XBRL-based Digital Financial Reporting

Epistemic traceability is the unbroken, verifiable chain of evidence, logic, and data structure that proves how a system or organization knows its assertions are true. Maximizing epistemic traceability minimizes epistemic risk . When a system possesses industrial-strength epistemic traceability, information is modular and interlocking. This allows machines and human auditors to automatically verify the lineage of a claim, ensuring complete data provenance (knowing exactly where data came from, who moved it, and how it was transformed) from origin to output. Epistemic traceability shifts an organization from a culture of "trust me, it's correct" to a system of "here is the automated, structured proof that the information is correct." Traceability and trackability demonstrates control. Traceability and trackability proves compliance. Traceability and trackability provides defensible compliance . Fragmentation impedes control.  Fragmentation impedes proof of compl...

As explained by Kurt Cagle and Chloe Shannon in their article,   DataBooks: Markdown as Semantic Infrastructure , a DataBook is effectively a microdatabase.   A DataBook is a document a human can read, a data file that a computer can process, and a toolbox that caries its own instructions . A DataBook is a technique that enables data and an explanation to travel together in a data pipeline . One important part of the magic of DataBook files to understand is that a DataBook can also easily be read and interpreted by LLMs. Another part of the magic of the DataBook is that everything travels together within one file including:  data meaning rules queries documentation Finally, DataBook files can easily be versioned by Github and Gitlab.  Both Github and Gitlab support MD files which are both based on CommonMarkup . And so, there appear to be different "flavors" of MD files, but they are close. There are no separate files which can be forgotten or lost.  ...

Often, when you are deeply immersed inside a system you cannot see the problems of that system. The system I am talking about is "the enterprise".   By enterprise I mean any economic entity, entity, business, business entity, commercial venture, undertaking, firm, industrial organization, company; small, medium, and large that is organized and has some purpose or "mission" or "mandate".  Could be a for profit business, a not-for-profit organization, a government agency. The "problem" was less of a problem until artificial intelligence came along.  But artificial intelligence is exposing  the "problem".  And, it is less of a problem and more of an inability to take advantage of an important opportunity. What causes the inability to take advantage, the real advantage, of  artificial intelligence is messy data.  The AI Ladder points this out: "AI is one of the greatest challenges and opportunities of our time. It is poised to change...

Back in 2018 I noticed something that I referred to as a fact ledger, see Introducing the Fact Ledger . I described a fact ledger thus:  "A fact ledger is a new type of ledger that offers utility and leverage when accounting, reporting, auditing, and analysis is done in a digital environment. My thinking on this has evolved and now I see what I would call a business events ledger .  And I am not the only one that sees this.  Kurt Cagle has the notion of what he refers to as an event graph or event ledger (see  Coherence, Holons, and the Spinning of Plates ).  Another accountant seems to have a similar idea and created an "events ledger".  I don't have permission to show this to you at the present time. And so fundamentally, my original notion of a "fact ledger" is an "event graph" or "event ledger" in terms of a holon as described by Kurt Cagle, see my Example Financial Statement Holon .  Applying this specifically to financial statement...

The two articles, Against the Stream and Meaning is Not Metadata  highlight a problem that I have experienced myself and have tried to solve related to digital financial reporting by creating things like my Logical Theory Describing Financial Report . Communication between business professionals that need software to do something and technical professionals that build that software is hard.  Very hard.  But it is important to persist to make sure the foundation is right. Why? Because you cannot retrofit a foundation to fix a foundation. The first article points out a "shared fiction" that currently exists between business professionals and technical professionals. What is important to understand is the ramifications of that shared fiction.   One ramification that I have experiences is software that is overly flexible in undesirable areas which results in software that is harder to use than it really needs to be.  Flexibility makes things more complicated. ...

Heard a new term, spreadsheet monkey . A spreadsheet monkey  (a.k.a. Excel monkey , spreadsheet robot , data janitor) is a colloquial term for someone whose work revolves around repetitive spreadsheet tasks; like copying, pasting, reconciling, and formatting data; rather than engaging in higher‑level analysis or system design. The phrase captures the frustration of professionals trapped in manual workflows that could be automated or structured through better data integration. In essence, a spreadsheet monkey symbolizes the human cost of fragmented information systems: people acting as the “glue” between disconnected databases and reports. A spreadsheet monkey is someone stuck doing repetitive, manual, error‑prone spreadsheet tasks instead of strategic, automated, or model‑driven work. A spreadsheet monkey is someone who connects the " bucket brigade " of spreadsheets. This role will be changing.  Artificial intelligence will reshape accounting and finance . One new audit firm...