of conclusion about whether that "stuff" is right.  They really have no logical basis for their conclusion.  When I work to verify financial report quality, I have a framework for doing so and I try and automate as much of the quality testing as I can.  (see point 4) I consciously create and provide a framework and proof of a report's quality.
  2. Complete: As part of my quality testing, I tend to like to be complete.  Every mathematical relation is consciously checked.  Not some, all.  Every high-level accounting relation is checked to be sure the report complies with those high-level associations.  I specify a complete logical schema, or as complete as automated software can get.  Then, everything else is checked manually.  This is a conscious process.  Why specify a complete logical schema?  Because then you understand the boundaries between what software can achieve and what you must do manually.
  3. Specify and Leverage High Level Logical Model: To achieve #1 and #2 above, I specify a high level logical model, a framework, that I can use for two things.  First, you can do more quality testing using that high level model because the model exists and is specified.  Second, when working with XBRL-based reports because of the high level logical model you can leverage that model in software, making using the software easier. To see this, read the Graph Hairball post. Also I leverage "norms".
  4. Leverage Lean Six Sigma Techniques, Principles, Philosophy: The focus of my MBA was what was referred to as World Class Manufacturing Techniques. Today that is called Lean Six Sigma.  Lean Six Sigma philosophy tend to work to prevent problems rather than fight the symptoms of problems.  Things should be done to consciously prevent mistakes from occurring.  This is as contrast to detecting mistakes and then fixing them or the failure cost of dealing with mistakes, both of which tend to be more expensive than simply fixing a system.
  5. Holistically: I look at the system holistically, not from the vantage point of one individual silo.
I took my approach XBRL-based digital financial reports, explained my approach, and gave that approach and framework a name.  I call it the Seattle Method.  I explained that approach to some people at OMG and they are using those ideas to create the Standard Business Report Model (SBRM).

To see what I do, consider having a look at my PROOF.  The raw material that shows the differences is there but not really explained.  Perhaps I will create a post that explains how to see what I am saying above.  Think "theory" and "proving the theory". That is in essence how PROLOG works.  In PROLOG you make a bunch of logical statements and PROLOG tells you if there are any contradictions.  A human has to figure out if there are missing logical statements such as missing rules.

Why do I seem to approach things differently?  Probably because I am a systems guy.  If you want to understand I would suggest the video A Theory of a System for Educators and Managers.

Additional Information: 

Comments

Post a Comment

Popular posts from this blog

Getting Started with Auditchain Luca (now called Luca Suite)

Image
(Note that a newer, production version as opposed to only the development version , of Luca Suite is available) The Innovator's Dilemma points out that disruptive innovations often look like toys that are doing everything wrong. Auditchain Luca , which is part of the Auditchain Suite , is a unique approach to creating a knowledge graph .  To experience using Auditchain Luca, have a look at the Getting Started with Auditchain Luca instructions. Be sure to grab the Excel and JSON import files used for this getting started exercise.   This getting started exercise uses one of several different approaches to getting information into a tool for constructing and viewing professional quality knowledge graphs .  Additional getting started guides will be made available over time. Auditchain Luca might not make sense to you if you don't understand the paradigm shift that is occurring and if you have not shifted your mental map. For the next step in your journey, consider trying creat
Read more

Relational Knowledge Graph System (RKGS)

Image
Relational Knowledge Graph System (RKGS) is an interesting idea that combines the relational paradigm and the knowledge graph paradigm.  This documentation, Why RKGS , explains the idea. This gets even more interesting with the notion of "relational AI".  RelationalAI is described as follows by the company of the same name, Relational.ai : “RelationalAI is a cloud-based relational knowledge graph management system, with state of the art probabilistic processing and declarative reasoning at scale to make developing Data Applications a superpower for your business.” This notion of knowledge graphs represented in relational databases fits into my belief that knowledge graphs have two aspects: the expression SYNTAX and the logic of what is expressed or SEMANTICS. While the LOGIC of a knowledge graph is always the same regardless of technical syntax used to express that logic; there are many, many different technical syntaxes that can be used to represent a knowledge graph. There
Read more

Professional System for Creating Financial Reports Leveraging Knowledge Graphs

Image
The document Professional System for Creating Financial Reports Leveraging Knowledge Graphs explains a new approach, a new paradigm, for creating financial reports.  The document provides a practical, nontechnical description of a professional system for creating provably high-quality financial reports where the report creator is using financial reporting schemes (such as US GAAP and IFRS) where report creator is permitted to modify the report model which leverages global standard knowledge graphs represented using XBRL. This new approach is useful, it is highly novel, and the details were highly complicated to pull together.  This is certainly not an incremental innovation.  It is definitely a disruptive innovation and it could very well be a foundational innovation. You can decide which for yourself. Foundational technologies have two dimensions that need to be considered: novelty and complexity. The more novel the technology is, the more effort needed to ensure users understand the
Read more