We operate in an era where most of our decisions and work is based on the information at our disposal. Organizations are continually competing to gain knowledge on the basis of this information. With these, come more complex products and services meaning that learning will continue to be an inevitable process in the organizational life-cycle.
This is becoming increasingly discernible that in order for organizations to gain the competitive edge, their knowledge assets need to be able to create knowledge and transfer this knowledge in a manner that is difficult for competitors to imitate. This is further confirmed by Nonaka who defines organizational knowledge as "the capability of a company as a whole to create new knowledge, disseminate it throughout the organization, and embody it in products, services, and systems."
What Is Knowledge Management
Kakabadse et al, view the chain of knowledge flow as data-information-realization-action/reflection-wisdom (see Figure 1). Data comprises observations or less meaningful facts drawn out of context of use(Zack, 1999).
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| Figure 1: Chain of knowledge flow |
Information is an upshot of committing data within some meaningful content, oft in the form of messages(Zack, 1999).
Knowledge is. thus, a “justified true belief”. Which essentially what people believe and value on the basis of conglomerated information (messages) through experience, communication of inference (Blacker, 1995).
An exercise of discipline or action is connoted by ones need to acquire the needful information and to realize the value of this information (Kakabadse et al., 2001)..
Therefore, realization can be regarded as information put to productive use and we may gain wisdom through action and realization, because according to Pascual-Leone, as cited by Kakabadse et al., (2001), “knowing how to use information in any given context requires wisdom”
Kakabadse et al, identify different disciplines which are very influential in the field of KM as:
- Philosophy, in defining knowledge; prominent being
- Cognitive science (in understanding knowledge workers);
- Social science (understanding motivation, people, interactions, culture, environment); management science (optimizing operations and integrating them within the enterprise); information science (building knowledge-related capabilities);
- Knowledge engineering (eliciting and codifying knowledge);
- Artificial intelligence (automating routine and knowledge-intensive work) and
- Economics (determining priorities).
- This has also led to a plenitude of literary definitions for KM, in fact, a review by Hlupic et al. (2002) as cited by Bouthillier et al. (2002) identifies 18 different definitions of KM. While there have been many attempts to define KM from a theoretical perspective these efforts hardly reference the relationships between KM and IM (Bouthillier et al., 2002)
How does KM differ from IM?
The difference between KM and IM has been a topic of discussion. More often the two terms “knowledge” and “information” are used interchangeably.
This essay focuses more on the practicalities as opposed to the theories or philosophies associated with knowledge, or better yet, the management thereof. Therefore we shall adopt Nonaka's (1994) definition of knowledge, as cited by Alami M. et al (1999) as "a justified personal belief that increases an individual’s capacity to take effective action."
This topic does not lend itself much to discussing the difference between data, information and knowledge since it is a debate which many authors have debated and one which extends beyond the scope of this topic, however we will adopt Vance's (1997) over-simplified definition of “information as data interpreted into a meaningful framework whereas knowledge is information that has been authenticated and thought to be true".
This topic does not lend itself much to discussing the difference between data, information and knowledge since it is a debate which many authors have debated and one which extends beyond the scope of this topic, however we will adopt Vance's (1997) over-simplified definition of “information as data interpreted into a meaningful framework whereas knowledge is information that has been authenticated and thought to be true".
There are a few gap theories which attempt to justify the metamorphosis of data into information and that of information into knowledge. Again most, of these efforts paint a fuzzy picture especially when trying to explain how information becomes knowledge, or how knowledge becomes information (again), but Alami M., et al (1999), provide this quotation of Nonaka and Polanyi. “Information becomes knowledge once it is processed in the mind of an individual, and knowledge then becomes information again once it is articulated or communicated to others in the form of text, computer output, spoken, or written words or other means”.
Who Owns Knowledge Management?
Galliers R. et al, (2001), "argue that the field of Information Systems should no longer be distracted from its natural locus of concern and competence, or claim more than it can actually achieve". They consider Knowledge Management (KM) and Knowledge Management Systems (KMS) to be one of the latest fads alongside BPR which have been adopted by IS but yet have little to offer. On the contrary and with a little agreement to their assertions I believe that although KM as a concept and as a practice is more about people management, a lot can be attributed to IS and it (IS) has a crucial role to play in the realization of KM within organizations. This is also especially true when considering that, data and information are some of the key elements when it comes to the implementation of KMS. Other than that, and on the same breath, one could also reason that IS deals more with the management of data than the management of information because the function of IS is more about the processing of data than it is about the information articulated from the processing of these data. IS may not be the ultimate custodian of KM initiatives within organizations, but it surely plays a pivotal role in the identification, implementation and management of the technology and systems essential for the fruition of these initiatives. As a matter of fact, because of the ever-present competitor factor implicit in KM as a concept, Strategic Management, R&D and Customer Relations play as big a role as Human Resources and to a lesser extent Finance, but this essay focuses more on the significant role that IS has to play in KM.
Where does IS fit in?
IS is an enabling function which provides the platform and technical tools and resources and facilitates the implementation of KM initiatives within organizations, which makes me believe that KM would still exist without IS or technology element albeit it would be a difficult endeavor.
