Improve The Value Of Knowledge Through Labeling And Categorization

Here is the question posed by Arnold King (

"I am interested in the phenomenon of knowledge specialization.For example, in medicine, there are many more specialties and sub-specialties than there were 30 years ago. My guess is that if libraries are still using classification systems, there should be a lot more categories. My guess is that major universities have many more departments than they did 30 years ago. I think this is important in economics because I think that businesses and economic systems have become harder to manage as a result. In short, the leaders tend to know less about the specialized information that is further down in the organization, because the amount of the latter is increasing (I conjecture). I would like some quantitative indicators of the rate at which new knowledge categories or sub-categories are being developed. Do you know how to even go about searching for such indicators?"

I spent some time thinking this over. I may have ranged too far from the question and I know I am preaching to the choir here, but thought the issue of economic value creation and knowledge categorization would benefit from a bigger picture perspective. The problem with the question is that knowledge is fractal in nature. It is endlessly complex and classification depends on scale and perspective. It’s not a matter of “there should be more categories… “; there are more. It simply depends on where you look and your perspective.

A category is a short hand representation for a lot of other stuff. Fractals describe natural phenomenon through mathematical constructs. They contain endless complexity - zooming in at any level reveals more and more intricate structures. Knowledge classification and representation has a similarly complex nature and categories are only meaningful given a specific scale. Categorization and classification has to take a perspective - that perspective implies a certain level of abstraction.

If we look at a field such as biology, the Medical Sub Headings (MeSH) classification contains the following: Biology [G01.273]

  • Botany [G01.273.118]
  • Computational Biology [G01.273.180]
  • Developmental Biology [G01.273.200]
  • Ecology [G01.273.248]
  • Exobiology [G01.273.295]
  • Genetics [G01.273.343]
  • Marine Biology [G01.273.476]
  • Microbiology [G01.273.540]
  • Molecular Biology [G01.273.595]
  • Etc.

According to MeSH, Marine Biology is an end node in the classification. But Marine Biology contains dozens of subfields. A brief survey of subfields reveals many ways of organization and classification of finer levels of granularity.

Communities of Practice can coalesce around extremely arcane branches of knowledge. I once cited a taxonomy of ‘liquid crystal phases’ that contained terminology that represented very specialized expertise. 2. Liquid crystal phases 2.1 Thermotropic liquid crystals 2.1.1 Nematic phase 2.1.2 Smectic phases 2.1.3 Chiral phases 2.1.4 Blue Phases 2.1.5 Discotic phases 2.2 Lyotropic liquid crystals 2.3 Metallotropic liquid crystals.

 To a specialist in the field, these terms probably represent “common” knowledge. However to the rest of the world, this is about as arcane as you can get.

What does this mean in the business world? Whenever we are brought into organizations to build classification systems for content and knowledge management applications, we are frequently asked if we have any “standard” taxonomies that we can repurpose for the business or industry. The answer is no, we will leverage existing organizing principles (both internal and external), but there is no “standard” way of organizing knowledge even for a specific process in a specific industry. Knowledge is endlessly complex and how you organize it depends on the nature of how that knowledge is to be applied to specific circumstances, the level of detail and the level of experience of the knowledge ‘consumer’.

So back to the original question about ‘the rate at which new knowledge categories or sub-categories are being developed’ - this may imply that there is an ‘official’ place where new categories are being developed.

There are a couple of ways to consider this. Standards bodies and are making an effort to classify knowledge and ‘official’ definitions as they relate to things like interoperability and specifications. As we know, every field of commerce, engineering, manufacturing, science, etc, has numerous associations and standards-making bodies. A quick search of the internet reveals hundreds of international, national and regional ‘types’ of standards organizations and literally tens of thousands of member organizations and associations. Each of these entities will likely have differing ways of classifying their collective knowledge.

Universities also classify and organize their knowledge, but for the purposes of packaging and delivering knowledge in specific fields of study and moving their knowledge consumers - the students - through various stages of competence. That knowledge is classified and organized for that consumption pattern.

