Okay, here we go again, let’s talk about a difficult term – Knowledge Management. Since its early launch around mid-1990s, many people are excited over a new muti-disciplinary subject called Knowledge Management (KM). So what is KM? This seemingly simple question has left many consultants, lecturers, and knowledge managers – lost for words. KM, according to the conventional wisdom, is about sending the right information to the right person at the right time. Surely if organisations are able to achieve this, productivity will go up and innovation cycle will be shorten. But before we get excited about the possibilities of what ‘sending-the-right-information-to-the-right-person-at-the-right-time’ can achieve, we need to think what it means and whether it represents KM as a whole.
There is a need to distinguish between information and knowledge here. Information is something that can be transferred from person to person without losing its context. For example: research paper on smoking is stored in C drive, the HIV patients in Singapore are x people. Knowledge, on the other hand, is deeply embedded in context, and therefore it is difficult to transfer from person to person without losing some of its context. In short, knowledge is situation-specific. For example: showing gory pictures of lung cancer can encourage people to give up smoking. This ‘knowledge’ may work on certain circumstances or people, but it can’t be applied universally. We need to get the context of the knowledge so that we know how to apply it.
With the help of technology, ‘sending-the-right-information-to-the-right-person-at-the-right-time’ can be achieved. Recent development in taxonomy, folksonomy, and metadata shows that technology can ‘push’ relevant information to the person who needs it, provided they tag the information. On the contrary, ‘sending-the-right-knowledge-to-the-right-person-at-the-right-time’ is difficult to achieve, because as mentioned above, knowledge is deeply embedded in its context. It follows that the right knowledge for one person may not be the case for another. Each individual has different beliefs, values, and assumptions that make it difficult to see what is the right knowledge and when to apply it at the right time. Culture and decision making bias distort facts, and thus influence how a person applies ‘the-right-knowledge-at-the-right-time’.
KM is about ‘sending-the-right-information-to-the-right-person-at-the-right-time’, as much as ‘sending-the-right-knowledge-to-the-right-person-at-the-right-time’. The implications are:
- KM involves designing and tagging information so that people can find the relevant one. Thus, knowledge managers need to develop corporate taxonomy / metadata, and ensure that the information is presented in user-friendly manner, so that people can quickly sum-up what the information is about. When information is shared, people can talk with one another about it. When people talk about the same information, they can create, transfer, and reuse knowledge – the three KM processes (see below for more details) – more easily since they have a common ground to (at least) begin the conversation. To put this in analogy, shared information is an entry ticket to KM.
- KM involves keeping culture functional in its environment and minimising decision making bias. Culture and decision making are inter-related. Culture is shared tacit assumptions of ‘how-things-work-around-here’. It is a result of interaction between the organisation and the environment where it operates. Overtime certain elements of corporate culture may become dysfunctional and disrupt the staff ability to apply ‘the-right-knowledge-at-the right-time’. For example: Bush administration’s decision to relocate critical resources from Afghanistan to Iraq, gives Taliban a breathing space and allows them to regroup. Bush administration was certainly not applying ‘the-right-knowledge-at-the-right-time’. Though cultivating functional culture can help to reduce decision making bias, culture can also magnify decision making bias. For example: the US government’s decision to cut spending during the great depression in 1929 felt right at that time, but on retrospect, it was a bad decision, the government should spend, instead of save, to revive the economy. The US government was influenced by the ‘we-should-save-during-hard-time’ culture then. So how can we minimise decision making bias? there is no ‘perfect’ answer. Nevertheless, tapping ‘wisdom-of-the-crowd’ can help greatly.
So how can we implement KM? by running its’ three processes:
- Create knowledge. This means new knowledge are created in the organisation. How can one create new knowledge? through conversations where ideas are exchanged and people can built on other’s idea. You can also create new knowledge by applying existing knowledge in a new context. For example: The Chile government, looking for ways to save their trapped miners, asks NASA for survival tips. The Chile government is applying ‘survival’ knowledge from outer space (original context) to underground mine (new context).