From an IS/IT perspective, the transformation of data management into information management is a rather smooth process since computers unreservedly lend themselves well to information systems. However things become more complex as we attempt to elicit knowledge out of these information systems. This is confirmed by Galliers, R. et al, “Whereas most people agree that data and information may exist outside humans, supporters of the community view of knowledge would argue that knowledge can never be separated from the knower and thus never stored digitally”
Sternmark D, argues that "all knowledge is tacit, and what can be articulated and made tangible outside the human mind is merely information"
Figure 2 illustrates Lawton’s (2001) Knowledge Management System Architecture
Lindvall et al, state the necessity of a collection of technologies for authoring, indexing, classifying, storing, contextualizing and retrieving information, as well as for collaboration and application of knowledge in order to support KM. The robustness of the back-end and the user-friendliness of the front-end are also cited as the basic necessities of a software for KM
The bottom-most layer in the architecture, Information and Knowledge Sources, represents reservoirs for explicit knowledge.
The knowledge repository is therefore supported by tools in the Low level IT infrastructure
Classifying and indexing tools become useful in organizing knowledge fitting into the organizational content resulting in the creation of a “knowledge map” based on the taxonomy of the organization. At the personalized knowledge gateway level, knowledge is distributed to those who need it through portals.
According to Lindvall et al, it is challenging to draw a distinction between IT and KM.
When looking at Lawton’s (2001) architecture model, KM is considered to reside in the upper layers while IT comprises the lower layers, albeit the bounds are blurry.
Tools and Technologies
According to a survey conducted by Skyrme. J, the first initiative of many KMS' involves the installation or the of Intranets and adding the best practice or "expert databases" while the second leading drive of KM strategies involves the creation of new knowledge, innovation and the transformation of this knowledge into valuable products and services.
Knowledge management as a concept is nothing new to IS because the 1970s saw an increased interest in "expert systems" and artificial intelligence although they fell short of expectations. Skyme. J, describes as an era where we "where we tried to make computers think, rather than using computers to help humans think".
Knowledge management as a concept is nothing new to IS because the 1970s saw an increased interest in "expert systems" and artificial intelligence although they fell short of expectations. Skyme. J, describes as an era where we "where we tried to make computers think, rather than using computers to help humans think".
Listed below are some of the key technologies and tools identified by Skyrme. J, which may facilitate KM initiatives with organizations
Intranet, Internet
The omnipresent Internet protocols which allow us to access “any information, any where, at any time”.
Groupware - (e.g. Microsoft Sharepoint, Lotus Notes)
These provide us with discussion databases and allow us to access the ‘organizational memory’, as well as current news feeds in areas of interest
Browsers and Client Software
These act as front-ends to information in many formats and many of the other knowledge tools such as document management or decision support.
Intelligent Agents
Due to the "problem of information overload intelligent agents can be trained to roam networks to select and alert users of new relevant information". He makes an example of a related technology which "British Telecom have found can summarize large documents, retaining over 90 per cent of the relevant meaning with less than a quarter of the original text".
Document Management
Much of explicit knowledge is shared using documents, especially structured documents, are the form in which much explicit knowledge is shared, because these turn out to be active knowledge repositories.
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Figure 2: Knowledge Management Sytem Architecture |
Figure 2 represents Lawton’s (2001) widely referenced knowledge management system architecture where sources explicit knowledge are handled by the lowermost layer. This explicit knowledge lives database records and email messages. This bottom layer is supported by standard authoring tools such as word processors, mail and file servers and database management systems.
The infrastructure layer is also supported by file servers, intranets, Internet and document and content management systems.
This knowledge that is to be captured needs to be relevant and serve the needs of each organizational context. Therefore, indexing and classifying tools prove handy when creating a “knowledge map” based on the corporate taxonomy.
At the next level, tools are required to support, data, the discovery of knowledge and collaboration services.
Whenever, users and application (e.g. e-Learning, competence management, intellectual property management and CRM’s), need access to information, portals can be used to distribute this knowledge.
This model of architecture does not differentiate much between IT and KM tools, however it considers the all the layers from ‘knowledge repository” upwards to be more KM aligned, while IT comprises the layers below with no clear boundaries.
Conclusion
Practitioners should not impose traditional information processing models on KM initiatives no more than they should build to predict the future. However these knowledge management systems should be built to anticipate surprises based on past experiences and lessons learned. Almost every knowledge management initiative should take note of IS and related technologies as a crucial elements towards its success. Although it may take a while for computers to match humans when it comes to acquiring and transfer knowledge, due to the humans's advanced reasoning capabilities, computers are good at processing data. Knowledge Management Systems should thus continue to provide us with the relevant facts so that we can make better decisions quicker.
References:
- Lindvall. M., Rus. I., Sinha. S.,2003,“Software Systems Support for Knowledge Management”, MCB UP Ltd.
- Sternmark. D., Information vs. Knowledge: The Role of intranets in Knowledge Management, Knowledge Management Group, Viktoria Institute
- Galliers, R. D. and Newell, S., “Back to the Future: From Knowledge Management to Data Management”, in Proceedings of ECIS 2001, Bled, Slovenia, 2001, pp. 609-615.
- Skyrme. J., “Knowledge Management Solutions – The IT Contribution” , David Skyrme Associates Limited
- Hlupic, V., Pouloudi, A., Rzevski, G., 2002, "Towards an integrated approach to knowledge management: `hard', `soft' and `abstract' issues", Knowledge and Process Management, 9, 1, 90-102.
- Zack, M.H., 1999, "Managing codified knowledge", Sloan Management Review, 40, 4, 45-58.
- Korac-Kakabadse, N., Kouzmin, A., Korac-Kakabadse, A., 2001, "From tacit knowledge to knowledge management: leveraging invisible assets", Knowledge and Process Management, 8, 3, July-September, 137-54.
