What is useful knowledge?

Useful knowledge, or rather, the idea of useful knowledge, is central to my concept of the innovation supply chain. But what is knowledge really? And how to identify its useful portion? Obviously, I’m not the first to ask these questions, and I’m certainly not the only one. If you simply google the word knowledge, you’ll get a pretty broad set of ideas, and they are not entirely coherent. But essentially, the majority of those ideas fall into two different camps: one holds a more abstract, philosophical view, the other takes a more concrete, practical approach.

Two approaches

Philosophers have pondered over knowledge, its origins and characteristics, for millennia. Already in the fifth century BCE, Plato had proposed the idea of knowledge as justified true belief (JTB). This approach to develop a coherent, comprehensive definition of knowledge found wide acceptance, but has been proven to be still incomplete and in need of adjustments. If you are interested in the current state of epistemology as the study of knowledge, I do recommend the entry in the Stanford Encyclopedia of Philosophy for a thorough overview. It has certainly served as a source of inspiration for my thinking on this subject. But for the specific context of the innovation supply chain, I need something more tangible and accessible; hence I’ll focus on the more practical approach.

That second approach seeks to describe –rather than define– knowledge within the framework of the data-information-knowledge-wisdom hierarchy (DIKW). Expanding on the information theory presented by Claude Shannon in 1948, this is a comparatively young approach. Where JTB addresses knowledge as a highly abstract, philosophical notion, the DIKW hierarchy uses a mathematical or technical foundation. Within this hierarchy, each layer builds on the previous, each layer takes in additional context, each layer increases understanding. It’s like a value chain, adding potential benefit and utility at each layer.

At the foundation of the hierarchy, you’ll find data: discrete, objective facts about the world. Data provide the raw building blocks for our access to and understanding of the real world.

At the second layer, there’s information, or linked data. Information analyses the connections between data points, it categorises and groups data, thereby giving us a snapshot of a tiny facet of the world. Information helps us see relations and understand what is happening around us.

Next is knowledge, i.e., organized information. Knowledge integrates different pieces of information, forming a whole concept. Knowledge gives us a map of the world, it makes the underlying patterns visible and offers a mental model of how things work. Up to this layer, the hierarchy presents the elements of inductive reasoning, building models and concepts based on past and present observations.

At the top of the hierarchy, wisdom presents applied knowledge; and that has a future dimension. Seeing the overarching principles, understanding why gives us orientation: when applied to a specific purpose, wisdom helps us make decisions.

Granted, all of this is a bit dry. So let’s take …

… an example

Let’s look at Alexander von Humboldt, the Prussian philosopher-explorer-geographer-botanist (I recently enjoyed Andrea Wulf’s Humboldt-biography The Invention of Nature, which offers a stimulating account of his truly impressive œuvre). In 1799, Humboldt and his partner-in-research Aimé Bonpland started what became a five-year expedition to South America, which led them from the Caribbean seashore to the highest peaks of the Andes. That path ultimately went all the way through the DIKW hierarchy as well.

The expedition itself focused on data collection and information gathering. Humboldt and Bonpland made observations and recorded measurements (data) of all kinds; and they carefully identified the relations between these data points (connecting elevation, temperature, humidity in a specific location with the plants living there). Partly during the expedition, partly in the later evaluation and analysis, it was especially Humboldt who integrated that information further. He drew out knowledge about the underlying patterns, as he described geographical areas of comparable climate conditions and developed the concept of isotherms. On that basis, he went on to identify overarching principles: the ideas climate zones and their horizontal layering both have their origin in the Andes. Humboldt’s unique contribution to wisdom is the understanding that nature is not a mechanical system (as was believed by most natural philosophers at his time); rather, nature itself works as an organism; everything is interconnected, nothing exists in isolation.

The results of Humboldt’s work had many valuable implications, for example practical advice on which crops to grow where, or the understanding that deforestation is the result of human intervention. What is more, he inspired generations of natural philosophers and researchers after him, as he opened up the avenues for what became modern climatology and ecology.

A description

Let’s get back to idea of useful knowledge then. As I’ve pointed out in the previous post, the flow of useful knowledge – its generation, dissemination, and application – is the core of the innovation supply chain. And in this context, you can consider ‘useful knowledge’ to be synonymous with ‘applicable knowledge’: the knowledge that has been or could be applied in coping with novel situations and circumstances, and in solving problems in novel ways. That term then comprises the layers of wisdom and knowledge in the DIKW hierarchy: wisdom as the knowledge that has already been applied in the past, and knowledge as the larger pool of concepts and ideas that could be applied in the future. Obviously, the applicability of knowledge is contingent upon the specific problem to be solved: for any concrete situation, the applicable knowledge is only a subset of all existing knowledge and wisdom. But it’s important to note that there’s no such thing as useless knowledge: even though a specific concept or principle might not be applicable in many contexts, it will still be useful in some.

And that gets us to a dilemma for innovation policy. If you consider the design of an innovation supply chain as structuring and organising the flow of useful knowledge, then the task ahead is actually a double task; with two contradictory objectives:

  • In the general sense, you’d want to make as much knowledge as widely available and accessible as you possibly could. That’s necessary, because you cannot predict where the next problem appears and where a suitable solution might come from. Broad and wide sharing of knowledge should accelerate innovation. And that’s entirely appropriate for knowledge and wisdom in general.
  • However, in the specific sense of dealing with a concrete problem, you’d prefer that this broad knowledge flow is reduced to the useful, the applicable portion. To focus the innovator’s attention and minimise any distraction, you’d want to suppress any knowledge that is not relevant for the problem at hand; but that seems an impossible task for you cannot anticipate which concepts, principles or ideas would really yield the novel problem-solution that you are after.

That general objective clearly applies to the dissemination of the flow of useful knowledge, whereas the specific objective sits on the border between dissemination and application. For an effective and efficient innovation supply chain, both need to be fulfilled. Neither of them is easily addressed, and both together present a genuine challenge for devising coherent policy on innovation.

Such a description of useful knowledge is of course only another step, another building block in developing the idea of the innovation supply chain. More to follow soon …

 

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