Overview

This overview is an attempt to take the work that I have done to understand XBRL-based reports and back into a framework for explaining those reports to accountants and software engineers building software for accountants.  The sources of the information provided below are my "lab notebook" from December 2007 to October 2022; my Digital Financial Reporting blog from October 2022 until January 2025; Mastering XBRL-based Digital Financial Reporting; and finally my Seattle Method documentation.

What I have tried to do is take the seemingly hundreds of incomplete, far too technical, typically nonstandard explanations that I have come across; take the good/best ideas from each, make improvements to those explanations, and resynthesize the information into a form that is useful to me and perhaps useful to other business professionals which have a liberal arts education (i.e. not a technical oriented computer science education).  How my conclusions were reached are generally provided via reference to the resources provided above.

Narrative

Accounting and reporting is an area of knowledge. The knowledge within that area of knowledge can be represented in machine understandable form using a number of knowledge representation approaches. Those different representation approaches are interchangeable to the extent of the logic included within each representation approach.

Financial statements and financial reporting as practiced when using financial reporting schemes such as United States Generally Accepted Accounting Principles (US GAAP), International Financial Reporting Standards (IFRS), Government Accounting Standards in the United States, International Public Sector Accounting Standards (IPSAS), and other such financial reporting schemes are not "standardized forms". Rather, such financial reporting schemes are intended to be, and should be, "customized reports".

Both standardized form based reporting and customized report based financial reporting provide specific capabilities.  If a customized report based approach is used, "freeform" or uncontrolled report customizations will not work.  Rather a "controlled report model" approach must be used in order to keep reports created within the boundaries of a specified report model in order to facilitate both the flexibility necessary for reporting economic entities required by these sorts of financial reporting schemes and the control necessary to enable effective reporting systems to eliminate "wild behavior" by reporting economic entities; keeping them within the necessary boundaries to facilitate the level of quality necessary to make use of reported information contained in customized report models and reports.

This reality is independent of any physical technical format used to represent the report models and report information. There tends to be two and maybe three best practices global standard technical formats for implementing such reporting systems: (1) XBRL and (2) RDF + OWL + SHACL; and potentially (3) Graph Query Language (GQL).  But other non-best practices based approaches are also possible.  All of these physical technical formats could be implemented within three patterns of problem solving systems: Semantic web stack, Graph databases, Logic Programming. The logic of the Standard Business Report Model (SBRM) is expressive using using any of these problem solving approaches:

Further, the logic of a business report per the Standard Business Report Model (SBRM) can be expressed using other physical technical formats. That means the SBRM logic can be bi directionally transferred between the different formats.  Further, that SBRM logic can be converted from the physical technical format to human readable format using a high-level logical model of a business report and the machine readable physical technical format into a human readable format (this is not bi directional). And finally, a "pixel perfect" human readable representation can also be created by mapping report facts into a human readable format such as Inline XBRL which is a combination of XHTML and XBRL.

Knowledge representations using SBRM can be passed from an enterprise software application, to another enterprise software application, to an agent, from one agent to another agent.

Sensemaking is the process of determining the deeper meaning or significance or essence of the collective experience for those within an area of knowledge.  My superpower seems to be sensemaking. My system of interest was the general purpose financial statement. A financial statement is effectively a complex message.

However, the capabilities of the Seattle Method and the Standard Business Report Model (SBRM) are not, and have never been, limited to only financial concepts.  SBRM relates to "words" and "numbers".  SBRM has a dimensional model.  This makes SBRM appropriate to many other general areas of business reporting. Financial reporting is simply one special use case of general business reporting.

Fundamentally; for a human to exchange information with another human, a human to exchange information with a machine, a machine to exchange information with a human, or a machine to exchange information with another machine; the following must be true: (true information exchange)

Humans effectively exchanging information is hard enough. To implement an effective exchange of information between software applications is in some ways harder, but in other ways easier.

A new era of human/machine teaming has arrived. Machines will augment the capabilities of humans, intelligence amplification.  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) could be improved and could 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.

Seem impossible? Lean Six Sigma techniques, philosophies, principles, and practices have been around for years, driving operational excellence in many different industries.  Industrial engineers have been making use of Lean Six Sigma for years. This has led to high product quality of manufactured products in many industries. Now Lean Six Sigma can be applied to information processing.

BOTTOM LINE: Humans and machines teaming up, each bringing what they do best to the table, making processes better, faster, and cheaper is what this is all about. The threat of inaccuracies is reduced; quality goes up.

Additional Information:

• Knowledge
• Area of Knowledge
• System
• Agent
• Logic
• Elements of Logic
• Knowledge Representation Approach
• Complexity