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STRATEGY & INSIGHTS
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There is no consensus on the definition of knowledge and philosophers have yet to agree after debating the topic for millennia. I will briefly describe the debate around the epistemic concept of knowledge to show how tacit knowledge is present in the everyday life of each of us as a necessary perspective before discussing the seminal works of Michael Polanyi and Harry Collins in [blog#2].
"Gold there is, and rubies in abundance, but lips that speak knowledge are a rare jewel. [Proverbs 20:15]" this quote is attributed to King Solomon, known for his wisdom, who argued how rare knowledge was. We are still in quest of this rare jewel without clarifying yet what knowledge is.
There is no consensus on the definition of knowledge and philosophers are anyway close to find an agreement after debating the topic for millennia. Plato's standard definition of knowledge is credited as "justified true belief," or JTB for short. That worked well until 1963, when the American philosopher Edmund Gettier wrote a famous three-page paper to show how the standard ternary JTB definition is inadequate. It is a necessary but not sufficient condition for knowledge. Gettier showed with two counterexamples that it is possible to form a justified true belief that does not result in any knowledge. The quest for the missing term is now known as the Gettier problem, which is still unsolved.
So, instead of a general definition of knowledge, let us restrict our scope to knowledge for knowledge defined within Information Technology. The standard view is the DIKW knowledge pyramid, where data is raw numbers and facts, information is processed data, knowledge is authenticated information, and wisdom is the possession and use of practical knowledge.
This view sees data as a prerequisite for information and information as a prerequisite for knowledge. It assumes a sequential process model where something simple (data) is converted into something complex (knowledge). Very nice, but the knowledge pyramid view is misleading and does not survive a careful evaluation, such as that done in a critique of the DIKW hierarchy.
Problems start with data. Data are never neutral. They are not only subject to misuse and abuse but are also often inaccurate, poor, intentionally false, or just fake. It is far better to consider that raw data do not exist and that potential uses, expectations, and context influence even the most elementary perception and theoretical constructs.
Data exists only as a solution to a practical problem and brings social, political, moral, and ethical connotations that determine what to collect and how to collect. Why are decision-makers so worried about data privacy and sensitivity if the data were raw? In the words of Geoffrey Bowker, informatics professor at the University of California, Irvine, "Raw data is both an oxymoron and a bad idea." Raw data carries a false sense of purity and untouchedness, obscuring the necessary processing before data collection.
Some researchers have even proposed a reverse hierarchy where knowledge must exist before information can be formulated and well before data can be measured to form information. Data can emerge if a meaningful structure, or semantics, is fixed and then used to represent information —only after knowledge and information are available.
In this view, information is explicit or articulated knowledge, and knowledge is "personalized, subjective, information related to facts, procedures, concepts, interpretations, ideas, observations, and judgments" [Alavi et al. 2001], somehow tacit, non-articulated knowledge.
To recap, knowledge is procedural knowledge or knowing-how to do something, and information is propositional knowledge or knowing-that some proposition is true. In this context, if knowledge is static, then the only knowledge we are interested is propositional, and then knowledge is just information. Otherwise, if knowledge is intentional action, then knowledge is a dynamic process, a way to the truth, i.e., procedural (know-how) in any case, very different from propositional knowledge (know-that).
Saying that knowing-that and knowing-how are different forms of knowledge does not mean they are separated and activated independently. Gilbert Ryle, in The Concept of Mind (1949), argues that "Knowledge-how cannot be defined in terms of knowledge-that" but also that "Knowledge-how is a concept logically prior to knowledge-that." Thus, knowledge is a multilayered dynamic concept where propositional and procedural knowledge has multiple relations of interdependence that are not easily separable but integrate nicely to produce intelligent actions. Of course, there is no reason to think humans are the only example of intelligent beings showing intelligence. Animals and machines shall do so. In the words of Harry Collins [blog#2], "We show that the boundary between humans and machines is permeable, at least insofar as humans find reason for acting in machine-like fashion. We explore the possibility of shifts in the position of the boundary between humans and machines: If humans change the way they acted, they could make the boundary between themselves and machines disappear."
