Knowledge as a Reusable Asset

Knowledge is an asset.  In fact, knowledge is our greatest asset.  Knowledge is an accumulation of your education and experience.  Your skills and abilities are knowledge assets.

Knowledge can be represented in machine-readable form.  As an example, here is knowledge of AASB 1060 which is a financial reporting scheme represented as an XBRL taxonomy.  That knowledge is effectively the terms, associations between terms, rules, structures, and such.

According to the Business Rules Manifesto, article 4; business rules should be represented in declarative form: "Rules should be expressed declaratively in natural-language sentences for the business audience."

Rules, in my view, can be represented or supported by an NFT or non-fungible token. To better understand NFTs, please see this document that explains the top 30 use cases of NFTs in the enterprise.

Knowledge must be managed.  Machine readable knowledge needs to be curated to keep it current.  This curation and management has value of machine readable knowledge is valuable because the machine readable rules are valuable.  This management and curation of rules takes effort.

Machine readable rules drive things like expert systems.  For example, see this expert system for creating financial reports.  If you don't understand expert systems, think of how CAD/CAM software changed how architects and builders work. Expert systems is an approach to constructing software.  These expert systems need to be easy for business professionals to make use of. And they will be.

Software can leverage what is known as a rules engine to process the machine readable rules to provide the functionality that drives something like an expert system.  You don't necessarily need a rules engine per se, but a rules engine is a very efficient way to write the code that processes the rules that drives the knowledge based expert system. But here are reasons to use a rules engine.

Auditchain Pacioli is a rules engine that is purpose built for processing business reports and in particular financial reports.

The flywheel is turning slowly, but it is picking up speed steadily. Listen to this NPR story about the role of skill, experience, and experimentation in innovation: Rebel With a Cause. Why it pays to break the rules in work and life.  The best experimentation is done with those that have skills and experience in the area that they are experimenting in.



Comments

Post a Comment

Popular posts from this blog

Getting Started with Auditchain Luca (now called Luca Suite)

Image
(Note that a newer, production version as opposed to only the development version , of Luca Suite is available) The Innovator's Dilemma points out that disruptive innovations often look like toys that are doing everything wrong. Auditchain Luca , which is part of the Auditchain Suite , is a unique approach to creating a knowledge graph .  To experience using Auditchain Luca, have a look at the Getting Started with Auditchain Luca instructions. Be sure to grab the Excel and JSON import files used for this getting started exercise.   This getting started exercise uses one of several different approaches to getting information into a tool for constructing and viewing professional quality knowledge graphs .  Additional getting started guides will be made available over time. Auditchain Luca might not make sense to you if you don't understand the paradigm shift that is occurring and if you have not shifted your mental map. For the next step in your journey, consider t...
Read more

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

Professional System for Creating Financial Reports Leveraging Knowledge Graphs

Image
The document Professional System for Creating Financial Reports Leveraging Knowledge Graphs explains a new approach, a new paradigm, for creating financial reports.  The document provides a practical, nontechnical description of a professional system for creating provably high-quality financial reports where the report creator is using financial reporting schemes (such as US GAAP and IFRS) where report creator is permitted to modify the report model which leverages global standard knowledge graphs represented using XBRL. This new approach is useful, it is highly novel, and the details were highly complicated to pull together.  This is certainly not an incremental innovation.  It is definitely a disruptive innovation and it could very well be a foundational innovation. You can decide which for yourself. Foundational technologies have two dimensions that need to be considered: novelty and complexity. The more novel the technology is, the more effort needed to ensure users ...
Read more