Knowledge Technology

Knowledge Technology

DOI: 10.4018/978-1-4666-4727-5.ch004
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This chapter surveys several classes of technologies and indicates which kinds of knowledge flows are enabled and supported relatively better and worse by such technologies. The authors look at common problems associated with the most prevalent and prominent KM technologies and then discuss interactions between such technologies and the knowledge life cycle. The discussion turns subsequently to examine expert systems technology, which addresses knowledge directly. This is followed by a discussion of simulation technology, which enables the development of tacit knowledge through practice in virtual environments. The chapter concludes with five knowledge technology principles and includes exercises to stimulate critical thought, learning, and discussion. As a note, the authors do not consider the kinds of emergent knowledge phenomena enabled via social media technologies here, but they devote the whole of the book’s third section to this topic.
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Knowledge Technology Problems

Have you ever worked with technology in an organization that failed to produce the intended results? Technology—particularly information technology (IT)—forms the foundation of many KM projects in practice and offers great potential in terms of enhancing knowledge flows. As noted repeatedly above, however, few information technologies even address knowledge as the focus or object of flow. Over-reliance on IT has sounded the death knell for myriad KM projects.

Ask yourself why most IT fails to support KM well. We have learned that knowledge is distinct from information. Yet we have learned also that knowledge builds upon information (and vice versa). It appears that flows of information are necessary to support flows of knowledge, but such former flows are insufficient to enable the latter ones. In this way IT to enable flows of information may be necessary to support flows of knowledge, but many KM programs rely naïvely upon such technology as sufficient to effect knowledge flows. IT is capable of transmitting signals (e.g., electrical waves and pulses across networks, photonic patterns from displays, acoustical patterns from speakers), but conversion of such signals into data, information or knowledge takes place within the minds of people receiving such signals, not the electronics of IT systems. To ensure flows of knowledge, one must do more than deliver signals. Thus knowledge flows require more than just IT.

Next ask yourself how some kinds of IT support KM better than others do. Some technologies claim to automate work processes. Many people think of automation as the ultimate in terms of IT evolution. The word processor application automates many document processing tasks such as formatting, spell checking and filing, for instance. The workflow application automates flows of documents through an organization, as another instance. Intelligent software agents can search networks autonomously and retrieve information automatically from distributed and disorganized sources (e.g., the Internet), as a third instance. Related shopping “bots” can identify and select automatically certain products and services based on lowest-price, as a fourth instance.

Nonetheless, people are required still to create the messages that become documents and to complete the work as it flows into their workspaces. People are required also to read and understand documents that are created and to build cumulatively upon the work of others in workflows. People are required further to determine which information retrieved by agents is relevant and to decide whether non-price product and service attributes are sufficiently compelling to override bots’ purchase recommendations.

In each of these instances, and as a general rule, the technologies automate some activities within workflows but not all of them. The people in an organization perform most workflow roles requiring knowledge—particularly those involving experience, judgment and like capabilities dependent upon tacit knowledge. This leaves to IT the largely systematic, clerical and procedural roles, for which requisite knowledge can be formalized explicitly (e.g., via computer software). Hence we see again, as in Chapter 2, how knowledge uniqueness and IT are intertwined tightly.

Other technologies such as computer databases and online information repositories are excellent at organizing, storing, manipulating and facilitating the query and retrieval of data and documents, respectively, but we know that even data organized within a database must be placed in context and used to enable direct action to become knowledge. Likewise, even documents organized within a document repository must be read, understood and used for action before they can be considered to represent knowledge. As noted above, such technologies are clearly important in, and in many respects necessary for, supporting knowledge work in the organization, but they are not sufficient to enable knowledge flows. Again, the people in an organization maintain responsibility for most workflow activities requiring knowledge, particularly tacit knowledge.

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