Digital Financial Reporting

The Accountant Quits is a blog and podcast related to accountants and change. This is worth checking out.

Algorithmic Business Thinking (ABT) is an idea created by MIT that helps business professionals and computer professionals communicate. This video, Algorithmic Business Thinking with Paul McDonagh-Smith, discusses ABT.

I get the impression that Algorithmic Business Thinking builds on Computational Thinking. That is mentioned a couple of times. ABT is important as we transition from the industrial economy into the digital economy, we are in the fourth industrial revolution now.

The following are the four cornerstones of ABT: 

• Decomposition: Taking larger problems or challenges and breaking them down into a series of smaller problems or challenges that are easier to manage and solve.
• Pattern recognition: Rather than trying to build everything from scratch, use things that you have done before or that someone else has done before to leverage in order to solve problems and overcome challenges.
• Abstractions: Filter out unnecessary details out of the problem or challenge in order to focus on what is important.
• Algorithms: Humans and machines working together to on an ordered, sequential set of actions in order to solve a problem or overcome a challenge.

Algorithmic Business Thinking uses the four ideas above in order to solve problems and overcome challenges.

Blackline has published a special addition book, Modernizing Accounting for Dummies. (If that link does not work, try this). This is a description of the book provided by the book:

Modernizing Accounting For Dummies provides a road map to help you move to modern accounting. After reading this book, you’ll understand the benefits of making the switch, as well as strategies to drive finance automation.

Modern accounting is an evolutionary process rather than a revolution. The book points out, "It’s a journey to automate, rethink, and rework ever more processes over time, using data as your guide, technology as your tool set, and people as your change agents."

There are three pillars to modern accounting: 

• Applying purpose-built automation
• Adopting Continuous Accounting
• Moving to a unified, collaborative process

 Enjoy! I refer to this as Computational Professional Services.  Here is the standards-based automation method I am using.

One thing that I would point out here is that Blackline is a proponent of Lean Six Sigma, see Get Lean on page 28 (PDF page 33).  I have been promoting the use of Lean Six Sigma's principles, philosophies, and techniques for years.  See this document.  My area of study for my MBA was Lean Six Sigma (at the time it was called World Class Manufacturing Techniques).

David Tuck wrote an interesting article, The Emancipation of Expert Knowledge. His premise is that today's professional services are expensive, antiquated, rigid, opaque, provide a primitive customer experience, and end up with dissatisfaction on both sides of a professional services transaction. David summarizes with this paragraph: (emphasis added by me)

The above are all flaws affecting the client due to the one-sidedness of today’s grand bargain. But that’s not to say that working in a professional service firm is a vocational utopia. The bargain is broken on both sides and there is much cause for professional dissatisfaction. The expensive fees frequently bring with them unreasonable client expectations that you feel compelled to bend over backwards to cater to. And whilst you develop immense intellectual capital as a professional, you don’t develop any of your own intellectual property.

David seems to be concluding that professional services should be a product rather than a service.

I agree with David.

And so how to you turn this expert knowledge which is being delivered as professional services into a product? How do you let professionals that develop expert knowledge own their intellectual property?

This is explained in detail in my paper Computational Professional Services. This is demonstrated in the working proof of concept expert system for creating financial reports a software engineer and I created called Pesseract. A framework is provided for effectively setting expert knowledge free in my method. You can see this working yourself by experimenting with Pacioli which is a rules/logic/reasoning engine that performs work and verifies information logic.  Or, try Luca or in a couple of weeks try the new cloud-based version of Luca.

Auditchain seems to add an additional important layer (or layers really) to everything that I have been doing for the past 20 years with regard to accounting, reporting, auditing, and analysis. More on this later. Listen to this podcast to understand what Auditchain is up to.

Here is expert professional knowledge that I have represented in machine-readable form using the global standard XBRL: Financial Reporting Schemes XBRL-based Knowledge Graph. 

