Knowledge management (KM) is the process of capturing, developing, sharing, and using organizational knowledge. Purposeful, concrete, and action-oriented knowledge management programs can yield impressive benefits for both individuals and organizations.
What is tacit knowledge?
There are two fundamental forms of knowledge – tacit and explicit.
The term tacit stems from the Latin word tacitus, which means “that is passed over in silence or done without words.” Tacit knowledge, by definition, is not articulated and codified, and therefore does not show up in any formal system.
However, as the mind+machine analytics framework shows, insight and knowledge still remain a part of the ‘mind’ section. As such, it would not be wrong to believe that the most interesting findings often are, and may remain, buried inside the human mind – in our case, even when an employee leaves the organization. Of course, there have been efforts to capture tacit knowledge, but codifying it is a time-consuming, expensive, and largely futile effort (especially if it has to be done as add-on to normal work!).
Insights and knowledge are things of the mind… but so are incentives. If people don’t have any incentive to undertake KM, they simply won’t. Incentives can be in the form of tangible and useful knowledge that improves an employee’s specific situation or an organizational incentive such as monetary rewards or promotions. Unless there are clear incentives associated with creating and sharing knowledge, employees are no more likely to do it than make philanthropic contributions.
Data and information versus insight and knowledge
Many companies approach KM at the data or information level, thereby creating lots of data lakes using expensive software and networks. On the other hand insights and knowledge, even when drawn from big data, are available in a highly compressed format. For example, analysis of 1 TB of data may yield only one insightful sentence. Therefore, KM should focus on insights and “so what’s” as well as learnings at the meta level, and not on storing basic data or information. Needless to say, the latter only leads to wasted efforts and investment.
Knowledge Management and the use case workflow
Fuzzy pools of documents on SharePoint drives, which can then be explored with linguistic search engines that deploy AI, are the hot topic of the day. A search engine can find a specific document in a 500 TB pool in a mere 100 milliseconds. Unfortunately, most documents can’t be understood if the context is not right. Even if a document is relevant, it may be in the wrong format or template, and may need a lot of work to be useful. As such, generic search engines hardly ever pull up anything that can be used as is or bring in productivity advantages. However, if the KM relates to a specific use case and the context is clear, it can be highly successful and productive.
Auto-Death
Plainly put, KM needs an “auto-death” functionality. Data, information, insights, and knowledge have different half-lives – data and information expire in days or possibly weeks, while true insights might last a year, and true knowledge possibly up to five years. Outdated data and information can clog a system, making it harder to find real insights and knowledge. The result could be frustrated users, who start building their local wikis and SharePoint folders. Of course, an auto-death function requires tagging of the collected data or knowledge. Today’s systems keep clogging up as such functionalities are hardly ever available.
What is needed?
Knowledge management has not evolved much during the past decade. Worse, clumsy systems have led many people to cringe at the very mention of the word. However, smart organizations have benefited a lot from KM. A good KM process puts the mind at the center; sets the correct incentives; and defines specific and manageable use cases that offer tangible output. Interestingly, these models were seen as far back as the 1990s, before elaborate IT support was available.
Modern KM is people-centered, stores less data and information, and focuses on insight and knowledge. It also uses more modern architecture, such as the knowledge object architecture, to enable useful elements such as audit trails, better prioritization of searches, and placement of knowledge in the right context.
The insights and knowledge I have tried to capture could be useful for you as well. For more information on the topic, please take a look at mind+machine book.
