When it comes to Knowledge Management, it’s important to know and understand the various types of Knowledge and how they overlap with the work performed and solutions that can be developed and applied in the Information Technology (IT) industry.
The Three Types of Knowledge
In knowledge Management (KM) there are three generally accepted types of Knowledge that have existed and been discussed for many decades. These Knowledge Types include:
- Tacit Knowledge,
- Implicit Knowledge, and
- Explicit Knowledge.
Tacit Knowledge (TK) is considered that knowledge which cannot be communicated or explained. This type of knowledge is mostly compared to concepts such as the genetic encoding that allows a living being to know how to do something without being able to explain how it knows. For example:
- How does a living entity know how to learn from and process patterns?
- How does a living entity know to breath?
- How does a developing child know how to mimic sound via voice or mirror hand gestures based on visual feedback?
- How do newborn animals like colts, fawns, and calves know how to stand, right after birth?
- (As silly as a it sounds…) How does a domesticated dog that has never been around other dogs know to try and bury its waste with its hind legs?
While not 100% proven, yet, results from genetic research continuously lead us in a direction which allows us to safely assume or hypothesize that such knowledge is genetically encoded in the DNA structures of living beings, where it is passed from parents to children. Whether such assumptions are right or wrong, they are mostly irrelevant for the purposes of Enterprise Knowledge Management (EKM), which is about solving real business and operating problems. This is because TK can’t be explained or measured and there is absolutely no documented proof that it can be used to positively improve enterprise knowledge.
Implicit Knowledge (IK) is that type of knowledge which we can think about. In other words IK exists in the mind and represents that kind of knowledge which has not been articulated to or manifested in a an objectified, physical and explicit form, outside the mind. For example, thoughts a person has that have never been documented or for which no physical representation has ever been created are considered Implicit Knowledge.
Explicit Knowledge (EK), sometimes also referred to as Objectified Knowledge (OK), is that type of knowledge which has been represented in some way, shape, or form, outside the mind. Examples include:
- Virtual creations such as data, digital documents, data visualizations, and models on computers.
- A physical or mechanical creation that was driven from ideas (e.g. paper documents, tools, vehicles, computers, robots, furniture, toys, etc.).
- Virtual representations signals (e.g. audio/sound, visual/site, magnetic, thermal, etc.
The rule is simple: If you put it in a form where you can communicate it, see it, hear it, taste it, smell it and/or feel it (i.e. you can objectify it), it is in the realm of Explicit Knowledge.
Defining Enterprise Knowledge Management
Enterprise Knowledge Management (EKM) includes the intentional solutions put in place to support any combination of one or more Knowledge Activities that are performed for the benefit of the enterprise. Such Knowledge Activities include but are not limited:
- Analysis & Analytics
- Transmitting and/or Receiving
- Searching and Browsing
Notice that all activities overlap 100% with what can be done with data.
Enterprise Knowledge, for the purposes of EKM, can exist in any one of three unique states:
- Mental: Virtually manifested, in the mind (the example for Implicit Knowledge).
- Computer: Virtually, manifested in a computer (an example of Explicit Knowledge/Objectified Knowledge) For example: data structures and electronic/digital documents.
- Physical: Physically manifested (an example of Explicit Knowledge/Objectified Knowledge). For example paper documents, magnetic tapes, film, manufactured physical products, etc.
Establishing a Knowledge Architecture to support EKM
Roles such as Enterprise Architects, Solutions Architects, and Engineers are often given the responsibilities for designing, establishing (and sometimes even operating and supporting) a comprehensive Knowledge Architecture for the specific purpose of meeting the Knowledge Management needs of their enterprise, either for parts and/or for the whole enterprise. Such solutions address processes, data, tools and technologies, skills, etc., that are established and woven together to support any combination of one or more Knowledge Activities.
Data is Knowledge
As it pertains to the benefits of Enterprise Knowledge Management (EKM), the IF4IT abides by the assumption that all Implicit and Explicit Knowledge is data. All data can be structured and transferred, regardless of whether that data is virtual or physical.
Data is the foundation for all communications permutations. For example, taking into account people and systems:
- System to System requires data,
- System to Person requires data,
- Person to System requires data, and
- Person to Person requires data.
In other words, if we extend Nickol’s decision tree (Nickols, 2000), which was using in the introductory image above, we are saying that all knowledge can be treated as data for EKM:
- Explicit Knowledge: Exists outside the mind as data, which is nothing more than an arrangement of natural elements in a way that is meaningful when later absorbed the generating human or a another receiving human.
- Implicit Knowledge: Exists in the mind as data. Scientists have proven that when you think your brain fires synapses and also moves chemicals and other organic matter in an organized manner. It is very safe to assume that, just like outside the body, the body is structuring natural elements into structures that are somehow meaningful (and if not meaningful at least observable) to the beholder.
- Tacit Knowledge: Theoretically is data, too, but has little to no measurable bearing on EKM. Think of it as that genetic encoding that is so deep in organic matter that we, as humans, are still a long way from being able to explain.
Because of this assumption, the following are all just different forms of data and are treated as equivalents:
- laying out blocks to spell the word cat
- seeing the word cat spelled in your mind
- hearing the word cat spoken
- laying out computer bits to spell the word cat
- creating smoke signals to create the word cat
- generating audio signals to construct the word cat
- turning on different pixels with different frequencies to see the word cat on your monitor or tv screen
In all cases, smaller knowledge constructs (even down to molecular particle levels) are constructed into other knowledge constructs to create different representations of the same concept.
Why is Data so important to EKM? The answer is simple… Things like data quantities, qualities, costs, activities, technologies, processing times, etc. can all be measured. Anything that is not measurable almost always has far less use to the enterprise.
Knowledge Structures / Knowledge Constructs
Every attempt at structure of any element (e.g. electrons, molecules, metals, plastics, wood, etc.) is a manifestation of knowledge that communicates something meaningful from the originator/sender/transmitter/source to the receiver/target.
When dealing with Explicit Knowledge, as it pertains to Enterprise Knowledge Management (EKM), it is critical to understand and know how to properly apply Knowledge Structures.
- Understanding Knowledge Structures
- Understanding Taxonomy Types and Uses
- Using an Enterprise Knowledge Map to Achieve Better Knowledge Management
Citations / References
- Nickols, F., 2000. The knowledge in knowledge management. The Knowledge Management Yearbook, 2000–2001.