Danger: when decision bias trumps analysis

By Ken Witt

I’d like to share some thoughts about a recent article that intrigued me and got me thinking about some of the challenges CGMAs  may face in providing strategic intelligence and analysis in their organizations.

A few weeks ago the Atlantic ran an article with the provocative title – Peak Intel: How so-called strategic intelligence actually makes us dumber, by strategy consultant, or former strategy consultant, Eric Garland. Garland stated that he had recently left his job as a futurist and strategic intelligence analyst because of the “endemic corruption of how decisions are made in our most critical institutions.” He cites some structural reasons connected to the consolidation of industries and creating bureaucracies in which “decisions are simply not in vogue right now” and also the impact of policy and politics and businesses that have become “too big to fail.”

However, his primary complaint is that executives are not interested in analysis or predictions that do not conform to their view of the world, or do not support decisions they have already made. Providing a number of examples, including one where a “silver-haired alpha-dog” refused to consider commonly accepted demographic information about global aging populations, Garland states that “the problem is, the market for intelligence is now largely about providing information that makes decision makers feel better, rather than bringing true insights about risk and opportunity.” 

As management accountants, a critical component of our stock-in-trade is analysis that offers insights about risk and opportunity. However, as is pointed out in the CGMA tool How to turn data into decisions, there are many emotional aspects that can derail investment and other business decisions. This primer provides a systematic approach to decision-making and “how-to” instructions for using various decision- tools, including target-costing, process mapping, fishbone charts and theory of constraints.  It also highlights a number of significant factors that can influence our interpretation of data taken from the field of behavioural finance.

Is your executive leadership or board open to analysis that challenges their assumptions, or do you have your own “silver haired alpha-dog” stories? What have you found to be the most effective strategies for overcoming resistance like this or other decision biases that you have encountered?