XBRL-based Digital Financial Reporting

Reporting is about disclosures.  Standards setters and regulators specify disclosures.  Accounting professionals within a reporting economic entity create disclosures based on the specification.  Auditors verify that a disclosure is true and fair.  Analysts extract information from disclosures and often compare disclosures across periods or between reporting economic entities. The work of creating a disclosure is tedious; yet at the same time there are very serious consequences from errors. The current conventional disclosure review process is labor intensive, prone to human error, time consuming, and costly. But what if there were a better way?  What if software could be used by standards setters, regulators, and anyone else creating a reporting scheme to effectively specify required disclosures and supporting information and that information was available in both machine-readable form and also human-readable information could be generated directly from that ma...

This version of the PROOF which I am referring to as the Super Proof helps technical people understand how the Seattle Method works to control the quality of an XBRL-based financial report. The first step to understanding this is to watch this video related to the verification of an XBRL-based financial report . (video is 6 minutes and 40 seconds) The next thing to understand is that you can load any of these XBRL files into software that supports XBRL and view the files. Pesseract is an excellent tool, but you need to download and install it. Contact me if you want a license. This Super PROOF compares and contrasts three different report configurations.  This is the human readable HTML version of the report . REPORT #1 : Start with this XBRL instance .  To verify that XBRL instance using a tool such as Auditchain Pacioli's Power User Tool , you have to add this extra information to the discoverable taxonomy set . The resulting verification result will look like what you se...

Modern accountancy will be spinning up over the coming years.  The transition will take time and no one knows exactly how it will turn out; but the change is inevitable .  By accountancy I mean the professional practice of accounting, reporting, auditing, and analysis.  This includes financial accounting, management (cost) accounting, and tax accounting. While no one knows exactly how this change to modern accountancy will turn out, there are plenty of clues that can help you make an educated guess. Here is my list of things that will change: (To effectively understand this list, you need digital literacy . Don't make the mistake of using your old mental map to try and understand.) Accountancy will become less tedious.    Today, accounting can be quite tedious.  Tomorrow, many of the tediousness will be reduced by the creation of improved, smarter tools. Reduced reliance on Excel, more use of semantic spreadsheets.   Excel is a great tool, except...

Informatics for Accountancy   (DRAFT - work in progress) is a summary of information which I have collected and synthesized over the past 25 or so years.  It provides a solid foundation of digital proficiency , it provides fundamental digital literacy.  This is a first step in becoming a freemason of the information age . After you reach the level of digital literacy, then you can work on digital fluency and mastery.  Brick-by-brick modern accountancy will be built out.  Accountancy for the information age. Additional Information : XBRL is an Extra Fancy Knowledge Graph Logical Digital Twin of Financial Reports Essentials of XBRL-based Digital Financial Reporting  (Platinum) Mastering XBRL-based Digital Financial Reporting Legal Informatics

Increasing efficiency or productivity without maintaining quality or increasing quality is useless.  It really achieves nothing. But how exactly do you measure quality when you automate a here-to-for manual process ?  For example, how exactly do you measure the quality of an XBRL-based digital financial statement ? Without some sort of standardized set of quality metrics, comparing quality becomes an apples-to-oranges comparison. The increased role automation plays in accounting, reporting, auditing, and analysis is inevitable.  Creating quality metrics and automated verification guarantees will make sure that the automation does not come at the cost of a decrease in quality. Measuring quality is not about developing your own quality metrics with which you evaluate yourself. Measuring quality is also for process control purposes such as Six Sigma . Measuring quality is also about the ability to perform apples-to-apples comparisons against competitors in the market place. ...

How you make a digital process efficient and how you make an analog process efficient are different.  When a process is digital, different tools can be employed. Not having digital proficiency can be a liability. A process is a set of steps or actions that when consistently followed leads from some starting point to the completion of some objective.  A process can involve multiple workflows.  A process tends to repeat. A project is a type of process that does not repeat.  All projects are processes; but not all processes are projects.  The difference between a project and a process is how often you perform some set of steps or actions.  The more often a set of steps or actions is performed; the more benefit there is to spending time to gain control over a process. Workflow is a sequenced set of processes; it describes the flow of inputs to outputs as a result of a process. Making a process efficient is different than making a project efficient.  Proje...

A paradigm shift is occurring.  The territory is changing.  A new map is necessary. Trying to use your old map to understand the new territory will be unsatisfying. This shift is caused by the difference between how "realspace" (the real world, analog) and "cyberspace" (the internet, digital) operate. While many things will stay the same; it is also the case that entirely new business models and products are possible. Probably the biggest mistake one can make is misunderstanding the notion of intelligence and grouping "human intelligence" and "machine intelligence" together. I would contend that it is a fool's errand to try and compare machine-intelligence to human-intelligence.  They are not even in the same ballpark and they won't be for a long time, if ever.  The notion of "superintelligence" in computers is a fantasy. Computers are nowhere close to achieving superintelligence which I would consider as being human-level inte...

Way back in like 2012 when XBRL-based reports were first starting to show up in the SEC's EDGAR system, I started trying to extract information from those reports.  Being an accountant, I realized that not every reporting entity provides information in one consistent way.  I tried to create one Excel spreadsheet for extracting information from ALL XBRL-based reports submitted to the SEC but I could not make that work because of my limited programming skills.  For example, trying to make the code work for when a multi-step income statement was used (e.g. reports gross profit) and when a single-step income statement was used could work...until I tried to cover all styles of income statements; and then I became overwhelmed. That is when I changed to creating separate Excel spreadsheets for each style of reporting. This blog post has like 13 such Excel spreadsheets for extracting information from XBRL-based reports . That worked, but that approach is not flexible or practical...

Someone pointed out that a knowledge graph is just a fancy type of database.  Not a relational database, rather a graph database.  Unlike a relational database that structures data into tables, a graph database structures data into a graph.  Doing this enables new types of pattern-based capabilities with no programming overhead necessary. Think about something.  Why would ISO which has the SQL standard for relational databases spend the time to create a GQL standard for graph databases?  Why would they take the time to do that? (For the answer, see page 11, the section Relational Databases Lack Relationships ) XBRL is in effect a global standard approach to representing a knowledge graph. XBRL is an Extra Fancy Knowledge Graph .  Now, you could represent a knowledge graph using RDF, GQL, SQL, Prolog, Datalog, JSON, JSON-LD, or probably there are even more possibilities.  All of these possible approaches can be grouped into three buckets : Semantic ...

There is no one formal definition of informatics as far as I can tell.  This is one definition that I synthesized from other definitions that I like: Informatics  relates to the intersection of information, people, and technology and the practical application of computational systems; understanding how people will "live" in the digital realm within some specific area of knowledge that makes sense to users of that technology. Informatics is the conscious management of information, knowledge, and accumulated knowledge Informatics spans the knowledge accumulation process of an individual member (learning) organization (institutionalized knowledge) area of knowledge (professional knowledge; subject matter) Informatics has theories, principles, frameworks, and strategies that can be applied to solve information management problems Informatics is about harnessing the power and possibility of digital technology to transform data and information into knowledge that people use every d...