Note: This is the second in a series on the application of the Cynefin decision-making framework to the field of competitive intelligence. Part I is available here.
We last left off with the introduction to Cynefin. To review, Cynefin is a framework based on complexity science and designed to help decision makers understand where their system stands in the external environment. This context allows not only for better decision-making, but also to avoid problems that arise when a management style is used outside of appropriate context.
As Cynefin’s creator – Dave Snowden – described it in the Harvard Business Review:
“The framework sorts the issues facing leaders into five contexts defined by the nature of the relationship between cause and effect. Four of these – simple, complicated, complex, and chaotic – require leaders to diagnose situations and to act in contextually appropriate ways. The fifth – disorder – applies when it is unclear which of the other four contexts is predominant.”
(For the sake of brevity here I encourage you, dear reader, to explore Cynefin yourself with the suggested resources appended to Part I of the series.)
If we were to match the utility of existing competitive intelligence practice and methodology to the Cynefin domains, we would see that CI is very useful in the ordered domains (simple and complicated), where causation is perceptible. However, in the un-ordered domains (complex and chaotic), where causation is less perceptible – CI would have substantially less utility.
After all, how can you develop KITs and KIQs if you don’t know what you don’t know? Lexis-Nexis does not have a premium subscription for the unknowable.
This lack of utility in the complex and chaotic domains presents a problem for CI.
Because much of the business world over that last 30 years has shifted from complicated systems to complex systems – that is, to the un-ordered side of the spectrum in the Cynefin domains.
As Gokce Sargut and Rita Gunther McGrath wrote in the September 2011 issue of HBR:
“complexity has gone from something found mainly in large systems, such as cities, to something that affects almost everything we touch: the products we design, the jobs we do every day, and the organizations we oversee.”
Facilitated by a revolution in information technology, systems that were once separated are now interdependent, and thus by definition – more complex. The result, as Sargut and McGrath write, is that:
“[c]omplex organizations are far more difficult to manage than merely complicated ones. It’s harder to predict what will happen, because complex systems interact in unexpected ways. It’s harder to make sense of things, because the degree of complexity may lie beyond our cognitive limits. And it’s harder to place bets, because the past behavior of a complex system may not predict its future behavior. In a complex system the outlier is often more significant than the average.
Making matters worse, our analytic tools haven’t kept up. Collectively we know a good deal about how to navigate complexity—but that knowledge hasn’t permeated the thinking of most of today’s executives or the business schools that teach tomorrow’s managers.”
Emphasis on “our analytic tools haven’t kept up.”
This is not to say that competitive intelligence methods that have come before us should be abandoned; but rather to acknowledge that the methods used in the past had utility, until they were shifted into an inappropriate context. Until CI evolves to be suitable for a complex environment, it will increasingly find itself being used in an inappropriate context.
Managing everything as is if it were simple would be ineffective, and managing everything as if it were complex would be inefficient. Cynefin is not a unified field theory or holy grail of anything – but it does provide a useful framework to put the pieces together in ways that will help to compare, make-sense of, and guide us in the selection and development of CI methodologies.
Coming Up – intelligence and strategy in a complex environment; and the complex adaptive organization.