Making Sense of Human-Machine Symbiosis

Cynefin Model

Cynefin Model

A NUMBER of people have remarked to me that Dave Snowden’s title for his forthcoming talk to ISKO UK on 23 April 2009 is less than informative. Well, it depends on how well you know his work since he moved on from IBM’s Institute for Knowledge Management and the Cynefin Centre to focus on his own company, Cognitive Edge Pte Ltd.

I’m no expert in Cognitive Edge’s pioneering approach, but maybe I can shed some light on themes he might address in his talk by describing the context within which I apprehend it, and making a few other links along the way.

The processes of organizing and sharing knowledge are complex because people are involved in both the input and the output. However much we try to codify and structure both, there is always that residue of ‘fuzziness’ – un-order – which Checkland in his Soft Systems Methodology described as giving rise to ‘ill-defined’ or ‘soft’ problems.  Although the computer can help us greatly with codification and structure, it has been virtually useless in the face of soft problems – until perhaps the advent of Web 2.0.

As we are increasingly obliged to acknowledge, organizations are comprised of both formal and informal relationships, and it is often the latter which provide the real channels for knowledge and information flow. But how do we tap into these informal networks, and even if we can, how do we make sense of and derive value from what we find? Major shifts and trends (good and bad) often start as ‘weak signals‘, almost undetectable by conventional means. How can we spot these early enough to be able to discourage bad trends and encourage good ones?

Cognitive Edge addresses these questions within an organization by collecting narrative and organizing and analyzing it for meaningful patterns using its open source methods supported by its proprietary software suite SenseMaker. It should be readily apparent that such early intelligence could prove vital to effective decision-making in many situations where the degree of risk is not clear.

Less readily apparent perhaps, is that knowledge organization has a key role to play in this scenario. As UCL alumnus Patrick Lambe says in his excellent book Organising Knowledge: Taxonomies, Knowledge and Organisational Effectiveness:

“Categorisation is, of course, fundamental to the management of risk. Different kinds of risk must be identified and grouped together based on origin, severity or remedy. Risk intelligence systems need to identify the signals or clues that would indicate particular categories of risk and put in place monitoring mechanisms (strategic early warning systems) so that these signals are picked up whenever a risk is emerging (Gilad, 2001).”

Moreover, it does not take a huge leap of the imagination to suggest that if software such as SenseMaker can discern patterns and trends even when weakly detectable, then it could presumably be employed in bridging the gap between formal vocabularies and newly emergent terms and concepts. Such tools are needed to help us move beyond the spurious divide between the formal taxonomic ‘elite’ and the folksonomic lumpenproletariat which is advancing the cause of neither party.

Interesting thought: If software like SenseMaker had been deployed at Lloyds, would they still have gone through with the HBOS takeover?

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One Response to Making Sense of Human-Machine Symbiosis

  1. “If software such as SenseMaker can discern patterns and trends even when weakly detectable…”

    This is a misconception about what SenseMaker does. SenseMaker doesn’t discern patterns: people do, and SenseMaker helps to make patterns visible. Rather like in astronomy: telescopes make no observations, but they make them possible.

    One could draw a closer analogy with statistical software such as MiniTab or SPSS: these programs don’t detect patterns, but they organise the collected data, and allow you to twist the stuff this way and that, for example viewing distribution curves and co-relating one set with another. Data visualisations turn numbers into visible arrays, and a human intelligence, looking at such an array, may be able to detect patterns that look significant.

    SenseMaker is a software suite which collects qualitative evidence from respondents, typically in the form of written or narrated/transcribed fragments in response to a “probe question”. There is no way that SenseMaker can “make sense” of such data, but at the same time as the narrative fragments are collected, one gets them “self-signified” by the respondents in the form of quantifiable responses to supplementary questions, which are answered through an interface of checkboxes and sliding scales.

    SenseMaker works with this quantitative “signifier” data, which if the questions have been well designed (aye, there’s the rub!) lets you look for patterns as described above. One also has immediate access to the database of narrative fragments themselves, to help further formulate an understanding.

    I think it should now be apparent why an enquiry using SenseMaker and its associated methods is a “Human-Machine Symbiosis”; but to understand the intellectual framework, hear some practical examples and figure out what this has to do with knowledge organisation, one would have to come to the ISKO event!

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