When one hears the phrase “Knowledge Management” in marketing material, it is usually put alongside some sort of content management system like Drupal or spin-offs like CiviCRM or OpenAtrium. These applications excel at providing flexible and scalable solutions for content management. 37Signals, by making their products bare-bones simple, gives access to solutions otherwise unreachable to non-technical workers. Basecamp and others provide a structured system for managing content and data that is often relegated to paper roll-a-dexes in typical SMEs because traditional solutions are costly and labour intensive to establish – thanks largely to their big-corporate lineage.
Even highly creative solutions like MindTouch or the swirling maelstrom of open source wiki solutions on the web provide different ways to manage content.
While the problem of where to put documents has been answered – technology has offered little in the way of assisting the management of the process of work and creativity. That is, CMS/CRM and the like offer methods to reposit and organize work outputs and lack a coherent and effective system for augmenting the work process, or assisting in the work inputs, particularly inputs exogenous to the organization.
For example, while a CRM (customer relationship manager) application can assist sales and support in tracking data associated with accounts and even provide a way of sign-posting the sales process, most do little in the way of augmenting the client research phase, pitch and messaging development, campaign deployment, data analysis, goal setting and the like. Typically this is done on a whiteboard in a meeting, in Microsoft Word with an implicit process in the mind of the sales manager or simply tracked individually by the sales team with informal activity synchronization over lunch or at regular meetings – hardly taking advantage of the real-time capabilities afforded by contemporary participatory technology.
Worse still are tools for handling work-inputs; the information gathered by workers to inform their work. Particularly, there’s little in the way of process enabling systems to facilitate the process of data -> information -> knowledge. That is, there are few systems that explicitly track the mechanisms, assumptions and decisions by which incoming data is connected with action. In most situations, one must read through acres of discussion threads (either on an intranet or, more likely, through email) such systems are simultaneously fragmented and extremely voluminous. To learn from this information, you must play it back in real-time, a massive productivity burden. Further, it’s often impossible to determine whether a given decision is a relevant precedent or not because organizations typically do not collate and annotate archives of conversation (how pedantic and weird would that be?).
Organizations that DO concretize the process of decision making (rather than merely the results) and store that process for easy retrieval do so implicitly or accidentally – industrial design firms, through their process of prototyping, review and specification are, in many ways, companies that specialize in making particular kinds of informed decisions on the behalf of others – so by professional convention, this process of knowledge management, in the true sense, is built-in. Many organizations, typically lack such a system, or if it does exist is mired in bureaucracy to the point of negating the benefit.
Learning is ultimately what these systems hope to facilitate, learning is in the technical sense, the process of perception that changes behaviour. Without behavioural change, it can be said that no learning has occurred. Which at an organizational level means that only that information which might alter the established momentum of the organization can result in learning. Such a system constraint diminishes the distortion of the confirmation bias in information analysis – by deliberately seeking only alternatives to assumed action, the process of learning will be more robust. The challenge in facilitating learning through software lies in processes conversion, discovery, translation and reconversion.
Conversion
Initially information must be converted from knowledge – someone must commit to writing (typically) what they have learned. This process will invariably produce a sub-set of what was actually learned and so the goal must be to capture as great a portion of knowledge as can be hoped for. Included in this conversion task must be a process for articulating the provenance of the knowledge, the context(s) to which it was/is applied and presumed requisite knowledge to comprehend the information. In systems that facilitate this kind of knowledge conversion, a great deal is made of discovery meta-data. This kind of a priori information architecture management tasks is problematic, as it takes on an erroneous and potentially harmful categorical view of information and knowledge. That such exists in one or even a plurality of enumerated domains. Information is slotted into a taxonomy – either explicitly through some established taxa (say, Library of Congress system) or implicitly through tagging (which in aggregate serve to categorize content in a so-called folksonomy).
This presents the problem as the content author or curator must predict the relevance of the information prior to its employment – an impossible task. The web enabled the feasibility of weak statements of relevance (tags) which is modestly better than strong statements of relevance (explicit categorization) but is still problematic because of its static predictive nature. Solutions like wikis make the process of re-purposing information low-cost, which certainly increases the chances of relevant knowledge discovery but do not address the issues of facilitated conversion – in a wiki you’re still presented with a blank form with no indication as to what to write (that is left as an exercise to the writer).
What this means is that only those people with the conjunction of domain knowledge and the ability to write good prose with little assistance are able to contribute meaningfully to knowledge bases. The ability to write well is a rare facility and requiring it of every contributor is obviously an issue, further depending on such an ability for the knowledge to be usable is also an issue. KM systems must provide some way for users to capture qualitative and quantitative knowledge without assumptions as to future relevance or requiring the user to compose the structure at every entry.
Conversion from tacit to explicit knowledge can be a worthwhile exercise for improving the knowledge of the users as it can clarify their thinking on a subject as they commit their knowledge back into information. However, the conversion process must facilitate the further steps in the KM process and keep the barriers to entry low so contributors are incented to contribute.
Conversion is the first step, the others are critically important also and are the subject of future posts.
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