Libraries of all sorts (University, Public, Corporate) attempt to do the same and build collections based on the needs of their audiences, institutional or organizational goals and philosophy of their curators (after all someone needs to make judgments about the collection). The Library of Congress has a much more challenging goal of serving a broad and far reaching mandate and has a historic structure that looks a little odd in places- (computer science being sandwiched between “Instruments and Machines” and “Elementary Mathematics” in the Mathematics subclass.)

Businesses attempt to organize and classify information, expertise, collective intelligence as a competitive advantage and a route to solving customer problems. Those categories are specific to market niche, customer needs, core competencies, and so on. How management leverages that knowledge and steers the organization to new opportunities and value creation is the fundamental economic problem.

But this is less about managing categories and classifications and having a direct handle on knowledge assets than it is about allowing for ’self organizing’ around solutions.

Classification provides the handles on knowledge to allow groups of specialists to find answers, share solutions and leverage information in order to develop new products and strategies, and address problems around getting the most from scarce resources while producing value.

Product management does not need to know how a group of engineers eliminate a particular bug in a product. The engineers need to know who to ask or what reference to utilize. Product management needs to design a product that works and satisfies the needs of the market. Manufacturing needs to solve their problems of high quality, cost effective physical production of that product, etc.

In every group of specialists, knowledge is created, classified and leveraged to create economic value. If they don’t have the correct classifications, or use inconsistent classifications or don’t organize that information so others can use it (causing delays, poor quality or higher costs) then maximum value is not being extracted from resources (time, energy, money, physical plant and equipment, materials, etc). (Self organization requires some constraints - it does not imply a free for all. Fractals allow for complex patterns to arise out of repetition and variation on set rules. The rules are simple and few, but there are rules.)

So back to the problem that Arnold points out: “I think this is important in economics because I think that businesses and economic systems have become harder to manage as a result. In short, the leaders tend to know less about the specialized information that is further down in the organization, because the amount of the latter is increasing”

I assume the problem he is referring to is knowledge specialization. Of course the more a leader knows about the intricacies of the organization and its knowledge, expertise and competitive advantages, the more effectively they can lead. But it is more about creating the correct conditions for self organizing groups of experts to solve problems.

In complexity, there is a sweet spot between chaos and control where value emerges. Too much chaos and nothing gets done. Too much control and there are no new solutions to problems. But what are necessary are mechanisms to encourage self-organization. Labels and classifications tell the organization what is important and allow people and teams to find and leverage knowledge that is created in one part of the organization and contribute to the overall goal or value creation. In the “Biology of Business” John Clippinger states that a manager’s job is to encourage knowledge flows. Knowledge flows are encouraged by use of tags that tell the organization what is important.

Therefore labeling things at the appropriate level of granularity for the team and the problem is the essential requirement for self-organizing behavior and emergent knowledge. Giving a team of developers attempting to create a new computer program the strategic goal of the organization from the annual report is not the correct level of granularity of information that will help them produce value. (It may provide guiding principles, but won’t help them fix bugs). Similarly providing the marketing group with source code is not going to help them position the product in the market (unless they are selling to a highly technical audience.) I realize I did not answer Arnold’s the actual question about the rate of creation of categories. But the bigger question is how this affects the ability to manage a business.

Knowledge flow creates value, rather than just classifying, storing, and organizing knowledge. Flow is encouraged by the correct classification by providing the labels so that different parts of the organization can recognize knowledge assets to apply in the course of performing work and producing value.

The bottom line is that economic value is created not by understanding where all the knowledge is and micromanaging activities, but by providing broad constraints on targets, problems to solve, competitive differentiation, values, and resources and then creating the right circumstances that allow teams of people to focus knowledge and expertise on solving problems. Knowledge classifications are part of the tools for communicating value and telling the organization when trial and error has produced something that can be reused and applied to solving other problems.

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Meet the Author
Seth Earley

Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.