Tacit knowledge and high value work

There was a lively discussion about the role of tacit knowledge over a couple of days at John Naughton’s daily blog recently. I’ll come back to the reason why the subject came up in a moment or so.

I came quite late in life to questions of knowledge management, and my first exposure was to the work of the Japanese professors Nonaka and Takeuchi, and their ‘SECI’ model, which argues explicitly that knowledge in organisations moves repeatedly between the tacit and explicit.

SECI stands for the four corners of their knowledge spiral: Socialisation-> Externalisation-> Combination -> Internalisation, in which knowledge dances between the individual, the group, and the organisation in different combinations.

Saying and knowing

No one really disagrees that that’s how it works, but different people have different versions of it. Dave Snowden, who did knowledge management at IBM before developing his own theory of different types of knowledge, has a memorable phrase about this:

We can say more than we write, and we know more than we can say.

This leads, incidentally, to his more radical observation—that you can’t conscript knowledge workers. You can only invite them to volunteer.

The SECI model was also known as ‘Ba’, a Japanese word that translates into English very, very, loosely as ‘place’. Snowden’s model has a Welsh name—Cynefin—that also translates very, very, loosely into English as ‘place’. It was a deliberate choice.

Social learning

The other writer whose work I came across as I was trying to understand how knowledge moved around organisations was that of the late Max Boisot, who had a model of the social learning cycle that also moves between tacit and explicit.

A lot of Boisot’s work is about innovation, and he developed this two dimensional model into a three dimensional space later on. I use this model in futures training to help people understand the place that futures work fits into the knowledge cycle of the typical organisation. (It’s the upward line on the left hand side.)

The idea of tacit knowledge comes originally from Michael Polanyi, who developed it in the 1960s as part of a theory about how scientific discovery and innovation worked. Anthony Barnett suggested to Naughton that Mike Cooley developed it as a “working concept” in his book  Architect or Bee in the 1970s. Cooley is a neglected figure now, but he was an influential advocate then for using technology (and manufacturing capacity) for social benefit, as in his work on The Lucas Plan.

Organisational knowledge

It is certainly the case that organisational knowledge rests hugely on forms of tacit knowledge. One of the reasons that the idea of ‘Business Process Redesign’ was a colossal failure in the 1990s was that it focussed on material flows and ignored the unofficial (i.e. tacit) ways in which knowledge flows around.

Anyway, the reason why Naughton was interested in the role of tacit knowledge was that it turns out to be absolutely critical to the manufacture of semiconductors and related processes.

He’d picked this up in a review of Chris Miller’s book Chip War, by Diane Coyle:

lots of great examples of the difficulty of copying advanced chip technology because of the necessary tacit knowledge: for instance, every AMSL photolithography machine comes with a lifetime supply of AMSL technicians to tend to it. This is either hopeful – China will find it hard to catch up fully – or not – the US or EU will not be able to catch up with TSMC because of the latter’s vast embedded know-how.

The TEA-laser

AMLS is the Dutch lithography company that develops and makes the photolithography machines that are used to etch the most advanced silicon ships in our computing devices. TSMC in Taiwan, is the world’s leading semi-conductor ‘foundry’. Naughton’s point is that the knowledge in the heads of the staff is also a critical part of this advanced manufacturing infrastructure.

In his first post, Naughton goes back to the work of Harry Collins, who studied how an item of lab equipment, the TEA-laser, migrated from lab to lab:

There was very good information in the journals about how to build such a laser. But anybody who tried to put one together using written articles failed. They had something that looked like a laser on their bench, but it wouldn’t lase. What people didn’t understand was that the inductance of the leads was important. If you’d been to somebody else’s lab, you would build a complicated metal framework to hold a big capacitor close to the top electrode. But if you were working from just a circuit diagram, you naturally put this big heavy thing on the bench, and the lead from the capacitor to the top electrode would be too long and have too high an inductance for the laser to work.

Up to speed

One of his readers responded to his first post by explaining how Intel had spent significant resources moving its fabrication engineers from plant to plant,

to ‘enskill’ local teams in a new Fab (fabrication) process. At any one point they were re-settling 50 odd people (and their families) from Israel to Leixlip, and then in time moving the Leixlip team to Arizona or Portland to bring the next team up to speed. They could ‘copy exactly’ the fabs but it was the people they needed to make the new equipment faultlessly churn out the wafers.”

There’s an important point here about the role of knowledge generally in high value production processes. And relatedly, in how productivity increases in leading edge economies. This is what piqued Naughton’s interest:

I regard (tacit knowledge) as a radically undervalued phenomenon that is relevant to all aspects of the computerisation of work, and of course to many of the arguments currently going on about so-called ‘AI’.

High value work

Naughton’s particular interest is in digital technologies, and how they work (or don’t work) socially. But there’s a much wider point here. Almost all high value work—perhaps all high value work—depends on tacit knowledge. There’s not space here to explore David Snowden’s Cynefin model, but it connects different types of knowing to different types of external environment.

The only space where you can really draw up clear instructions to get things done—i.e. formalise the knowledge processes—is in the ‘Clear’ space in the bottom right, where rules work well as an instruction set. In all of the others, and especially in the ‘Complex’ and ‘Chaotic’ spaces on the left hand side, the ‘sensing’ function comes after some form of complex human activity. In other words, people have to use their knowledge and experience to interpret what’s happening.

Heart of value

I wrote about this a few years ago when I did a project on high value work for the Association of Finnish Work. In that piece, I talked about our finding that high value businesses “have human capabilities at their heart… People, in short, are at the heart of value.” This is the reason that most credible stories about the impact of AI in the workplace see the future of AI as being augmenting human work. And so far, of course, one of the dirty secrets of AI is that people sit behind the automata, filling the gaps, often with the soft knowledge that is the essence of tacit knowledge.

Harry Collins, as it happens, had a memorable metaphor for the difficulties in translating and recording tacit knowledge—trying to make it explicit, in other words:

In formal knowledge-engineering exercises in the 1980s, researchers would interview experts to try to extract the rules or heuristics that they employed in their work — and then try to express those in computerised ’expert systems’ which would supposedly work, but often didn’t. Harry’s metaphor was that such interviewing methods are like straining dumpling soup through a colander: you get the dumplings, but you lose the soup. And it’s the soup you really need, because it’s the tacit knowledge.

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A version of this article was also published on my Just Two Things Newsletter.