Because the analogy and its use for innovative thinking is presented so well, I am going to go all recursive on you and use the article as a showcase of the right ways of using analogy for innovation and for communicating about innovation.

The first step in any analogy is pointing out to the audience the relevant correspondences between the concepts:

“Baseball teams have to assemble the best talent possible, just like companies have to bet on the best innovation opportunities. A baseball team chooses between acquiring talent on the free agent market or drafting and building talent. A company chooses between acquisitions or organic growth.“

A good analogy focuses on deep structural correspondences between concepts:

“Acquisitions are expensive, but perceived to be lower risk, because the talent (or idea) has proven itself demonstrably in the marketplace (for baseball, that means success on a major-league diamond). Organic growth is typically cheaper, but perceived to be risky because many times highly touted initiatives or prospects don’t pan out.“

What this means is that the author isn’t just drawing correspondences between the elements of the two concepts (e.g., “acquiring talent on the free agent market” = “acquisitions”). He is explaining how elements and the relationships between them in one concept correspond to elements and the relationships between them in the other concept.

These correspondences then lead to certain inferences. Inferences, that are important to the point the author is making about innovation:

“Just as a baseball team doesn’t have complete information about what a player’s true level of ability is on draft day, you don’t know the real potential of any one innovation project…. Good teams collect as much data as possible. They have sophisticated models to project how rough performance can project to the major league level.“

Leads to the inference that for companies:

“With a well-organized scouting team, you should gather multiple data points in preparation to “draft” innovation opportunities.”

By pointing out the correspondences between rich concepts such as the ones being used in the Baseball Analogy article, the audience is then able to make their own inferences using their own detailed knowledge of the concepts in the analogy:

“Of course, the market for companies is more liquid than the market for baseball players. We bet you if you ran the data the absolute best return on investment would be acquiring a hitter who has proven himself at a critical midpoint….Ask yourself: What is the equivalent inflection point in our market?“

It is by working out the inferences resulting from correspondences such as these that innovation is made possible. However, these inferences are not possible unless your audience possesses detailed knowledge of the concepts used in the analogy. I, for one, know next to nothing about the Major League Baseball draft. Consequently, I can follow the analogy made by the author and the points being made but would be absolutely unable to determine an “equivalent inflection point” at which I would be most likely to receive the “absolute best return on investment.”

Using analogy for communicating about innovation or for innovation itself requires knowledge of BOTH the concepts used in the analogy. This knowledge can be provided by the person making the analogy or through personal experience, but acquired it must be for innovation to happen.

So, the question is “Is Ratbert making another bad analogy?” I believe the answer to this question depends on the mapping between the source and target domain.

Quick refresher: “Source domain” refers to the domain/knowledge from which the information is being “imported” so to speak. In the case of the cartoon, the source domain would be knowledge about the aging of good wine.

“Target domain” refers to the domain/knowledge to which the information is imported or the analogy is being drawn. In the cartoon, knowledge of Ratbert is the target domain.

“Mapping” refers to the drawing of correspondences between the source and target domain. In the cartoon, this would involve establishing the ways in which Ratbert is like a fine wine and herein lies the determination of whether this is a good or a bad analogy.

Ratbert is making the correct inference for this particular analogy – that Dogbert is saying that initially Ratbert’s wisdom may be lacking but over time will improve with age.

However, in verifying this inference, Dogbert reveals that there is more to the analogy that meets the eye.

He is probably quite correct in making the statement that the analogy holds to the extent that Ratbert resembles a grape. The crux of the matter is exactly HOW Ratbert resembles the grape.

If the resemblance is confined to the fact that Ratbert is round, covered in a thin skin, and grape-colored, then the analogy falls apart. However, if it is because Ratbert shares some of the structural characteristics with grapes that result in fine wine (thick skin, late to ripen, possessing complex flavors that are only revealed with age), the analogy holds. In other words, analogies succeed, not because of physical similarities between the source and the target domain but because of structural similarities.

For Ratbert’s wisdom to age to perfection with time, the wine analogy would suggest that it would help for him to have a “thick skin” (the better to withstand the ravages of the environment), be “slow to ripen” (play, not take life too seriously, take each stage of life as it comes and gain a range of diverse experiences in the process) and be a complex individual with many facets to his personality and many interests.

The structural mapping between source and target domain is the critical component to the success of an analogy. When the mapping only involves mapping the surface features between domains, you have a useless or even misguided analogy at worst and a trivial analogy at best.

Insight, inferences, problem solutions,and effective communication are all made possible by the structured mapping of correspondences between the source and target domain.

The greater the depth and complexity of the mapping – the better the result. Not an easy task by any means but one well worth the effort.