BLOG:  Digital Financial Reporting

This is a blog for information relating to digital financial reporting.  This blog is basically my "lab notebook" for experimenting and learning about XBRL-based digital financial reporting.  This is my brain storming platform.  This is where I think out loud (i.e. publicly) about digital financial reporting. This information is for innovators and early adopters who are ushering in a new era of accounting, reporting, auditing, and analysis in a digital environment.

Much of the information contained in this blog is synthasized, summarized, condensed, better organized and articulated in my book XBRL for Dummies and in the chapters of Intelligent XBRL-based Digital Financial Reporting. If you have any questions, feel free to contact me.

Entries from February 20, 2022 - February 26, 2022

Essential Role of Taxonomies, Ontologies, Schemas, Theories, Models

Taxonomies, ontologies, schemas, theories, and models are not really all that sexy; but they are essential, and often forgotten, when it comes to getting artificial intelligence to fundamentally work or scaling artificial intelligence and get it to perform useful work.

Machines cannot read, interpret, make sense of, or otherwise understand data that has no structure.  It is that structure that provides the power.  A well designed taxonomy, ontology, schema, of a model are fundamental to teaching machines to understand patterns in data and information.

Subject matter experts with knowledge, skill, expertise, and training in an area of knowledge are critical to building taxonomies, ontologies, knowledge graphs, schemas, theories, and models correctly.  Clean data is also critical to this pattern detection and documentation process.  This process actually has a name, it is called sensemaking.

Sensemaking is the process of determining the deeper meaning or significance or essence of the collective experience for those within an area of knowledge. Sensemaking involves:

  • Looking for patterns in information.
  • Making connections among different things.
  • Synthesizing lots of information and categorizing it into small chunks.
  • Think about the big picture.
  • Think about the "why" of a situation.
  • Organizing and untangling things.

It is this sensemaking that yields the machine-readable taxonomies, ontologies, schemas, knowledge graphs, theories, models, and meta-models that make artificial intelligence fundamentally work and scale and perform useful tasks.

The machine-readable information makes no sense really unless you have a tool that can process the machine-readable information.  Pacioli, which I now describe as an XBRL-based Financial Report Knowlege Engine, is such a tool.

The global standards based machine-readable information by itself, like my examples, make no sense if you don't have a tool that can process the information.  A tool like Pacioli really makes no sense without all that machine-readable information.  But when you put the two together correctly, what appears to be "magic" happens.

But it is not really magic, it is just logic and math.

Subject matter experts in the area of accounting, financial reporting, auditing, and analysis that have the skills, experience, training, and vision will put these pieces together.  All this will get better, and better, and better over time as the machine-readable information evolves and the tools improve.

If you want to understand, read the Essence of Accounting, Financial Report Knowledge Graphs, and the Seattle Method.  Become a bounty hunter.  Help built the accounting oracle machine.

###############################

Logical Schema

Graph Schema Languages

Terminusdb schema to blueprint appliations

WOQL uses datalog

TypeDB, a way to describe the logical structure of your data

Why Graph Databases Will Win

Posted on Friday, February 25, 2022 at 07:10AM by Registered CommenterCharlie in | CommentsPost a Comment | EmailEmail | PrintPrint

SFAC 6 Updated by SFAC 8

Today, Louis Matherne who is the Chief of Taxonomy Development at the FASB posted a message indicating that the FASB has updated Elements of Financial Statements (Chapter 4) and Presentation (Chapter 7) of the US GAAP conceptual framework.

I have represented the SFAC 6 Elements of Financial Statements in machine readable XBRL.  Looks like I need to update that.

Elements of Financial Statements and Presentation are foundational.  They are the basis for my Fundamental Accounting Concepts Relations.

Just as a reminder, the elements of financial statements defined by the FASB (and learned in Intermediate Accounting class) are:

  • Assets
  • Liabilities
  • Equity (Net assets)
  • Comprehensive Income
  • Investments by Owners
  • Distributions to Owners
  • Revenues
  • Expenses
  • Gains
  • Losses

Here are the relationships between the elements: (from this document related to business events)

Elements of Financial Statements says this about the elements of financial statements:

Elements of financial statements are the building blocks with which financial statements are constructed. The term elements refers to broad classes, such as assets, liabilities, revenues, and expenses. This chapter focuses on the broad classes and their characteristics and does not discuss or define particular items that might meet the elements definitions. For example, economic items and events, such as cash on hand or inventory, that meet the definitions of elements are not elements as the term is used in this chapter. Rather, they are called items or other descriptive names. Notes to financial statements are not elements, though they serve important functions that are distinct from elements, including amplifying or
complementing information about items in financial statements.

 For more information please see Essence of Accounting.

########################################

My Version of SFAC 8 in Machine Readable Form

Another version of SFAC 8 which adds features for not-for-profit entities (This is a more precise version)

Posted on Thursday, February 24, 2022 at 01:55PM by Registered CommenterCharlie in | CommentsPost a Comment | EmailEmail | PrintPrint