Twelve Questions Executives Can Ask To Improve Decision Making

I first became interested in decision making more than a decade ago, back when I was running marketing at Business Objects.  My interest was prompted by the evolution of taglines among BI vendors.  In the early days, taglines were descriptive like First in Enterprise Decision Support or The Enterprise Data Mart Company.

Over time, pressure mounted on marketing to pitch benefits — the message shouldn’t just be about getting people information, but the benefit of having it.  Slogans evolved accordingly:  Now You Know, The Power To Know, and Business Intelligence:  If You Have It, You Know.

But was knowing enough of a benefit?  You could certainly take it up a level, and Cognos did:  Better Decisions Every Day.  For a marketing slogan it was good enough, but was it true?   Did providing better access to corporate information  invariably improve decision making?  It seemed like a leap so I decided to research it.

I’ll never forget when Cornell professor Jay Russo told me, “the primary use of new information is selective filtering to justify previously established conclusions.”  So, despite the commonsense appeal of the Cognos tagline, you most certainly could not draw a straight line from “more information” to “better decisions.”

I studied how individuals and groups  made decisions.  I read interesting books like Russo’s Decision Traps (later positively reframed into Winning Decisions) and Smart Choices.  Years later I became interested in mass decision making  in The Wisdom of Crowds and behavioral economics in Predictably Irrational and Why Smart People Make Big Money Mistakes.

I remember asking Russo why decision making wasn’t more of a focus in business schools.  His answer came down to two things:

• If you can’t measure it, you can’t manage it.  Until corporations want to start measuring decision making, you can’t focus on improving it.  (I remember once suggesting a BI product that tracked votes on strategic decisions, evaluated their success years later, and calculated batting averages for team members.  The idea was shot down as my colleagues imagined executives fleeing like cockroaches under an illuminated light.)

• Executives perceive their jobs as decision-making and themselves as experts.  Think:  Why would I need a class in decision making?  I make decisions for a living and my success in rising up this organization is proof that I am good at it.

But if quenching thirst is the ultimate benefit of Coke, improved decision making really is the ultimate benefit sought by BI consumers.  The problem was  — and is — that BI software can’t deliver it.

So if you want to improve your decision making, then you’re going to have to read up a bit, either through the books I’ve referenced above or via a recent article in Harvard Business Review entitled Before You Make That Big Decision, which provides 12 questions that senior executives can ask about decisions and decision-making processes to avoid the most common errors.

Here are those 12 questions and the biases that they are trying to detect:

1. Is there any reason to suspect motivated errors, or errors driven by the self-interest of the recommending team?  (self-interest bias)
2. Have the people making the recommendation fallen in love with it?  (affect heuristic)
3. Were there dissenting opinions within the recommending team?  (groupthink)
4. Could the diagnosis of the situation be overly influenced by salient analogies?  (saliency bias)
5. Have credible alternatives been considered?  (confirmation bias)
6. If you had to make this decision again in a year, what information would you want and can you get more of it now?  (availability bias)
7. Do you know where the numbers came from?  (anchoring bias)
8. Can you see a halo effect? (halo effect)
9. Are the people making the recommendation overly attached to past decisions?  (sunk-cost fallacy, endowment effect)
10. Is the base case overly optimistic?  (overconfidence)
11. Is the worst case bad enough?  (disaster neglect)
12. Is the recommending team overly cautious?  (loss aversion)