Page 77 - IRMSA Risk Report 2020

OUR APPR O A CH : HO W W AS D ATA C OLLEC TED ?

This research was conducted using an approach and software tool that has its roots in weak signal detection in complex
contexts. Sensemaker® was developed by Prof Dave Snowden (Cognitive Edge) and differs from traditional quantitative
and qualitative research techniques in two foundational ways:

SELF-INTERPRETATION DISINTERMEDIATION

Respondents are prompted to share a story of an actual Data is presented in visual form. Quantitative patterns
experience. They are then asked to answer questions are accompanied by supporting narratives to provide
about their story, adding quantitative data to the context. Decision-makers can interact with these patterns
qualitative story. This puts the power of interpretation and read the stories to come to their own insights. Again,
back in the hands of the storyteller, vs qualitative there is no intermediaries involved that adds their own
processes where interpretation is done by analysts biased views.
and bias often creeps in. These quantitative questions
are presented as triangles where three concepts are
presented that are in tension with each other. These
questions force respondents to think, as there is no
clear “right answer”. Sensemaker data is therefore much
richer than normal surveys.

The results presented below represent how the various respondents felt the questions related to their particular story. In
scatter graphs, each dot represents someone’s story and where they placed it.

WHO RESPONDED ?

We categorised the responses as follows:
E X E C U T I V E S RISK AND REL ATED
SPECIALIST S
( 3 1 % ) : G O V E R N I N G PR OFESSIONALS ( 5 7 % ) : ( 1 2 % ) : A C ADEMICS ,
B O D Y M E M B E R S , RISK M ANA GEMENT,
E X E C U T I V E S A N D FORENSIC , C ONSULTANT S ,
S E N I O R G O V E R N M E N T C OMPLIANCE , ENGINEERS .
O F F I C I A L S . RESILIENCE , E T C .

WHAT WERE WE L OOKING FOR ?

The insight we were interested in is how people perceived the management of risk in the real world, based on actual
their experiences inside and outside the boardroom. To explore this, we looked at actual stories - retellings of experiences
– with positive or negative outcomes. Stories can be considered as “warm data” (Nora Bateson); they encapsulate our
emotional and embodied response to what happened. Given that our perceptions and decision-making make use of
embodied, emotional, and rational processes, stories provide a better insight into the environment we operate in than
does a traditional survey.

WHAT KINDS OF RISKS DID OUR RESPONDENT S TALK
ABOUT ?

The stories were fairly equally distributed between how they impacted on strategy, operations, and reputation in the
organisations where they originated.

WHAT INFL UENCED THE OUT C OMES IN THESE ST ORIES ?

There was a significant difference between what influenced the outcomes in the stories told. Incidents driven by insight
were more successfully managed than those driven by emotion and politics. Where decisions were influenced by
politics, learning did not happen – the context in which these decisions were taken stayed the same after the event, and
respondents felt more negative about their stories.

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