Digital Financial Reporting

Taxonomies, ontologies, schemas, theories, and models are not really all that sexy; but they are essential, and often forgotten, when it comes to getting artificial intelligence to fundamentally work or scaling artificial intelligence and get it to perform useful work.

Machines cannot read, interpret, make sense of, or otherwise understand data that has no structure.  It is that structure that provides the power.  A well designed taxonomy, ontology, schema, of a model are fundamental to teaching machines to understand patterns in data and information.

Subject matter experts with knowledge, skill, expertise, and training in an area of knowledge are critical to building taxonomies, ontologies, knowledge graphs, schemas, theories, and models correctly.  Clean data is also critical to this pattern detection and documentation process.  This process actually has a name, it is called sensemaking.

Sensemaking is the process of determining the deeper meaning or significance or essence of the collective experience for those within an area of knowledge. Sensemaking involves:

• Looking for patterns in information.
• Making connections among different things.
• Synthesizing lots of information and categorizing it into small chunks.
• Think about the big picture.
• Think about the "why" of a situation.
• Organizing and untangling things.

It is this sensemaking that yields the machine-readable taxonomies, ontologies, schemas, knowledge graphs, theories, models, and meta-models that make artificial intelligence fundamentally work and scale and perform useful tasks.

The machine-readable information makes no sense really unless you have a tool that can process the machine-readable information.  Pacioli, which I now describe as an XBRL-based Financial Report Knowlege Engine, is such a tool.

The global standards based machine-readable information by itself, like my examples, make no sense if you don't have a tool that can process the information.  A tool like Pacioli really makes no sense without all that machine-readable information.  But when you put the two together correctly, what appears to be "magic" happens.

But it is not really magic, it is just logic and math.

Subject matter experts in the area of accounting, financial reporting, auditing, and analysis that have the skills, experience, training, and vision will put these pieces together.  All this will get better, and better, and better over time as the machine-readable information evolves and the tools improve.

If you want to understand, read the Essence of Accounting, Financial Report Knowledge Graphs, and the Seattle Method.  Become a bounty hunter.  Help built the accounting oracle machine.

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Logical Schema

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