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The third place of knowledge management

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On a recent long car drive, my daughter randomly brought up the topic of “third places.” She has a knack for raising obscure intellectual topics; I suspect that her alma mater offered classes on the best techniques for doing so, and she settled on “nonchalant topic drop followed by discreet eyebrow raise at the listener’s ignorance.” For those of you who are more on my plane, a third place, a term coined by Ray Oldenburg in his 1989 book The Great Good Place, is basically a social space, separate from the two usual environments of home and work. In a world of remote and home working, this third place has become increasingly rare and, simultaneously, essential.

This led me to think about the role of KM in organizations and how it also has an essential third place, although not the physical place Oldenburg proposed, that is increasingly being neglected. Let me explain.

During the past decade, KM has become increasingly oriented toward “explicit” knowledge over “tacit” knowledge. Explicit knowledge is codified in documents and data and is heavily emphasized by the current drive toward automation and AI. Tacit knowledge, intuitive know-how, experience-based practices, and values, however, have taken a back seat. It’s not hard to figure out why this has happened: You can’t automate tacit knowledge. And in a world that uses terms such as “hyper-automation” with no sense of irony, tacit knowledge doesn’t fit the mold.

How does this relate to the concept of third places? Think of it this way: Explicit knowledge feeds AI; it’s the lifeblood of training sets and provides explicit answers to explicit questions. For example, a customer who asks, “When will my warranty expire?” wants a clear-cut answer, such as “On the 21st of May.” Many valuable use cases effectively exploit explicit knowledge. Let’s call this first place “mechanistic KM”; as much as there is a right or wrong answer, it’s somewhat predefined and predictable, and much of the work here is ripe for automation. But this is the tip of the knowledge iceberg.

There is a second place that is human-centric, which I’ll call “curated knowledge.” This is a traditional area for KM to address, where tacit knowledge is captured and converted to explicit knowledge. In this place, KM systems provide a repository of resources to help workers do their jobs. An archetypal example would be a legal firm that has curated both prior cases and the firm’s accumulated expertise to help partners and employees solve legal problems. KM systems here provide a library of curated resources that workers can explore to test ideas and assumptions and check facts and opinions; they are more to inform than to answer questions explicitly.

Tacit, undocumented knowledge

The third place I alluded to goes far beyond the first two and takes us into the actual world of tacit knowledge. Here, knowledge comes from and often remains as personal experience, impressions, and intuition; it’s undocumented and often hidden and elusive. When work issues arise—for example, a customer inquiry—group knowledge is often used to resolve it. One employee knows part of the answer; the rest of the answer comes from others in the team.

Tacit knowledge is born, nurtured, and lives in this third place; the organization is home to KM as a living, breathing entity. It is where we learn, share, support, bond, and encourage; it’s the place where theoretical KM overlaps with organizational dynamics. It’s a vast, touchy-feely place rife with office politics and relationships where concepts such as hyper-automation generally don’t belong. Yet, it’s a place where good KM practices have historically thrived.

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