1. The document discusses challenges with knowledge sharing among experts, who often rely on tacit knowledge gained from extensive experience that is difficult to express explicitly. 2. It reviews literature that knowledge has both tacit and explicit elements and that transferring only explicit knowledge has limitations. True knowledge sharing requires understanding tacit knowledge through observation and practice. 3. The author proposes designing a system to passively capture experts' tacit practices through rich media and provide explicit knowledge resources, to better facilitate knowledge sharing and learning. The system aims to address inherent problems in transferring only explicit knowledge.

1. MORE EFFECTIVE KNOWLEDGE
SHARING FOR EXPERTS WHO
IDENTIFY INSIGHTS FROM DATA
CE Dibsdale, M.Sc. Information Systems
Part Time Ph.D. Student – self funded
13 February 2015
Charlie had 23 years experience in maintaining
and operating complex machinery in nuclear
submarines Followed by 17 years industrial
experience at the heart of building, selling and
researching predictive maintenance solutions for
Rolls-Royce. I write text books and sit on
international standards groups for this discipline.
I am now owner of my own consultancy
company ‘Cedibs predictive technologies Ltd’.

2. “You Know more
than you can say”
Michael Polanyi (1891 1976)
The inventor of ‘tacit knowing’
(commonly misunderstood as Tacit knowledge)

3. The problem – who cares…
Experts are:
1. Rare
2. Expensive
3. Hard to train
4. Do not scale for large
services
5. Take their knowledge
with them

4. Data to wisdom? (Ackoff is wrong!)
Data Info Knowledge Wisdom
Only humans can synthesize this, via sense making and
understanding, in a context, from their own perspectives
Machines process
data
Wisdom seems to be a ‘value judgement’, not a true transform of knowledge
For example: One person’s wisdom is another’s folly

5. Peer reviewed academic journal paper…
IS THIS (explicit)
KNOWLEDGE?
OR IS THIS DATA?
In philosophy the ‘positivistic’
view says this is the only trustable
knowledge that exists in our
world.
Yet….
The ‘constructivist’ perspective
suggests these papers are a
selected set of data
If we did not interpret and
understand differently, as humans,
how could we innovate and have insight?

6. Explicit knowledge sharing

7. Literature search – results..
Generally agreed that knowledge
comprises two types:
• Explicit Knowledge
– Can be expressed and written
down
– Can be consciously
rationalised
– No problems with sharing
explicit knowledge…. ?
• Tacit Knowledge
– ‘Hard’ to express and write
down
– Held unconsciously cannot be
rationalised – but practice can
be reflected on
– Build on practice – is a skill
– Knowing how
Variation exists in concepts:
• No agreed definitions
– Since the time of Plato!
• Disagreement:
– Can tacit knowledge be
shared or not
– Is tacit and explicit
knowledge integral or
separable
– Is it impossible or just hard to
express or codify tacit
knowledge
• No recognition of variability
in human interpretation of
explicit knowledge
This is a mess!

8. I posit… (I need a basis to continue)
• ‘Tacit knowledge’ implies it can
be treated as an object. It
can’t. It’s ‘tacit knowing’
• Tacit knowledge cannot be
expressed or codified
• There are problems of
transferring explicit knowledge
• All knowledge has tacit and
explicit elements
• Explicit knowledge has a base
in tacit knowing (for example:
you have to be able to read)
• Tacit knowing is personal, but
the practice can be reflected on
in groups
• Tacit knowing can be learned by:
– Observing experts at work
– Practice what experts do
– Practice practice practice
– Reflect. then repeat
• Explicit knowledge can be
learned by
– Learning rules, theories or
concepts and how they apply to
aims and goals
– Working by rules until they
become internalised
– Reflect (include questioning the
goals) and check.
• Work to consensus – shared
understanding
– Develop insights, share them and
iterate

9. System – part of the future methods
This system will observe tacit practice with passive workflow
And provide very rich media for explicit knowledge data

10. Conclusions:
• Knowledge management is a mess
• There is little empirical evidence to support tacit
knowing
– The prevailing ‘positivist’ backgrounds and attitudes of
engineers and scientists tend to dismiss tacit knowing as
valuable.
• The concept of explicit knowledge sharing and common
understanding is just assumed
– No recognition that human sense making and understanding
have wide variation
• To move forward the researcher has to make some
assumptions.. From the mess..
• The researcher will design and implement a system to
address the highlighted problems inherent in knowledge
sharing and learning