- Transfer knowledge. This means knowledge are shared / transferred in the organisations. Note that knowledge can’t be shared by simply sharing documents. It has to be shared through stories or other medium where knowledge buyer can see the knowledge context – or clarify with the knowledge seller directly. So, conversation and/or observation are also good ways of sharing / transferring knowledge.
- Re-use knowledge. To be able to ‘re-use’ knowledge, we need to be able to get ‘the-right-information-at-the-right-time’ so that we can use the relevant information, and apply the knowledge either in the same context or in the new context (where new knowledge is created). It also involves good decision making so that we can apply ‘the-right-knowledge-at-the-right-time’. Hence, we have to not only converse with diverse people, but also be aware of decision making bias and be able to tap into ‘wisdom-of-the-crowd’.
That’s how I see KM. See the picture below for illustration.
What do you think? Feel free to comment.
K IS THE EDGE OF SCIENCE and KM IS IN GREY AREA BORDERING “SCIENTIFIC KNOWLEDGE” and “KNOWLEDGEABLE SCIENCE
Dear Roan,
The following two articles taken from my Social Networking Site (SNS) “MOBEE KNOWLEDGE CoP” ( http://mobeeknowledge.ning.com ) and some of its K-base to somewhat extend could responded to your main issue on what is the essence of KM :
1. WE ARE ALL WITNESSES THE BIRTH OF KNOWLEDGEABLE SCIENCE BEYOND SCIENTIFIC ERA
Source : http://mobeeknowledge.ning.com/forum/topics/we-are-all-witnesses-the?xg_source=activity
Posted by Md Santo on July 6, 2010 at 8:32am
The piece of writing below is some part of my discussion with Douglass Weidner, Chairman at International KM Institute, Washington DC, USA through LINKEDIN’s “KM EDGE Group” discussion regarding the “Definitions of Knowledge Management” :
……Never before in human history of science, we could hardly define a learning object as in defining K as well as KM. There area about over 60 KM definitions we could find in the literatures! It seems something must go wrong with this phenomenon as far as our mindset is scientific mindset and supposing we have “treated successfully KM” as “Scientific Knowledge”
Since 18th century, the beginning of Scientific or Newtonian era, the evolution of DIKW as supposedly continuum phenomenon also taken place. Thanks to the development of IT, the evolving of DIKW history among others could be noted in the form of statistical methodology, data base techno development, content management system, information management system etc. But, following the evolution of science and technology since involving knowledge as main learning object in which KM as its highlight began prominently exposed, we’re facing many constraints to make K as well as KM becoming truly Scientific Knowledge. Some of its “symptoms and signs” showing we’re not succeeding in making KM as an integral part of Scientific Knowledge are the following ( major sources cited from Seth Early, 2007) :
• No acceptable and workable KM definition so far
• The essence meaning of Scientific Knowledge still “blurring”, marked with some problems of KM eg relevant knowledge is in the eye of the beholder, no “right answers”, language is imprecise, meaning is vague, centralized control and creation of knowledge does not work….
• Scientific knowledge as well as KM also challenged by : lack of clarity, absence of meaningful measures, lack of understanding of knowledge flow, complex and changing business environment, knowledge needs are context specific and differing perspectives lead to lack of alignment
• Not as in Data or Information, K as well as KM considered as having characteristics of Complex (Adaptive) System that have so many interrelated components that predicting outcomes becomes difficult if not possible.
Further, by nature, Knowledge totally different with Data and/or Information. In the process of Knowledge formation, Knowledge evolved as emergent behavior inside human body as complex system, having consciousness and free will (mind and value) as well as behaving dynamically as subject. It is contrary with Data and Information which is exist outside human body, still treated as object, passive, static and having no consciousness. This is the overlying condition why KM seems in the grey area, behaving as pseudo science and still difficult for us to define KM universally accepted.(Visit http://mobeeknowledge.ning.com/forum/topics/we-are-the-knowledge-hy… – WE ARE THE KNOWLEDGE : HYBRID DEFINITION OF KNOWLEDGE).
