3 Frameworks for knowledge technologies
3.1 A knowledge management technology framework
In the introduction to a book on knowledge management technologies, Borghoff and Pareschi (1998) described a framework for organisational memory that has been developed within Xerox to promote understanding of the roles and interplay between different technologies (Figure 4).
While the example technologies and techniques described in the different boxes will evolve, the broad differentiation of roles is a useful one to bear in mind. Borghoff and Pareschi map their framework to the concepts of explicit and tacit knowledge as follows: explicit knowledge is contained in ‘Knowledge repositories and libraries’ (top left in the figure); tacit knowledge is embedded in ‘Communities of knowledge workers’ (top right); meta knowledge is in tools for ‘Knowledge cartography’ (lower box). They term the links between these the ‘Knowledge flow’, which is construed as distributing documents to the right people at the right time.
In the light of what we have said above, you could (by now, hopefully) take the authors to task for the uncritical use of terms such as ‘knowledge flow’ from ‘tacit knowledge’ (we now understand that knowledge may be ‘sticky’ – Brown and Duguid, 2001; and that it may never be possible to ‘convert’ tacit knowledge to other forms), but their contribution is with respect to the role of technologies, rather than epistemology. Remember also that Borghoff and Pareschi's ‘documents’ could refer to a (possibly multimedia) digital artefact which may even be compiled automatically on request from diverse data sources.
The ‘bird's-eye view’ provided by Borghoff and Pareschi's framework encapsulates insights from many other analyses and empirical studies of organisational knowledge, and provides a useful framework for thinking about how technologies relate to each other. It highlights the fact that traditional information systems have a role to play in knowledge management (repositories for codifiable information), but that there are important links to people which emphasise the fundamentally social, tacit, dynamic nature of knowledge as it is generated, shared and analysed by knowledge-intensive communities within organisations. The challenge is to integrate these resources and their interconnections.
In the course of a single task, we may draw on all the knowledge resources shown in the framework in Figure 2, switching rapidly from one to another. For instance, interpreting a stable, formally structured document (for example, a company report or a technical specification) still requires tacit, interpretative skills, which may require access to an expert colleague. Often, such documents are annotated with important notes to the recipient or others (the pervasive phenomenon of the ‘informalisation’ of formal records) in order to situate that knowledge with respect to a particular problem.
As emphasised in Figure 4, it is the timely flow of information between different states that makes it a useful knowledge resource. To adopt a marine metaphor, if expertise gets trapped in any one of the ‘pools’, it stagnates; that is, it becomes out of date and cannot serve as a resource when needed. As we have discovered, the transformation of knowledge representations between different states has implications for what is gained and lost in the power of the representation. See Box 3.1 for an alternative framework which you may find useful.
Box 3.1 An alternative framework for knowledge technologies
A similar but slightly different way of classifying knowledge management technologies is described by O'Leary (1998), who highlights the processes of ‘converting and connecting’. These are summarised below, with references to sections in this unit where we discuss the issues.
Converting individual to group knowledge: knowledge sharing is the assumption underlying models of organisational memory, but it is hard to implement in some cultures, and not straightforward.
Converting data to knowledge: for example, uncovering patterns in databases using data mining (‘Data mining’ in Section 4.3).
Converting text to knowledge: tools for analysing recognisable genres of document and summarising them; tools for evaluating and discussing documents (‘Debating and negotiating meaning’ in Section 4.2).
Connecting people to knowledge: there are many approaches to locating and presenting large amounts of information, including visualisation (‘Information visualisation’ in Section 4.3) and agents to seek out information of interest (‘Software agents’ in Section 4.3).
Connecting knowledge to knowledge: how are knowledge resources interlinked? Agents that can query multiple databases provide one solution (‘Software agents’ in Section 4.3). Ontologies and metadata (‘Ontologies’ in Section 4.3) for describing and interrelating knowledge structures are another route. Boundary objects (‘Communities of practice and technology’ in Section 4.2) help to bridge between communities of practice.
Connecting people to people: maps of who knows what (‘Mapping who knows what’ in Section 4.1) and communication technologies such as telephone, fax, audio/video conferencing, and shared workspaces such as electronic whiteboards over the internet.
Connecting knowledge to people: this again includes agent systems and web-based ‘push’ technologies which deliver to users streams of information such as news summaries and stock prices.
Using Figure 4 as an organising framework, we will next explore the concepts of corporate/organisational memory (Section 3.2), meta-knowledge (‘knowing what you know’ – Section 4.1), tacit knowledge (Section 4.2) and explicit knowledge (Section 4.3), discussing how technologies can support the knowledge resources and processes that the framework suggests.