A question that comes up from time to time, and indeed one I pose to myself quite regularly, is how does the investment in industry event participation pay off. The first answer is usually that it is a way to generate business leads. This is true but it is also superficial. If that is the only goal then there are definitely more effective ways to proceed which are also less taxing. So what then might be another potential benefit that we could use to justify the investment of time and energy that is called for? At the recent Intelligent Content Conference (San Jose, February 2014), I encountered one of these other benefits in the form of a really good question.
My presentation was entitled "So You Want to be a Content Engineer" and, as usual, it included a handful of painfully precise definitions and then followed a somewhat unforgiving logic to wind up with "Do You Still Want to be a Content Engineer?" I will return to the topic of Content Engineering at another time. What I wanted to highlight was one of the questions that was put to me once I was done. It was posed by Tatiana Batova from Arizona State University (@TatianaBatova) and Rebekka Andersen from the University of California, Davis (@RebekkaUCD). The question stuck in my mind for a few weeks until, sitting in a coffee shop in Vancouver, I finally hit upon an answer.
So what was this vexing question? It was a simple one, as the hardest questions usually are. "How does your definition of Content as Potential Information fit into the commonly used Data - Information - Knowledge hierarchy?" There could simply be no better question to distract me. Squirrel! (I think that they did this on purpose.)
I have danced around an answer to this question on a few occasions. Below are some of the more germane posts that addressed aspects of this question. But none of them ultimately tackled this question directly and not finding a way to integrate these different elements into a single story was a sore point. So this question hit its mark to be sure.
- Perspectives on Data, Information & Knowledge
- The Anatomy of Knowledge
- The Great KM Divide
- Content in the Wild
- The Truth about Content
I will try to save my valiant readers the effort and aggravation of digging through these past posts and summarize the key parts here. Essentially, there has been a long-standing model called the Data - Information - Knowledge - Wisdom hierarchy referred to in short as the DIKW pyramid. In Perspectives on Data, Information & Knowledge, I tried to reset the foundation of this hierarchy so it could be more practically useful.
In The Anatomy of Knowledge, I took up some of the key concepts feeding into a practical definition of knowledge and this led me to make a clear distinction between the potential inherent in knowledge and the real-world decisions and actions that become possible as a result. I was separating potentials from actuals and in doing so I was following a venerable line of reasoning running from Plato through to Sir Francis Bacon and beyond. (Of course, many of my colleagues in the Knowledge Management industry found this logic to be a little too harsh, preferring more of a "mint jelly" definition of knowledge that simply tastes better.) I illustrated the dichotomy between potentials (blue) and actuals (green) in what I dubbed The Knowledge Dynamic.
In The Truth about Content, I set out to address a core weakness that afflicted the content management industry - namely that there was no consistent or particularly illuminating definition for the core concept of "content". Following the potential / actual dichotomy seen in the Knowledge Dynamic, I chose to define content as "potential information" - as something we manage in order to execute highly effective information actions. This definition of content suffers from being a little too abstract for the tastes of many content professionals but it does have some serious legs (it does for example flow natually from the latin etymology behind the word "content") and it can come to grow on you over time because it does help when we come around to "managing content".
So to get back to our core question here - how does content, when defined as potential information, fit into the Data, Information and Knowledge Hierarchy? It turns out that the answer was already present in the definition of content as potential information that, once transacted, becomes the content that the information transaction carries into the world for interpretation and application. So our Data-Information-Knowledge pyramid and the core of my knowledge dynamic becomes:
It turns out that there are several interesting implications of making content the connective tissue that connects information transactions to data resources and accumulates these transactions into the social construction of grounded knowledge that, in turn, serves as the basis for sound judgment and effective action. In effect, the placement of content in the role of intermediary in these models underlines why content is so important as a concept and why there is, and should be, a burgeoning industry focused on designing, managing and publishing content as expeditiously as possible.
And I think that this is ultimately what I have been trying to do over all these years: to identify exactly how content practices and technologies concretely fit into the big picture of what it is that enterprises are doing and perhaps even into what they figure that they are doing.
Without exhausting the many pathways I could take from this model, we can still explore a few avenues. For example, the above model might stand as a form of ideal for an enterprise that must draw upon a massive and growing base of high quality data in order to perform effectively and in order to build up a market-leading understanding of the domains of knowledge that are germane to its ongoing evolution. We might think of an enterprise that designs and builds complex engineered systems as falling squarely into this high-end scenario.
There are other scenarios that we can consider. For example, how about an enterprise that operates in a well understood area, say government operations or mass produced consumer products, where a big part of the success of the operation will depend on streamlining how much data and knowledge that their business activities demand. It is a common mistake to think of data, information and knowledge as undiluted goods that must be universally maximized. In reality, an effective business will acquire, manage and leverage only that data, information and knowledge that it really needs. Anything more than that is overhead and often a contingent liability.
And then there are businesses that are all but allergic to data, information and knowledge and their success frequently depends on how formuliac their operations can be made. Think of an auto dealership or a call center. In these scenarios, the range of information transactions that are supported is incredibly narrow and keeping it so is central to maintaining their margins.
What we see in this case is a winnowing down of the intellectual resources that an enterprise depends upon. The goal is increased efficiency. And depending on the type of business this strategy can be more than just advisable. It might be a matter of survival. But consider how and when this particular strategy will break down. What happens, for example, if the industry landscape changes violently? Will a streamlined enterprise such as this be able to adapt? Without access to deeper stores of knowledge and data, these types of streamlined enterprises frequently perish in the face of major environmental change. But once stability returns, newly streamlined enterprises will spring up and once again choke out those competitors that carry around too much knowledge. It is the circle of institutional life.
All of this helps to encourage us to continue exploring and elaborating a suitably robust concept of content. It turns out that by following this line of reasoning we advance our understanding of content quite substantially and this advancement still fits neatly within our core definition of content as potential information. Our model here helps us to see that inherent to the concept of content is its grounding in data resources and in established (and evolving) knowledge assets. Authority, established through fully contextualized references, becomes something that is difficult to separate from the notion that content is the potential upon which an enterprise can effectively execute many different information transactions and do so in many different contexts.
There is merit, I would submit, of exploring these implications and possibilities. There are many enterprises that really do depend on extensive and expanding arrays of knowledge assets and data resources and our work in the "content business" is a key piece in the overall puzzle of their future success. And there is merit in developing and deploying a more robust definition for content than might seem appropriate in more streamlined and selective scenarios. At this lower end of the spectrum, it is conceivably possible to get away with defining content as "words and pictures" or "what we put into the template fields". As we move up the spectrum, we quickly find that an overly simple definition of content leaves us high and dry when we have more important problems to solve. Understanding content as potential information, and seeing it as the connective tissue enlivening the data, information and knowledge dynamic, gives us a full range of possibilities. And this is what we want of our content.
And besides, putting content into its place within the hierarchy of data, information, content and knowledge provides us with a much more entertaining acronym than we had previously.