Social Learning and Knowledge Management

Social Learning and Knowledge Management

The Nonaka and Takeuchi Knowledge Spiral

The Nonaka and Takeuchi Knowledge Spiral (click on the image to learn more)

For about five years around the turn of the century most of my days were spent helping clients manage their knowledge. Back in 2000 knowledge management (KM) was really big. Every year I’d head off to Amsterdam for the obligatory industry conference, KM Europe. We even had our own home grown conference, KM UK, with pretty much the same people but with less impressive venues. Then suddenly things went quiet – KM Europe was suddenly cancelled in 2005, KM UK limped along (and is still going today). KM had lost its way. The promises hadn’t been fulfilled. Of course KM just didn’t disappear overnight – it just degraded gracefully. One client, a very large UK multinational, shed their KM teams and announced that KM was now ’embedded in the business’.

KM still goes on but it’s likely to be on the margins and not essential for peak organisational performance whereas in 2000 KM really was positioned as a game changer.

So what happened? That’s a good question and one which this post is my first attempt at exploring why KM failed to deliver on its early promises. And why do this sort of navel gazing now? Because knowledge management appears to be making a comeback but this time it has a shiny new suit and it’s called social learning.

The KM wave was initially driven by big tech – the idea that an organisation could somehow manage its knowledge was attractive in an economy where knowledge was as important as capital. But knowledge was elusive and the most valuable forms of knowledge refused to be stored in corporate databases. The response from most practitioners was a more people centric approach. The valuable knowledge resides in people’s heads so the best way to surface it is via conversations in communities – communities of practice and communities of interest.

Before I go into why I think social learning and KM are very closely related let’s take a very quick tour of the fundamentals of KM.

 What is Knowledge Management (KM)?

“Knowledge management (KM) comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organizations as processes or practices.” Source: Wikipedia

Just like we love to debate the actual meaning of the word ‘learning’ KM people loved to debate the meaning of the word ‘knowledge’ and in particular how knowledge is different to ‘information’ and ‘data’. The knowledge pyramid graphic (see below) was seen frequently in KM circles. Most KM people used to work in ‘information management’ so you can see why the definition was so important. I don’t want to go into this debate here but one really critical thing to understand if you have any hope of applying KM is the difference between explicit and tacit knowledge.

Knowledge Information and Data

Explicit and Tacit Knowledge

Explicit knowledge is knowledge that can be codified in some way (written down, stored in a visual, or embedded in a process). Explicit knowledge is good because although it is created by people it can be stored in a system. In our case a KM system but more of that later. How does explicit knowledge differ from information? Good question (see the debate on information versus knowledge mentioned above).

Tacit knowledge is knowledge, sometimes called know-how, that resides in people’s heads and is hard to codify (write down). Why is it hard to write down? Usually because it is either complex or contextual or simply because those who have it don’t actually recognise its value (unconscious competence).

Systems Centric or People Centric?

The rise of KM was largely driven by the desire of organisations to capture as much explicit knowledge as they could and store it in a system so that it could easily be shared with others. This systems centric KM became dominant in the early days because it was fuelled by considerable investment from big tech who were re-positioning their information products as KM systems (OpenText, Documentum, Autonomy etc.). The problem facing systems centric KM was the fact that it relied on people to make it work. How could organisations get their people, and in particular their ‘experts’ to share what they knew?

To address the people issue we had people centric KM. This view of KM recognised that if people were to share knowledge it had to be both explicit and tacit and the best way to do this effectively was through direct communication in networks. Specifically networks that were created around a specific topic or area of interest or practice. These networks became known as communities of interest (COI) or communities of practice (COP). In fact the  term COP had been around before (see refs below) –  it was simply co-opted into the language of KM.

The Perfect KM System

The perfect KM system combined both the systems and people centric approaches and acted as a store for explicit knowledge and a connector for tacit knowledge. If you didn’t know something you logged into the KM system and did a search and that search would either tell you what you wanted to know or it identified someone who would have the answer. BP called it their ‘yellow pages’ and even spun a commercial product (called unsurprisingly ‘Connect‘) out of the ground breaking working they did in KM.

KM had sorted knowledge sharing. In future, organisations would be less reliant on people who knew key stuff but didn’t share that stuff effectively. In practice there were lots of problems when it actually came to making  KM work in practice and unsurprisingly most of those problems revolved around people’s behaviours and attitudes to sharing what they know.

In my next post, ‘Why social learning won’t work‘, I’ll look at what went wrong with the KM dream and why understanding why KM didn’t deliver on its initial promise has some useful lessons for those of us attempting to introduce social learning into organisations.

In the third and final post, ‘Why Social Learning Will Work‘,  I’ll explore some ways we might be able to get social learning working for us by starting small and looking for quick wins.

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