For the rest of this blog, I consider propositional knowledge equivalent to explicit knowledge and procedural knowledge equivalent to tacit knowledge:
Declarative Knowledge refers to facts or information stored in the memory that is considered static in nature. Declarative knowledge, also referred to as conceptual, propositional, or descriptive knowledge, describes things, events, or processes; their attributes; and their relation to each other.
Procedural Knowledge refers to the knowledge of how to perform a specific skill or task. It is considered knowledge related to methods, procedures, or operation of equipment. Procedural knowledge is also referred to as Implicit Knowledge or know-how. Contrary to common opinions, procedural knowledge can be stored in a machine.
What is tacit knowledge, exactly? Tacit knowledge is in everyday life. It is what humans do "without anyone telling anything to anything or anyone" [Collins 2010].
John Wiseman's film, Die Hard 4.0, gives us a crispy example. The script is well known. John McClane (Bruce Willis) and a young hacker Matt Farrell (Justin Long), join forces to take down master cyber-terrorist Thomas Gabriel (Timothy Olyphant) in Washington D.C. In an initial sequence, McClane complains that the phones don't work; they are all dead. But Matt replies that the phones work, and he only needs to reprogram the network and link it into the old sat-coms. John is intrigued and asks Matt how he could connect the phone to the network “How do you know all this stuff?”. The answer is insightful " I don't know. There is a lot rattling around up there. I couldn't tell you." This dialogue is an excellent example of tacit knowledge in action. Matt knows how to reprogram the network but does not know how to explain it to John. John does not follow what Matt is saying but knows what to do with the information discovered by Matt. That is how things go between humans since tacit knowledge collects all those things that we know how to do but we do not know how to explain to others.
Many different human activities involve what psychologists call tacit knowledge. Riding a bike, for instance, is something many of us can do, but without knowing how. Mastering a martial art or cooking also involves tacit knowledge. Another expression of tacit knowledge is how experts become experts in their field and develop higher skill levels by learning what they know. The examples are countless. The common point is that these activities need more than an instruction manual to be mastered. Other human activities, such as understanding a joke, require tacit knowledge and social awareness to judge if the joke is funny. Nobody learns how to understand jokes from manuals! We all have some intuition or background knowledge to perform these skills. To have the proper understanding is what we identify as having tacit knowledge [Lowney 2012].
We are talking of a knowledge that is acquired via personal experience and is difficult to transmit to others, differs from explicit or propositional knowledge (know-that) because it cannot be stored, measured, or exchanged but can be transformed, codified, and automated, incorporates everything we know how to perform but cannot articulate in words, is related to procedures and practices of experts, learned via practical experience, context, and instruction.
A better way to define Tacit Knowledge is to contrast it with Explicit Knowledge and show the key differences between Tacit Knowledge Vs. Explicit knowledge, as follows:
In a broad sense, tacit knowledge is the knowledge we get while doing something (e.g., bike riding, car driving, cooking, teaching, standing, etc.). Still, expressing in a language or being conscious of it isn't easy, even if it is interrelated with explicit knowledge. For example, the recipe for cooking a pizza is explicit knowledge, but knowing how to prepare a tasty pizza requires tacit knowledge, i.e., the knowledge that guides you on what to do and in what circumstance.
“If only HP knew what HP knows, we could be three times more productive.”
Lewis Platt, former CEO, HP
Lewis Platt the former CEO of Hewlett-Packard, gives us another excellent example of tacit knowledge when he said: "If only HP knew what HP knows, we could be three times more productive." This famous aphorism applies to many other organizations, as pointed out by Jesse Wilkins: “Not knowing what your organization knows is definitely a recipe for rework, stagnation, and inefficiency. Encouraging the sharing of employee knowledge to serve enterprise objectives remains an important goal for information professionals. Organizations with successful content-sharing cultures focus on removing barriers to information flow” [Tacit Knowledge Vs. Explicit Knowledge]. We are dealing here with a kind of knowledge that is stored in the heads of experts but never transformed into a company knowledge base or handbooks. Many organizations do not recognize tacit knowledge since it is quantifiable only indirectly as a loss when senior workers leave, but tacit knowledge is critical to run a business successfully.
In the next blog, I will introduce the two principal researchers that have pioneered Tacit Knowledge Michael Polanyi and Harris Collins.
Personal views and opinions expressed are those of the author.
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