Science and technology developed following DIKW evolution pathway in which KM evolved proceeding after Information Management System era. The role of Knowledge in emerging KM giving new paradigm to human history in seeing the human scientific mindset since the last three centuries. The impact of new paradigm of Knowledge surely will give wide impact on the development of science and technology in the near future.
We’ve noted at least there are five basic implications resulting from new paradigm of Knowledge that most likely will giving wide impact on our mindset ( http://mobeeknowledge.ning.com/forum/topics/five-basic-implications-of-new ). From this standpoint we come up with general explanation on this phenomenon ( http://www.scribd.com/doc/29259487/KNOWLEDGE-R-US-not-KNOWLEDGE-R-OURS and http://mobeeknowledge.ning.com/forum/topics/knowledge-r-us-not-knowledge-r?xg_source=activity )
For further anticipation we’ve systematically developed the analogy with Human System Biology (HSB) applied to KM model called as HSB-based KM (visit our K-Base http://delicious.com/mobeeknowledge/humansystembiology (24 articles) and take attention especially to http://mobeeknowledge.ning.com/forum/topics/the-epistemology-of-human ) and fundamentally rooted from our DI – KW model in which DI separated from KW, not DIKW as a continuum considering that Knowledge is human knowing tools achieved as inborn as well as acquired (visit our Knowledge-Base on DI – KW Model http://delicious.com/mobeeknowledge/di-kwmodel – 12 articles). Finally, our outcome are two kinds of KM definitions as I’ve mentioned before, Contextually-driven KM definition and Content-based driven KM definition ( http://www.scribd.com/doc/28696847/How-to-re-postulating-the-paradi … and http://mobeeknowledge.ning.com/forum/topics/why-knowledge-management-has and link http://mobeeknowledge.ning.com/forum/topics/human-enlightment-staging )
BTW, I understand regarding the motto “WIIFM” (“What’s in it for me?”) you mentioned. It is also my motto when working in “routine condition not facing a new paradigm” . But in process of evolving KM as part of changing of human history since Newtonian era where we’re likely to be the witnesses the birth of “knowledgeable science”, the situation totally becoming different. We as KM practitioners should be cautious in seeking workable definition unless we will be trapped in pragmatic mind set …….
2. http://bit.ly/9sapId (“THE EDGE OF SCIENTIFIC BOUNDARY : KNOWLEDGE MANAGEMENT DOMAIN”) : in matrix diagram describing the above article
3. Some of Mobee Knowledge-base related to your main issue :
On Human System Biology-based KM http://delicious.com/mobeeknowledge/humansystembiology
On DI – KW model http://delicious.com/mobeeknowledge/di-kwmodel
On KM Definition http://delicious.com/mobeeknowledge/kmdefinition
On KM Tools, KM Process Framework and KM Standards Culture and Value http://delicious.com/mobeeknowledge/kmtools+kmprocessframework+kmstandards
On Knowledge Map http://delicious.com/mobeeknowledge/knowledgemap
Md Santo
Interesting article. I was originally surprised by your initial definition of KM as an information management system, but it all came together with the “sending-the-right-knowledge-to-the-right-person-at-the-right-time.”
I could suggest a couple of further roles that IT systems could have in the KM process beyond information transfer:
1. As a forum for virtual informal collaboration: IT systems that allow for informal conversation, notes, etc can help to some degree at codifying tacit knowledge (particularly if it is read by people in the same trade or community of practice where the context is understood).
2. As expert finders. i.e. the sources of tacit knowledge.
Also perhaps one other consideration for the processes of KM might be “remove obsolete knowledge.”
Anyway, it was a good read and a very sound article. Good job.
I just came across a web site called kagoon, which impressed me a lot and I believe this would be the direction to which km will go. it is basically about having one huge open taxonomy linked by the users to everything in the world.