Management consultants use issue trees to help them structure how they solve complex problems. A closely related animal is the question map, which is at the heart of our problem-solving approach and, therefore, a recurrent theme on this site. There are two types of maps: why maps and how maps.

(Note: Although issue trees and question maps have the same objectives, maps have more structured rules, so I’ll talk primarily of questions maps. But whatever helps you creates better question maps will help you create better issue trees, so don’t see it as a limitation.)

A question map is a graphical breakdown of your key question. Maps have four basic rules:

  • They consistently answer a singly type of question, either a why or a how question
  • They progress go from the key question to concrete hypotheses
  • They have a MECE structure
  • They use an insightful structure

Questions maps are useful to efficiently consider all the potential answers for your problem and understand how they interrelate.

A why map helps you diagnose

A why map helps you search for all of the potential root causes your problem. You list these in logical groups on the first column to the right of your key question, ensuring that your groupings are mutually exclusive and collectively exhaustive—or MECE. As you further progress to the right, you continue drilling down in the details of each grouping. In that sense, why maps are similar to Ishikawa diagrams, except that they flow in the opposite direction.

If our problem is to find Harry to dog, who disappeared suspiciously and might be the victim of kidnapping (or, rather, dognapping), we might structure our why map by considering what keeps him away and drill into further detail from there (see below).

A why map helps you identify the root causes of your problem.

how maps helps you identify your options

With a how map, you look for all the potential solutions to your problem, your options. As a general rule, you want to know the why before you get to the how, so if you don’t know the root cause(s) of your problem, find these first.

Question maps progress further into details until elements are sufficiently explicit. Then come the hypotheses, analyses and data sources (more on that in later posts). Once you have tested which solutions are viable, you are ready to select one, for instance by using a decision matrix.

A how map helps you identify potential options to solve your problem.

A decision tree breaks down a key question into MECE branches and then states explicitly the expected value of each branch—by multiplying the probability of success by the payoff associated with success. Although we usually don’t use a how map to spell out the expected value of each outcome, these maps are in some sense more complete that standard.

Why maps and how maps share some properties

Irrespective of their types, question maps are insightful: They break down the key question in an insightful way, adding value with each column.

Question maps are useful in various ways. First, they provide a graphical breakdown of the problem, a sort of roadmap. By including all possible answers to your key question in your map, you reduce the probability that you’ll overlook some. Second, they spell out the various alternatives in concrete details, so you don’t stay at a philosophical level. Finally, they are useful in managing your problem-solving effort—especially if you are leading a team—because they help you keep the big picture in mind, so that you can allocate resources judiciously.

Learn more about why maps and how maps

In any situation, don’t exhaust yourself finding the one perfect question map. There’s usually more than one suitable map, and no map is perfect. So, create the best map that you can, then move on. And update it periodically in light of new evidence.

Conducting a profitability analysis (i.e., looking at how revenues and costs fare up) is a common theme in business. If you’re looking for increasing your profitability, or if you’re facing an analogous problem, using the saturation framework that may help.

References

Chevallier, A. (2016). Strategic Thinking in Complex Problem Solving. Oxford, UK, Oxford University Press, pp. 45–75.

Davis, Ian, David Keeling, Paul Schreier and Ashley Williams. “The Mckinsey Approach to Problem Solving.” McKinsey Staff Paper, no. 66 (2007): 27.

De Kleer, Johan and Brian C Williams. “Diagnosing Multiple Faults.” Artificial intelligence 32, no. 1 (1987): 97-130.

Duncker, Karl and Lynne S Lees. “On Problem-Solving.” Psychological monographs 58, no. 5 (1945): i.

Fischhoff, Baruch, Paul Slovic and Sarah Lichtenstein. “Fault Trees: Sensitivity of Estimated Failure Probabilities to Problem Representation.” Journal of Experimental Psychology: Human Perception and Performance 4, no. 2 (1978): 330.

Kazancioglu, Emre, Ken Platts and Pete Caldwell. “Visualization and Visual Modelling for Strategic Analysis and Problem-Solving.” In Information Visualisation, 2005. Proceedings. Ninth International Conference on, 61-69: IEEE, 2005.

Platt, John R. “Strong Inference.” Science 146, no. 3642 (1964): 347-353.

Wojick, David. Issue Analysis – an Introduction to the Use of Issue Trees and the Nature of Complex Reasoning, 1975.

Image credit: Free-Photos/Pixabay.

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