Cutter Senior Consultant Barry Devlin is passionate about extending BI approaches into true business insight. His four-part series over at Upside.com digs into the science of decision making (hint: we’re less rational than we think!) and how that fits with current BI tools (hint: not very well). Here’s an excerpt from How Do You Make Decisions? (Part 4).
In my book, Business unIntelligence: Insight and Innovation beyond Analytics and Big Data, I developed a conceptual model for decision making support in today’s world. This model consists of three “thinking spaces.” Two of these are obvious and long-recognized: information and process. However, I also explicitly included a people space in the conceptual architecture, sitting atop the information and process spaces.
As seen in Figure 1, this space — like the others — is defined by three axes that define key characteristics that must be considered in the design of solutions or software tools. In this case, they describe characteristics of decision making as performed by people in organizations. The horizontal axes depict psychological characteristics of attitude and motivation; the vertical axis represents the social aspect of role.
The dimension of attitude describes a spectrum of psychological states that take us beyond the purely rational, using the triune model of the brain described in Part 3. The fight-or-flight reptile brain and emotional, old mammalian brain are represented by the reactive/emotive area, indicating pre-rational or irrational behavior.
The third level, the neocortex, is represented by the other three areas: left-brain logical/rational thinking, including numerical and verbal skills; right-brain intuitive/integrative thinking, the basis of innovation; and the empathic/social area, responsible for personal identification, social skills, and empathy.
Each of these areas is amenable to technological support and enhancement. The near-exclusive focus of today’s BI tools is on logical/rational decision making, with limited interest in intuitive/integrative and empathic/social aspects. The reactive/emotional area is almost entirely ignored, although advances in artificial intelligence — affective computing for emotion recognition, for example — show promise in supporting this area.
Why not start at the beginning of the series? Barry offers great insights into the science of decisions that will help you adopt a more integrated approach to business decision making. (Part 1, Part 2, and Part 3)