Good Practices Model for Implementing XBRL-based Reporting System

The document, Good Practices Model for Implementing XBRL-based Reporting System, documents a practical, good practices-based guidance for implementing an XBRL-based reporting system where those reporting within the system are permitted to modify the report model and the need for quality is high.

The good practices-based guidance was gathered over probably 20 years and builds on the foundation set by the U.S. Securities and Exchange Commission (SEC) and European Single Market Authority (ESMA) which collect financial information of public/listed companies using XBRL.

A very basic working prototype is provided which provides all the details necessary to implement your own XBRL-based reporting system.  You can get to that working prototype here.

This graphic compares the good practices model with the SEC and ESMA XBRL-based report repositories:


Try the good practices model out and see how it works.  Why reinvent the wheel or repeat the mistakes that have been made by others.

Comments

Post a Comment

Popular posts from this blog

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

Graph Hairball

Image
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
Read more

PLATINUM Business Use Cases, Test Cases, Conformance Suite

Image
A brick wall is made of exactly two things: bricks, mortar. But a brick wall created by a master craftsman, or mason, and a brick wall created by a “weekend warrior” with no knowledge of masonry will be very different. Master craftsmen are created and that process takes time and effort. This blog post is a first prototype that brings several things together into one useful resource for business professionals.  It pulls together the human readable information of the Showcase of Reports   and the machine readable information of the Conformance Suite of XBRL-based Financial Reports Prepared using the Seattle Method .  Then, I want to incorporate the information found in these PLATINUM XBRL-based Financial Report Examples . The three resources can be useful and used independently; but they are even more useful when used together.  Business professionals really could not care a less about the technical details of getting some result that they want.  But technical professionals need to under
Read more