Knowledge as a Product

A business model explains how a company makes money. A moat is a defensive measure and explains why that business model keeps working even when competitors attack it. Without a moat, a business model is fragile. Without a business model, a moat is meaningless.

Companies build moats in order to make their success hard to compete with, hard to imitate, and hard to erode over time. A strong moat lets them keep customers, maintain pricing power, and generate durable profits.

One type of moat is knowledge. But knowledge only becomes a moat when the knowledge is encoded. Raw expertise is not defensible. Encoded expertise is. Knowledge is encoded when it is turned into metadata, rules, theories, ontologies, taxonomies, schemas, governance frameworks, process automation logic algorithms. When knowledge becomes machine‑readable, enforceable, and extremely hard to replicate you create a moat that artificial intelligence cannot erode. Knowledge can be organized within a knowledge organization framework.

Doing this well makes knowledge operational. Thinking that artificial intelligence is going to figure all this out on its own is a fools errand. Sure, machines can help...but make no mistake; this is hard work.

One new moat is encoded knowledge.  A new business model is providing that encoded knowledge which is curated, governed, trusted, and of high quality to software applications, including agentic artificial intelligence or generative artificial intelligence.

New business models are emerging which will deliver super-useful machine-readable knowledge products that will drive modern software applications.  This software will enable teams of humans and machines to get work done.  It is my view that accountants, auditors, and analysts will create such knowledge products to package and sell their hard earned skills and experience.  In my view, NFTs will very likely play a role in packaging knowledge products.

To understand what a knowledge product is, you first have to understand the difference between data, information, knowledge, insight, and wisdom. Briefly;

Data is raw and unprocessed and tends to be understandable only in one context, usually only locally.  Information is data in context and has been processed and organized; information can provide global oriented meaning. Knowledge is refined, trusted, and actionable information that has been processed, organized, and/or structured in some way making the information super-useful.  Insight and wisdom come from applying knowledge to some specific situation.


Finally, it is important to differentiate "meaning" and "understanding" correctly. Meaning is not something inherent in just words or objects. Meaning is consciously and deliberately produced through shared human systems, shaped by context, and sustained by communities of stakeholders.

Understanding is when something makes sense to you well enough that you can use it, explain it, or see how it fits into the context with or connects to other things you know.

Meaning is intersubjective.  Understanding is individual. Meaning enables understanding. Understanding validates meaning. Humans understand, machines do not understand.  Machines only interpret.

Data Product:

A data product is generally a reusable raw and unprocessed data asset, engineered to deliver a trusted dataset to a user for a specific purpose. It integrates data from relevant source systems, processes the data, ensures that it is compliant, and makes the data accessible to those with the right credentials.  The focus of a data product is raw, unprocessed data which can be numbers, text, or some sort of measurement.  Examples of a data product are stock market data, website traffic logs, a list of social media posts.  The data might be delivered in the form of a CSV file, spreadsheets, an API, an XML file, RDF, JSON, or a DataBook.

A data product generally provides the foundation for further analysis and insights. For example, you might use a data product in some analysis model that you have created.

Information Product:

An information product tends to provide processed information to the information product’s user. Information products are often used to monetize knowledge.  Information products are organized and interpreted data which provides context and meaning. Information is generally provided in a way that the information can be consumed such as reports, dashboards of information, articles, and in the future very likely machine-readable knowledge graphs of high-quality information.

Information products help their users do things like understand trends, patterns, relationships within the information. An example of an information product might be the analysis of stock market data for a specific company or companies within a specific industry. A financial report of a company can be considered an information product.

Knowledge Product:

A knowledge product is refined, trusted, and actionable information that has been processed, organized, and/or structured in some way or put into practice in some way making the information super-useful.  The information is ready to use.  The knowledge is derived from expertise, research, lessons learned, skills acquired.  Knowledge products allow the user of the knowledge product to make informed decisions or better decisions.

Knowledge products provide insights, best practices, good practices, expertise which was derived from information, skills and experience. Knowledge products tend to answer "why" and "how" types of questions.  Knowledge products can be delivered in a wide variety of forms such as training material, templates, guides, checklists, tutorials, artificial intelligence models, spreadsheet models.  In the future knowledge products will, I believe, include machine-readable knowledge graphs that, preferably, use a global standard format and are proven to be of high quality per a logical schema.

Knowledge products empower their users, and the user could be a human or a software application such as an expert system for creating financial reports or your tax return.  AI-powered software agents or assistants need machine-readable knowledge to operate.  Subject matter experts in some area of knowledge will create these knowledge products.

Knowledge products are actionable; they should equip the users of the knowledge product to make informed decisions or take effective action in the moment. This could be in the form of recommendations of some sort, knowledge about best practices, or maybe a checklist or step-by-step guide.  Knowledge products are driven by the skills and expertise and deep understanding gained within some specific field.  Knowledge products do not provide a surface level understanding, they offer true insights and understanding.

Knowledge products tend to be tailored to the audience or a specific group of users and address the needs and challenges of that audience or group.  Knowledge products might teach new knowledge.  They might provide expert consultations to tap into the expertise of specific individuals or organizations.  They could provide personalized recommendations or solutions to very specific problems.

Decision Product:

A decision product is a system designed to consistently produce high‑quality decisions for a specific business function, using data, information, rules, models, metadata, schemas, and context. A decision product is not a tool that helps humans decide; it is a product that makes decisions.

A decision product is not a chatbot bolted onto a workflow. A decision product is a system that takes messy reality, partial information, conflicting priorities, human heuristics, business constraints, and other such inputs and produces actionable choices within explicit guardrails and constraints, measurable outcomes and feedback loops, are auditable and traceable to understand precisely why a decision was made, and all within clear boundaries provided in advance.

This graphic from this article helps one understand the relationship between these different sorts of products which also could be delivered in the form of a service.

Remember, unless information or knowledge is encoded in some way it cannot be exchanged effectively and consumed by a machine. But simply encoding something does not make it information or knowledge; it also must be correctly decoded.

Additional Information:

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