Organizations are expected to thrive in a world that has become increasingly complex. We have more data today than we have the time to understand. Change accelerates faster than we can proactively plan for. And countries, industries, ecosystems are more connected than ever. All this means that it becomes harder to understand how our present day actions will affect things beyond our usual scope of attention.
With increasing complexity, there is a tendency for analysts to fight back with more complexity – more detailed forecasting models, more initiatives, more goals on strategic plans. The idea is that you can respond to the growing number of potential problems by doing more things. This focus on managing many variables is called “detail complexity” and it’s an attractive way to solve problems because it provides a feeling of control and certainty – as long as we measure and track all these variables, then we feel like we have a greater handle on the organization.
But here’s the problem with a complex world. Cause and effect are not always obvious – decisions made in one department can impact many other stakeholders without any one group’s awareness. And decisions today may have long-term consequences months and years out much after those decision makers have conducted their reflections of whether their initiatives were successful. Detail complexity is also expensive – it costs a lot to capture a large variety of datasets, revise models, and check for errors.
Organizations need to develop the capacity to understand and discuss dynamic complexity, not just detail complexity. Dynamic complexity looks at the whole and focuses on the feedback relationships that can drive behavior over the long-run. Dynamic complexity asks questions such as “How do we improve quality while growing market share and sustaining talent?” Detail complexity asks “What is the projected market share in 5 years?” or “What top 3 initiatives will improve employee retention this year?”
Dynamic complexity recognizes that actions have a different set of consequences locally vs. elsewhere in the organization. Dynamic complexity recognizes that solutions that seem obvious may have non-obvious consequences. This approach actually simplifies our understanding of the system by helping us identify the deeper patterns behind the noisy events of today and the multitude of spreadsheets.
Detail complexity helps us plan, but dynamic complexity helps us understand. We shouldn’t do the former without first doing the latter. Otherwise we would miss the forest for the trees. The challenge here is that detail complexity work is more concrete – people may end up with their minds tied up in execution mode, focused on the tedious tasks of meeting short-run deadlines that this type of work demands. The more time we spend on lower leverage work, the less mental capacity we have to see the dynamic big picture.
Without dynamic thinking, organizations might have the best products and hire top talent, but still fall short of potential because they aren’t able to leverage their distinct functions and people into a more productive whole. A well-functioning system is one where the whole is greater than the sum of its parts. Organizations that plan to stick around need to cultivate cross-team collaboration, organization-wide feedback, and awareness of dynamic complexity.