As I can best explain it; Auditchain is converting that information into NFTs which increases trust and provenance, using these NFTs within an incentivised gaming model, processing all this with their Pacioli rules/logic/reasoning engine, leveraging Merkle trees and Merkle proofs, storing things using immutable digital distributed ledgers in a blockchain, providing decentralized orchestration, and literally redefining how expert knowledge can be used in the financial accounting, reporting, auditing, and analysis supply chain.

This could all provide a massive increase in efficiency if thousands or even millions of professionals can effectively share their expert knowledge using a machine-readable form and effectively collaborate with software engineers. This could be a new economy, a new platform for delivery of professional services. This video that relates to gaming, Economy of the Metaverse, helps one understand the possibilities here.

Double-entry accounting was built for this 600 years ago.  The way I see it, this is an example of a new way accountants can monetize their professional knowledge which serves an example for other groups of professionals that may also choose to set their expert knowledge free.

In a prior blog post I discussed the three primary problem solving logic paradigms. In that blog post I provided a graphic which I have updated to the following graphic because the RuleML folks have, appropriately, changed a term they are using.  Here is the new graphic: (note the change in the title of the blue circle from "Knowledge Graph" to "Semantic Web")

The reason for the change in the blue circle from "Knowledge Graph" to "Semantic Web" is because all three of the above problem solving logic paradigms are approaches to implementing and processing knowledge graphs.

And so, a knowledge graph can be implemented using any of the three primary problem solving logic paradigms: 

• Semantic web: (The Knowledge Graph Cookbook: Recipes that Work explains how to use this approach)
• Graph databases: (Graph Databases explains how to use this approach)
• Logic programming: (The Power of Prolog explains how to use Prolog; Learn Prolog Now! explains how to use Prolog; Expert Systems in Prolog is a book on this topic; Building Expert Systems in Prolog is a free PDF; Pacioli is an implementation of this which uses Prolog; Pesseract is an implementation that mainly uses SQL)

Which is the best approach? I cannot tell you; I don't have the proper background.  Here is a comparison of the semantic web approach and the graph database approach. Here is a comparison of PROLOG and graph databases. Either of the three approaches can work, each has a different set of pros and cons.

You could also use other standard approaches or even proprietary approaches to implementing knowledge graphs to store information and then processing the information within those knowledge graphs.

Keep in mind that we are talking about knowledge graphs, not data graphs.  This will help you understand the difference between data, information, knowledge, insight, and wisdom:

Another really good graphic is the following which someone posted on LinkedIn:

The focus here is not on the data; rather the focus is automating the process of gaining insight and wisdom as best as possible.  There are many syntaxes that can be used for this. And you need more than data to achieve this objective.

XBRL is a technical syntax. The XBRL technical syntax can be effectively bidirectionally converted between any of the three primary problem solving logic paradigms.

What matters is that the logic provided within any of those technical syntaxes be sufficient to control the information.  This is what my method is all about.  Yes, you need some standard physical format to move information from point to point.  But you need standard meaning/logic to make sure you are not moving garbage around. Controlling your process is critically important to achieving success. This is science.

If you have any insights or comments, please send them to me.

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LogTalk

LIquid Part 1

LIquid Part 2

Prolog: An Influential Language for Knowledge Management and Ontology Engineering

Knowledge Graphs: Introduction, History, Perspectives

Knowledge Representation and Ontologies (Excellent)

Why Semantic Knowledge Graphs are the Only Way to Build an Enterprise Data Fabric

GSQL, TigerGraph

Enterprise Knowledge Graph Trends 2021

RDF Levels Advantages of Labeled Property Graphs

TerminusDB

Fluree (Web 3.0 database)

Katana

Why Graph Databases Will Win

Cambridge Semantics

Knowledge Graphs (Engine B)

Knowledge Graph Terms (Kurt Cagle)

Datalog in Practice (Video)

pyDatalog

Native vs Non-Native Graph Databases

At Its Core: How Is a Graph Database Different from a Relational One? 

PROLOG Programming for Artificial Intelligence

ACID vs BASE Database Transaction Models

Logic and Semantic Networks